text:
\section{Introduction}
\label{sec:intro}
We address the problem of semantic segmentation of unknown categories. Detecting novel objects, for example, in front of a self-driving vehicle is crucial for safety yet very challenging. The distribution of potential objects on the road has a long tail of unknowns such as wild animals, vehicle debris, litter, etc\onedot} \def\vs{vs\onedot, manifesting in small quantities on the existing datasets \cite{Yu2020CVPR, Caesar2020CVPR, Cordts2016CVPR}.
The diversity of unknowns in terms of appearance, size, and location adds to the difficulty.
In addition to the challenges of data, deep learning has evolved around the closed-set assumption.
Most existing models for category prediction owe their success to curated datasets with a fixed set of semantic categories.
These models fail in the open-set case with unknown objects by over-confidently assigning the labels of known classes to unknowns~\cite{Hein2019CVPR, Nguyen2015CVPR}.

\begin{figure}[t!]
    \centering
    \includegraphics[width=\linewidth]{gfx/qual_ra_teaser.pdf}
    \caption{\textbf{Preserving Objectness and Eliminating Noise.} This figure compares the result of our method, NLS, to state-of-the-art methods PEBAL~\cite{Tian2022ECCV} and DenseHybrid~\cite{Grcic2022ECCV}. Our method clearly segments the unknown objects and eliminates uncertainty at semantic boundaries and ambiguous background regions.}
    \vspace{-.1in}
    \label{fig:qual_teaser}
\end{figure}

The existing approaches to segmenting unknown objects can be divided into two depending on whether they use supervision for unknown objects or not. In either case, the model has access to known classes during training, i.e\onedot} \def\Ie{I.e\onedot inlier or in-distribution, and the goal is to identify the pixels belonging to an unknown class, i.e\onedot} \def\Ie{I.e\onedot anomalous, outlier, or out-of-distribution~(OoD). Earlier approaches resort to an ensemble of models~\cite{Lakshminarayanan2017NeurIPS} or Monte Carlo dropout~\cite{Gal2016ICML} which require multiple forward passes, therefore costly in practice. More recent approaches use the maximum class  probability~\cite{Hendrycks2017ICLR} predicted by the model as a measure of its confidence. However, this approach requires the probability predictions to be calibrated, which is not guaranteed~\cite{Szegedy2013ARXIV, Nguyen2015CVPR, Guo2017ICML, Minderer2021NeurIPS, Jiang2018NeurIPS}. In the supervised case, the model can utilize outlier data to learn a discriminative representation, however, outlier data is limited. Typically, another dataset from a different domain is used for this purpose~\cite{Chan2021ICCV}, or outlier objects are artificially added to driving images~\cite{Grcic2022ECCV, Tian2022ECCV}. Despite the better performance~\cite{Tian2022ECCV}, outlier supervision can bias the model to a particular distribution of outliers.

\begin{figure}[t!]
    \centering
    \includegraphics[width=\linewidth] {gfx/Mask2FormerArchitectureSmallV2Cropped.pdf}
    \vspace{-0.1in}
    \caption{\textbf{Overview.} This figure provides an overview of the training pipeline based on the Mask2Former architecture \cite{Cheng2022CVPR} and the inference of unknown objects. The model predicts region class probabilities $\mathbf{P}$ and membership scores $\mathbf{M}$. After matching regions to the ground truth via bipartite matching, we train the model with a semantics loss on $\mathbf{P}$ and a mask loss on $\mathbf{M}$.
    For unknown inference, we aggregate votes from object queries in logits $\mathbf{L}$ as a result of multiplying $\mathbf{P}$ and $\mathbf{M}$. We compute the anomaly score as the negative sum over the class dimension $K$. For $\clubsuit$/$\spadesuit$, see \secref{sec:one_vs_all}.}
    \vspace{-.1in}
    \label{fig:overview}
\end{figure}

The common approach to segmentation is per-pixel classification by making a decision for each pixel independently. For spatial smoothness or objectness, the models can rely on learning necessary inductive biases from data. However, unknown segmentation methods built on this paradigm fail to preserve the object boundaries or to uniformly assign a score to the whole object region as shown in \figref{fig:qual_teaser}.
Preserving objectness is important for the following steps such as incremental learning in open-world settings \cite{Cen2021ICCV}. In this work, we explore the potential of classifying regions instead of pixels to better learn the concept of objectness for unknown object segmentation. Region or mask classification has been initially proposed for instance segmentation \cite{He2017ICCV} and recently extended to include semantic segmentation~\cite{Cheng2021NeurIPS, Cheng2022CVPR}.

We explore the potential of query-based mask classification to discover unknown objects.
Specifically, we found that object queries in Mask2Former \cite{Cheng2022CVPR} behave like one vs. all region classifiers.
Object queries specialize in predicting the region of a particular class and unknown regions are ignored by all the queries. Each object query casts a vote representing its confidence that a specific pixel belongs to a particular region. By interpreting these votes as logits, we show that simple baselines such as max logit \cite{Hendrycks2022ICML} become considerably more reliable for detecting unknowns and even achieve state-of-the-art performance on some datasets.

The accuracy and false positive rate are commonly reported together in the literature because a good accuracy at detecting unknowns is typically achieved at the cost of a high false positive rate. Furthermore, different sources of uncertainty make predicting unknowns difficult, especially at the boundaries separating inliers or in ambiguous background regions. To reduce false positives, we examine the behavior of logits at each pixel and propose a novel anomaly scoring function called Negative Logit Sum (NLS). With mask classification and NLS, our method without supervision outperforms the existing methods that use outlier supervision on challenging datasets with high distribution shifts such as Road Anomaly~\cite{Lis2019ICCV} and SMIYC benchmark \cite{Chan2021ARXIV}. NLS does not require retraining, outlier supervision, or an external network. We show that regularizing NLS using outlier supervision further improves the performance without affecting the closed-set performance.
\section{Related Work}
\label{sec:related}
\boldparagraph{Semantic Segmentation Paradigms} Since the appearance and success of Fully Convolutional Networks (FCN)~\cite{Shelhamer2015CVPR}, semantic segmentation architectures have revolved around the per-pixel classification paradigm.
This paradigm has been extensively studied to increase the closed-set performance with various convolution and pooling operations~\cite{Chen2018PAMI, Chen2018ECCV, Dai2017ICCV, Zhao2017CVPR, Yang2018CVPR}, and to aggregate multi-scale contextual information~\cite{Yu2016ICLR, Yuan2020ECCV}. Recent work shifted towards transformer-based architectures~\cite{Xie2021NeurIPS, Strudel2021ICCV, Yuan2021ARXIV, Zheng2021CVPR, Yang2021NeurIPS} and attention mechanisms~\cite{Harley2017ICCV, Li2018BMVC, Zhao2018ECCV, He2019ICCV, Li2019ICCV, Huang2019ICCV, Fu2019CVPR}.

On the other hand, mask classification has been mainly adopted by instance segmentation and object detection models~\cite{He2017ICCV, Hariharan2014ECCV, Carion2020ECCV} since it allows pixels to belong to multiple proposals and provides the flexibility to detect a variable number of objects in the scene.
Max-DeepLab~\cite{Wang2021CVPR} employs mask classification for panoptic segmentation but with many auxiliary losses. Although some earlier efforts have been made to apply mask classification to semantic segmentation~\cite{Carreira2012ECCV, Hariharan2014ECCV}, they were quickly outperformed by the per-pixel methods until recently. MaskFormer variants~\cite{Cheng2021NeurIPS, Cheng2022CVPR} apply query-based mask classification and attention to obtain a unified segmentation model which shows competitive performance with specialized semantic and instance segmentation architectures across benchmarks  \cite{Lin2014ECCV, Cordts2016CVPR, Zhou2017CVPR, Neuhold2017CVPR}.

\begin{figure*}[t!]
    \centering
    \includegraphics[width=\linewidth] {gfx/object_query_votes_heatmap.pdf}
    \label{fig:oq_vote}
    \vspace{-0.2in}
    \caption{\textbf{Specialization of Object Queries.} Certain object queries specialize in predicting a specific class. For each query, we show how many times it predicts a region to belong to a class with high confidence. The sparsity in the plot clearly shows the specialization of queries.}
    \label{fig:one_vs_all}
\end{figure*}

\begin{figure*}[t!]
    \centering
    \includegraphics[width=0.9\linewidth] {gfx/masking_hmap_v2.pdf}
    \caption{\textbf{Performance when Masking Object Queries.} We show the impact on per-class IoU on Cityscapes \cite{Cordts2016CVPR} when using two types of masking: hard masking without any interactions between the queries (top) and soft masking by allowing interactions in the transformer decoder (middle) compared to the original model without masking (bottom). Without interacting with one another, specialized object queries can sufficiently segment their particular classes.}
    \label{fig:masking_queries}
\end{figure*}

\boldparagraph{Unsupervised Anomaly Segmentation}
Unsupervised methods utilize their knowledge about inlier data to detect anomalies at inference time. Early work measures uncertainty based on the observation that anomaly samples typically result in low-confidence predictions. The uncertainty of a model can be estimated through maximum softmax probabilities~\cite{Hendrycks2017ICLR, Liang2018ICLR}, ensembles \cite{Lakshminarayanan2017NeurIPS}, Bayesian approximation~\cite{Mukhoti2018ARXIV}, Monte Carlo dropout~\cite{Gal2016ICML}, or by learning to estimate its confidence~\cite{Devries2018ARXIV}. However, posterior probabilities of a closed-set model are not necessarily calibrated, leading to overconfident predictions on unseen categories~\cite{Szegedy2013ARXIV, Nguyen2015CVPR, Guo2017ICML, Minderer2021NeurIPS, Jiang2018NeurIPS}. Therefore, follow-up work focuses on making a clear distinction between inliers and outliers by using true class probabilities~\cite{Corbiere2019NeurIPS}, unnormalized logits instead of softmax probabilities~\cite{Hendrycks2022ICML}, standardized class-wise logits~\cite{Jung2021ICCV}, and the distance to learned prototypes of known classes~\cite{Cen2021ICCV}. Overall, unsupervised approaches are typically efficient without any extra training but they are inherently limited to which extent they can separate inliers and outliers due to a lack of supervision with outlier data.

Deep generative models are also used for unsupervised anomaly segmentation. Early methods are primarily based on density estimation~\cite{Lee2018NeurIPS, Ren2019NeurIPS} while subsequent works focus on reconstruction.
Several works rely on the predicted segmentation maps to resynthesize~\cite{Lis2019ICCV, Xia2020ECCV, Haldimann2019ARXIV, Vojir2021ICCV} or inpaint the inputs~\cite{Lis2020ARXIV} and measure discrepancy with comparison networks. Others apply localized adversarial attacks~\cite{Besnier2021ICCV}, synthesize negatives using normalizing flows~\cite{Grcic2021ARXIV}, or combine Gaussian mixture models with discriminative representation learning~\cite{Liang2022ARXIV}.
Generative methods are typically impractical for real-time safety-critical applications due to high computational costs and long inference times. Additional comparison modules and the change in input distributions require extra training.
Moreover, synthesized unknowns often do not generalize well to real anomalies~\cite{Grcic2021ARXIV}. Several works~\cite{Nalisnick2018ICLR, Serr2020ARXIV, Zhang2021ICML} show that generative models tend to estimate high likelihoods on out-of-distribution samples.

\boldparagraph{Anomaly Segmentation with Outlier Supervision}
Out-of-distribution data can be used to regularize the model's feature space by learning a representation of unknowns.
With the increase in the availability of wide-range datasets, initial approaches utilize generic datasets such as ImageNet \cite{Russakovsky2015IJCV} and 80 Million Tiny Images \cite{Torralba2008PAMI} for OoD. Given data, OoD detection can be simply treated as binary classification~\cite{Bevandic2018ARXIV, Bevandic2019GCPR}. Outlier data can also be used to estimate the distributional uncertainty of OoD samples~\cite{Malinin2018NeurIPS} or to fine-tune parametrized OoD detectors~\cite{Hendrycks2019ICLR}.
The energy score has been proposed as a better alternative to softmax in terms of separation~\cite{Liu2020NeurIPS}. SynBoost~\cite{Biase2021CVPR} is a supervised image resynthesis method that treats void regions as anomalies to obtain an uncertainty signal. Recent work uses a subset of COCO~\cite{Lin2014ECCV} or ADE20K~\cite{Zhou2017CVPR}, either as entire images~\cite{Chan2021ICCV} or after cut-and-paste into the inlier scenes~\cite{Tian2022ECCV, Grcic2022ECCV}. Meta-OoD~\cite{Chan2021ICCV} maximizes the entropy on outliers, whereas PEBAL~\cite{Tian2022ECCV} learns adaptive energy-based penalties by abstention learning. Combining likelihood and posterior evaluation, DenseHybrid~\cite{Grcic2022ECCV} achieves state-of-the-art results. However, different models are fine-tuned for each benchmark by using multiple datasets with high distribution shifts, resulting in a higher degree of supervision and variety. Our model, on the other hand, can achieve better performance by only using COCO~\cite{Lin2014ECCV} for fine-tuning.

\section{Methodology}
\label{sec:method}
In open-set segmentation, the goal is to segment unknown objects without compromising closed-set performance. We approach this challenge by exploring mask classification and uncovering its innate ability to detect semantically unknown regions. Based on our analysis, we propose a novel anomaly score function enabled by the unique properties of mask classification. We further show that minimal fine-tuning using outliers improves unknown detection without affecting closed-set performance.

\subsection{Mask Classification}
We build our method on top of the Mask2Former architecture \cite{Cheng2022CVPR}, which is an improved version of the initial MaskFormer~\cite{Cheng2021NeurIPS}.
We give only a brief overview to make the discussion self-contained; please refer to Cheng et~al\onedot} \def\iid{i.i.d\onedot \cite{Cheng2022CVPR} for details.
Mask2Former consists of three main parts: the backbone, the pixel decoder, and the transformer decoder. The backbone processes the input image to extract features at multiple scales. Then, the pixel decoder further processes the multi-scale features to produce high-resolution per-pixel features $\mathbf{F} \in \mathbb{R}^{C_{p} \times H \times W}$.
The transformer decoder takes the resulting multi-scale features $\{\mathbf{f}_i\}_{i=1}^{D}$ as well as $N$ learnable object queries $\mathbf{Q} \in \mathbb{R}^{N \times C_{q}}$, where $C_{p}$ and $C_{q}$ denote the embedding dimensions.
At each layer of the transformer decoder, object queries are refined by interacting with each other and with one of the scales $\mathbf{f}_i$ in a round-robin order.

The refined object queries are first processed with a 3-layer MLP, resulting in $\mathbf{Q}_{p} \in \mathbb{R}^{N \times C_{p}}$ to predict $N$ regions. The binary masks for all regions are obtained by multiplying $\mathbf{Q}_{p}$ with pixel features $\mathbf{F}$ and applying a sigmoid $\sigma$ to the result:
\begin{equation}
    \mathbf{M} = \sigma(\mathbf{Q}_p~\mathbf{F})
\end{equation}
$\mathbf{M} \in \mathbb{R}^{N \times H \times W}$ represents the membership score of each pixel belonging to a region.
In parallel, refined object queries are fed to a linear layer followed by softmax to produce posterior class probabilities $\mathbf{P} \in [0, 1]^{N \times K}$ of $K$ classes.

In contrast to per-pixel semantic segmentation, the ground truth masks are partitioned into multiple binary masks such that each mask contains all the pixels that belong to a class. Then, bipartite matching is used to match every ground truth mask to an object query in a way to minimize the losses. For region classification, the cross-entropy loss is applied to the matched object query class predictions. For region prediction, a weighted combination of dice loss~\cite{Milletari2016THREEDV} and binary cross entropy loss is applied to the binary mask predictions. The training steps are highlighted with a red box in \figref{fig:overview}.
In inference, the class scores or logits $\mathbf{L} \in \mathbb{R}^{K \times H \times W}$ are calculated as the multiplication of mask predictions with class predictions by broadcasting the class prediction to all the pixels within the region:
\begin{equation}
    \mathbf{L} = \sum_{n=1}^{N} \mathbf{P}_n \mathbf{M}_n
    \label{eq:vote}
\end{equation}

\subsection{One vs. All Behavior of Object Queries}
\label{sec:one_vs_all}
The logit term $\mathbf{L}$ as defined in \eqnref{eq:vote} has a deeper interpretation because of its structure. In essence, $\mathbf{L}$ aggregates weighted votes over all object queries to decide whether the pixel belongs to a certain class. There is an important difference between $\mathbf{L}$ and logits in standard per-pixel segmentation. In the standard setting, the logit that belongs to the true class is directly optimized to be higher than the logits of other classes.
In the case of $\mathbf{L}$ in \eqnref{eq:vote}, regions $\mathbf{M}$ predicted by the object queries are not constrained to be mutually exclusive or exhaustively cover the entire image. This allows a pixel to be assigned to more than one region or possibly to none. %
Independent region predictions lead to independent votes from object queries about the per-class membership of a pixel.
This allows object queries to behave implicitly as one vs. all classifiers, where each object query predicts a certain class, leaving ignored regions to be interpreted as unknowns.

We empirically demonstrate this behavior on the BDD100K validation set~\cite{Yu2020CVPR}. We first identify which class each object query specializes in by counting how many times it predicts a certain class with high confidence, e.g\onedot} \def\Eg{E.g\onedot greater than $98\%$. As shown in \figref{fig:one_vs_all}, some object queries consistently focus on a single class. After identifying specialized object queries, we mask out other object queries and use only those to test our hypothesis on one vs. all behavior.
We apply two types of masking; before the transformer decoder ($\clubsuit$ in \figref{fig:overview}), which enforces hard masking, or after the transformer decoder ($\spadesuit$ in \figref{fig:overview}), which allows them to still interact in the transformer, leading to soft masking. \figref{fig:masking_queries} shows per-class IoU using both strategies compared to the original model without masking. Due to the independence of object queries, the performance remains the same for frequent classes but drops significantly for rare classes, such as train, truck, or bus. The drop in performance indicates that when a class cannot be learned properly, e.g\onedot} \def\Eg{E.g\onedot due to data scarcity, the object query relies on interactions in the transformer decoder, which explains the slightly better performance of soft masking than hard masking.

\begin{figure*}[t!]
    \centering
    \includegraphics[width=\linewidth]{gfx/logits_v2.pdf}
    \caption{\textbf{Categorizing the Behavior of Logits.} Outlier pixels receive extremely low votes from object queries \textcolor{red}{(a)}. Inlier pixels receive a high vote from a single object query \textcolor{red}{(b)}. Boundary pixels separating two inlier classes receive moderate votes from both object queries \textcolor{red}{(c)}. Ambiguous regions receive weak votes from multiple object queries \textcolor{red}{(d)}. Clustering clearly outlines these four behaviors of logits \textcolor{red}{(e)}.}
    \label{fig:logits}
    \vspace{-.1in}
\end{figure*}

\subsection{Negative Logit Sum (NLS)}
The term $\mathbf{L}$ in \eqnref{eq:vote} aggregates the independent decisions of object queries about whether a pixel belongs to a certain class. Based on this behavior, we can identify several distinct modes of $\mathbf{L}$. We cluster the logits over classes at each pixel, i.e\onedot} \def\Ie{I.e\onedot $K$-dimensional vector, using k-means to characterize the modes, visualized in \figref{fig:logits}\textcolor{red}{e}. For an inlier pixel, only a single object query will vote for it with high confidence (\figref{fig:logits}\textcolor{red}{b}), whereas true outlier pixels do not receive any votes from any object query (\figref{fig:logits}\textcolor{red}{a}). Simply using max logit as an anomaly score already achieves great performance in detecting unknowns (\figref{fig:qual_ml_vs_nls} and \tabref{tab:ra_fs_val}), since the maximum will be high for inliers and low for outliers.
However, there are pixels that disrupt the separability between the inliers and the outliers. For example, pixels on a boundary between two inlier classes (\figref{fig:logits}\textcolor{red}{c}) or ambiguous background pixels (\figref{fig:logits}\textcolor{red}{d}) end up with a max logit lower than the inliers, causing them to be mistaken as an outlier. To mitigate this, we propose to use the negative sum of the logits as an anomaly score instead of max logits:
\begin{equation}
    \mathbf{NLS} = -\sum_{k=1}^{K}{\mathbf{L}_k}
    \label{eq:nls}
\end{equation}
where $\mathbf{L}_k$ is the logit of class $k$. Boundary and ambiguous regions are commonly characterized by having more than one weak vote from object queries. By summing these weak votes, NLS can assign a lower anomaly score than the max logit to these regions. This simple function brings a considerable decrease in false positive rate, especially for small OOD objects (\tabref{tab:ra_fs_val}). \figref{fig:qual_ml_vs_nls} highlights the differences between the anomaly maps predicted by max logit and NLS.

\subsection{Outlier Supervision}
\label{sec:outlier_supervision}
Given minimal outlier supervision, NLS can be directly regularized leading to significant improvements in segmenting unknowns without affecting closed-set performance. Without the need to retrain the entire model, we only fine-tune the MLP and the linear layers after the transformer decoder (see Supplementary), which constitutes only $0.21\%$ of the total model parameters. We use Anomaly Mix as proposed in \cite{Tian2022ECCV}, where objects from COCO dataset~\cite{Lin2014ECCV} are cut and pasted on Cityscapes images~\cite{Cordts2016CVPR}. Regularization is achieved by encouraging NLS to be higher than some threshold $\alpha_{out}$ for outliers and below some threshold $\alpha_{in}$ for inliers. The additional outlier loss function $\mathcal{L}_{nls}$ is defined as the average of the two following loss functions:
\begin{eqnarray}
    \label{eq:in_out_loss_func}
    \mathcal{L}_{in} &=& \sum_{\mathbf{p} \in \Omega_{in}} \left(\max(0, \text{NLS}(\mathbf{p}) - \alpha_{in})\right)^2 \\
    \mathcal{L}_{out} &=& \sum_{\mathbf{p} \in \Omega_{out}} \left(\max(0, \alpha_{out} - \text{NLS}(\mathbf{p}))\right)^2 \nonumber%
\end{eqnarray}
where $\Omega_{in}$ and $\Omega_{out}$ are the set of inlier and outlier pixels, respectively. The hyper-parameters $\alpha_{in}$ and $\alpha_{out}$ can be chosen without tuning by considering the properties of $\mathbf{L}$. The logits depicted in \figref{fig:logits}\textcolor{red}{a} and \figref{fig:logits}\textcolor{red}{b} correspond to the ideal form that inlier and outlier logits should take, respectively. For inlier pixels, it is sufficient to be chosen by a single object query, therefore $\alpha_{in} = -1$ suffices, and the same logic applies to $\alpha_{out}$ for being close to zero. %

\begin{figure*}[t!]
    \centering
    \includegraphics[width=\linewidth]{gfx/qual_test.pdf}
    \caption{\textbf{Max Logits vs NLS}. While max logit \cite{Hendrycks2022ICML} performs well in segmenting unknowns, the proposed NLS eliminates falsely high anomaly scores at the boundaries separating inliers (marked in ellipses), thereby reducing the false positive rate considerably (see \tabref{tab:ra_fs_val}).}
    \label{fig:qual_ml_vs_nls}
\end{figure*}

\section{Experiments}
\label{sec:experiments}
\input{tab/smiyc_merged.tex}

\subsection{Datasets}
We train the model on the Cityscapes dataset~\cite{Cordts2016CVPR}, which consists of 2975 training and 500 validation images. It contains 19 classes which are considered as inliers in anomaly segmentation benchmarks. The classes in the dataset can be seen in \figref{fig:one_vs_all}. For evaluation, we consider multiple datasets. First, Segment Me If You Can~(SMIYC) Benchmark \cite{Chan2021ARXIV} contains two datasets: anomaly track and obstacle track. The anomaly track has 100 images that contain unknown objects of various sizes in diverse environments. The obstacle track contains 412 images with typically small unknown objects on the road, 85 of which are taken at night and in adverse weather conditions. Both datasets are characterized by a high domain shift compared to Cityscapes, making them particularly challenging.
Road Anomaly~\cite{Lis2019ICCV} is an earlier and smaller version of SMIYC. It consists of 60 images with diverse objects in diverse environments.
We report results on the Fishyscapes Lost\&Found dataset~\cite{Blum2021IJCV}, which has 100 validation and 275 test images.
The domain of this dataset is similar to that of Cityscapes, and the anomalous objects are mostly small and less diverse compared to other datasets.

\subsection{Experimental Setup}
\boldparagraph{Implementation Details}
We follow the setup of \cite{Cheng2022CVPR} for closed-set training on Cityscapes. We use the Swin-B~\cite{Liu2021ICCV} architecture as the backbone. Differently, we use only one decoder layer in the transformer decoder instead of nine (see \secref{sec:ablations}).
For outlier supervision, we fine-tune the MLP and the linear layers for 5K iterations with a batch size of 16 using the standard loss functions used in \cite{Cheng2022CVPR} in addition to the losses defined in \eqnref{eq:in_out_loss_func}.
The previous work~\cite{Tian2022ECCV} samples 300 new images every epoch out of 40K COCO images with objects that do not intersect with Cityscapes inliers. In our case, we sample only 300 images at the beginning and fix them, then an image is randomly chosen and pasted on the Cityscapes images with probability $p_{out}$. We experimentally set $p_{out}$ to $0.1$.

\boldparagraph{Evaluation Metrics}
For comparison to previous methods on the Road Anomaly and the Fishyscapes, we report Average Precision~(AP), Area under ROC Curve~(AuROC), and False Positive rate at the threshold of $95\%$ True Positive Rate~(FPR@95). On SMIYC, the public benchmark reports AP and FPR@95 for per-pixel metrics as well as component-level metrics that are designed to measure the statistics of detected objects \cite{Chan2021ARXIV}. Specifically, the proposed metrics aim at quantifying true positives (TP), false negatives (FN), and false positives (FP) of detected unknown objects. Please see the benchmark paper~\cite{Chan2021ARXIV} for more details on these metrics.

\subsection{Quantitative Results}
\subsubsection{Segment Me If You Can Benchmark}
\tabref{tab:smiyc_merged} shows the results on anomaly and obstacle tracks of the public SMIYC benchmark. Without outlier supervision, NLS outperforms all the models, including those trained with outlier supervision, in AP while maintaining a competitive FPR@95. In terms of component metrics, the gains with NLS are more pronounced, which is due to an improved ability to characterize objectness, compared to the previous work. With outlier supervision, the performance gap improves with respect to the previously best method %
consistently across both tracks: +5.8\%  and +4.6\% in AP and +1.5\% and +6.7\% in mean F1 for anomaly and obstacle tracks respectively.

DenseHybrid~\cite{Grcic2022ECCV} achieves a slightly better FPR@95 on the anomaly and obstacle tracks but NLS achieves +13.3\% and +4.6\% better AP respectively, and better performance in all component-level metrics. ObsNet \cite{Besnier2021ICCV} achieves impressive performance at the component-level, however, not at the pixel-level. NLS consistently performs well across both tracks in both pixel and component-level metrics.

SMIYC is characterized by high domain shift and diversity of objects in terms of size and appearance, making it particularly challenging. While some methods, like DenseHybrid~\cite{Grcic2022ECCV}, rely on more diverse data when fine-tuning, NLS with mask classification shows that no outlier supervision is necessary for performing well under domain shift, thereby surpassing the limitations of the existing methods.

\input{tab/ra_fs_val.tex}

\subsubsection{Road Anomaly \& Fishyscapes LaF}
\tabref{tab:ra_fs_val} shows the results on the Road Anomaly~\cite{Lis2019ICCV} and the Fishyscapes Lost and Found (LaF) validation set~\cite{Blum2021IJCV}. In addition to NLS, we report the performance of max logit \cite{Hendrycks2022ICML} based on logits as computed in \eqnref{eq:vote}. Max logit with mask classification outperforms all the existing methods owing to the mostly correct behavior of logits (see \figref{fig:logits}). Using NLS results in a considerable improvement in FPR@95 on both datasets; by 4.22\% on Road Anomaly and 8.69\% on Fishyscapes LaF. This shows that NLS can better utilize the potential of mask classification. With outlier supervision, the results are further improved in all metrics.
GMMSeg \cite{Liang2022ARXIV} and PEBAL \cite{Tian2022ECCV} perform better in FPR@95 on Fishyscapes LaF but fall behind in AP with 10\% and 5\% gap, respectively. Furthermore, their success does not map to Road Anomaly.

\tabref{tab:ra_fs_val} also shows the in-distribution performance on the validation set of Cityscapes. Fine-tuning NLS with outlier supervision does not hurt the closed-set performance, it rather improves it slightly. This can be attributed to the fact that detecting unknowns using mask classification is highly coupled with detecting known classes; when known classes are detected better, it becomes easier to find unknown regions ignored by known classes. Therefore, fine-tuning can improve unknown detection by enhancing closed-set performance.
\subsection{Ablation Study}
\label{sec:ablations}
\input{tab/per_pixel_abl.tex}
\begin{figure*}[t!]
    \centering
    \begin{subfigure}{.3\linewidth}
        \includegraphics[width=\linewidth]{gfx/dec_layers_metrics.pdf}
        \caption{Number of Decoder Layers}
        \label{fig:dec_layer_metrics}
    \end{subfigure}
    \hfill
    \begin{subfigure}{.3\linewidth}
    \centering
        \includegraphics[width=\linewidth]{gfx/dec_layers_val_loss_v2.pdf}
        \caption{Behavior of Different Losses}
        \label{fig:dec_layer_val_loss}
    \end{subfigure}
    \hfill
    \begin{subfigure}{.3\linewidth}
    \includegraphics[width=\linewidth] {gfx/object_queries_ablation.pdf}
    \caption{Number of Object Queries}
    \label{fig:object_queries_ablation}
    \end{subfigure}
    \caption{\textbf{Ablation Study on Architectural Choices.}
    With more decoder layers, in-distribution performance on Cityscapes improves but the outlier performance drops in terms of AP and FPR@95 on Road Anomaly \subref{fig:dec_layer_metrics}.
    Good performance is mainly due to better mask prediction at the cost of a higher semantics loss \subref{fig:dec_layer_val_loss}. The drop in mIoU with hard masking (\textbf{HM mIoU}) is another indicator of semantic information loss in the object queries with more decoder layers. The performance improves as the number of object queries increases \subref{fig:object_queries_ablation}.}
\end{figure*}
\input{tab/ablation_merged.tex}

We validate the effectiveness of mask classification with Mask2Former~\cite{Cheng2022CVPR} and the proposed scoring function NLS on Road Anomaly in comparison to a couple of per-pixel semantic segmentation methods with max logit~\cite{Hendrycks2022ICML} in \tabref{tab:per_pixel_abl}. We choose PSA~\cite{Liu2021ARXIV} due to its superior closed-set performance on Cityscapes validation set, and Segmenter~\cite{Strudel2021ICCV} due to learnable object queries but without mask classification. Despite the impressive in-distribution performance of PSA, it performs poorly in outlier detection. Segmenter performs worse in terms of closed-set performance but better in terms of detecting outliers, proving the effectiveness of learnable object queries by specializing to each class. However, it cannot reach the performance of the Mask2Former with mask classification. NLS further improves the performance, especially by reducing the false positives compared to max logit and achieves the best performance in all metrics.

\boldparagraph{Number of Decoder Layers} Although more decoder layers improve the inlier performance, i.e\onedot} \def\Ie{I.e\onedot mIoU on Cityscapes, we found that using fewer decoder layers results in better performance in case of outliers. \figref{fig:dec_layer_metrics} highlights the decrease in performance in terms of the AP and FPR@95 on the Road Anomaly dataset as the number of decoder layers increases. We investigate this behavior by isolating the sources of error with respect to the number of decoder layers. \figref{fig:dec_layer_val_loss} shows semantic and mask losses of the Mask2Former~\cite{Cheng2022CVPR} averaged over the validation samples on Cityscapes. With more decoder layers, we observe that the semantic cross-entropy loss increases while the mask-related BCE and dice loss decrease. This shows that the increase in inlier mIoU with more decoder layers can be attributed to a better ability in detecting masks at the cost of a higher semantic error.

\figref{fig:dec_layer_val_loss} also shows the mIoU evaluated by applying hard masking (see \figref{fig:masking_queries}) on the specialized object queries. Specialized object queries perform worse with more decoder layers. The information loss in the object queries as well as the degradation in the semantic loss show the importance of semantics for outlier detection compared to precise masks.

\boldparagraph{Number of Object Queries} The original Mask2Former \cite{Cheng2022CVPR} uses 100 object queries. We train different models by varying the number of object queries to observe its effect on detecting outliers. We focus on the AP values on both Road Anomaly~\cite{Lis2019ICCV} with large objects and on Fishyscapes LaF~\cite{Blum2021IJCV} with small objects. \figref{fig:object_queries_ablation} shows that more object queries result in better AP on both datasets. Even if some object queries specialize in predicting a particular class, the other object queries still play a role.

\boldparagraph{Outlier Data Exposure}  To show the effect of the number of fine-tuning iterations, we show results in different steps averaged over five different runs on Road Anomaly~\cite{Lis2019ICCV} in \tabref{tab:mlp_abl_iter}. %
The results improve with more iterations while maintaining a low standard deviation. We also show the in-distribution performance, mIoU on the Cityscapes validation set. %
Impressively, more fine-tuning with outlier data slightly improves in-distribution performance. To check the effect of the frequency of outlier data exposed in fine-tuning, we experiment with different values of $p_{out}$ as shown in \tabref{tab:mlp_p_out_ablation}. We choose $p_{out} = 0.1$ as it strikes a reasonable balance between performance and the number of outliers needed to improve the performance.

\boldparagraph{Outlier Bounds Optimization} As discussed in \secref{sec:outlier_supervision}, the structure of the logits eliminates the need to tune $\alpha_{in}$ and $\alpha_{out}$ for outlier supervision. We support that by running fine-tuning experiments with different values of $\alpha_{out}$, and report the results averaged over five different runs on Road Anomaly dataset~\cite{Lis2019ICCV} to account for the variance caused by sampling different outlier data. As shown in \tabref{tab:mlp_alpha_out_ablation}, the AP and FPR@95 values do not show a general trend in terms of the mean. We use $\alpha_{out} = -0.1$ based on its slightly better performance on average.
\section{Conclusion and Future Work}
\label{sec:conclusion}
In this work, we explore the potential of mask classification to segment unknown objects. We analyze its properties and demonstrate its inherent ability to express uncertainty by operating as an implicit one vs. all classifier. This allows the existing methods, such as max logit \cite{Hendrycks2022ICML}, to perform considerably well. Upon analyzing the behavior of logits, we propose a novel anomaly score function that eliminates irrelevant sources of uncertainty such as inlier boundaries and ambiguous backgrounds, leading to a considerable decrease in false positive rates. We further demonstrate that with minimal fine-tuning in terms of memory and computation, the proposed anomaly score can achieve additional gains without affecting the in-distribution performance.

As this work represents an initial attempt to utilize mask classification for unknown segmentation, its properties can be further explored with potential improvements. Given the increased ability to preserve objectness, open-world incremental learning is one step closer as unknown masks are more reliable as a source of supervision. While current efforts are limited to static image datasets, temporal or depth information can provide important cues to detect unknowns.

\boldparagraph{Acknowledgements}
We thank A. Kaan Akan, Ali Safaya, G\"orkay Aydemir, and Shadi Hamdan for their valuable feedback. This project is funded by the Royal Society Newton Fund Advanced Fellowship (NAF\textbackslash R\textbackslash 2202237). Nazir Nayal and M{\i}sra Yavuz are supported by the KUIS AI fellowship.

\section{Implementation Details}
\label{sec:imp_details}
\subsection{Architecture}
\figref{fig:full_mask2former} illustrates the full Mask2Former architecture along with our unknown inference procedure. The main components of the model are the backbone, the pixel decoder, and the transformer decoder. We explain the details of each next.

\boldparagraph{Backbone} We use the Swin-B variant as the backbone~\cite{Liu2021ICCV}. It can take an RGB image with any resolution higher than $32 \times 32$ as input and outputs feature maps at several resolutions to the pixel decoder. Specifically, the output feature maps are downsampled with strides 4~($\mathbf{x}_4$), 8~($\mathbf{x}_8$), 16~($\mathbf{x}_{16}$), and 32~($\mathbf{x}_{32}$) with respect to the input image.

\boldparagraph{Pixel Decoder} Following~\cite{Cheng2022CVPR}, the pixel decoder mainly consists of 6 layers of deformable attention (MSDeformAttn)~\cite{Zhu2021ICLR}. The multi-scale feature maps with strides $\mathbf{x}_8$ $\mathbf{x}_{16}$, and $\mathbf{x}_{32}$ are processed with MSDeformAttn layers to produce $\mathbf{f}_1$, $\mathbf{f}_2$, and $\mathbf{f}_3$, respectively. In \cite{Cheng2022CVPR}, the three processed feature maps are passed to 9 transformer decoder layers in a round-robin fashion. However, we found that using a single layer in the transformer decoder works better for unknown detection. Therefore, we only pass the last layer $\mathbf{f}_{3}$ to the transformer decoder.
The feature map $\mathbf{x}_{4}$ is processed with a $1 \times 1$ filter-size convolutional layer and then added to the processed feature map $\mathbf{f}_1$ after bilinear upsampling. Finally, the output is passed to a $3 \times 3$ convolutional layer to produce per-pixel features $\mathbf{F} \in \mathbb{R}^{C_p \times H \times W}$, where $C_p = 256$ is the embedding dimension. The computation can be summarized as follows:
\begin{equation}
    \mathbf{F} = \text{Conv}_{3\times3}\left(\text{Conv}_{1\times1}(\mathbf{x}_{4}) + \text{Upsample}(\mathbf{f}_{1})\right)
\end{equation}

\begin{figure*}[t!]
\centering
    \includegraphics[width=\linewidth] {gfx/Mask2FormerArchitecture.pdf}
    \vspace{-0.1in}
    \caption{\textbf{Detailed Architecture.} This figure provides a more detailed view of the Mask2Former \cite{Cheng2022CVPR} architecture, including our modifications and unknown inference computation. We use a single transformer decoder layer as opposed to the original implementation that uses 9 layers. Therefore, only a single scale feature $\mathbf{f}_3$ from the last layer is passed from the pixel decoder to the transformer decoder. For outlier supervision, all modules are frozen except for the MLP and the linear layers shown in pink.}
    \vspace{-.1in}
    \label{fig:full_mask2former}
\end{figure*}

\boldparagraph{Transformer Decoder} Learnable object queries $\mathbf{Q} \in \mathbb{R}^{N \times C_q}$ are fed to the transformer decoder layer to be processed with the feature maps from the pixel decoder, where $N = 100$ is the number of object queries and $C_q = 256$ is the embedding dimension. A single transformer decoder layer mainly consists of a cross-attention layer followed by self-attention and feed-forward network (FFN), each of which is followed by a LayerNorm. The cross-attention operation is performed with mask-attention where each object query only attends to regions it predicted in the previous layer. Since we use only a single layer, each object query attends to the region it predicts directly from the input feature map before being processed in the transformer decoder.
Learnable positional embeddings are added to the object queries. %
The transformer decoder outputs a refined set of object queries $\mathbf{Q}_r$ that predict the regions and classify them.

\boldparagraph{Region Class Prediction} The refined object queries $\mathbf{Q}_r$ are fed into a single linear layer followed by a softmax to produce the class probability of each region $\mathbf{P} \in \mathbb{R}^{N \times K}$ where $K$ is the number of classes.

\boldparagraph{Membership Maps Prediction} The refined object queries $\mathbf{Q}_r$ are also fed into a 3-layer MLP, so that $\mathbf{Q}_r$'s dimensionality matches that of $\mathbf{F}$. Then, $\mathbf{Q}_r$ and $\mathbf{F}$ are multiplied before being fed into a sigmoid activation to produce the per-pixel membership maps $\mathbf{M} \in \mathbb{R}^{N \times H \times W}$.

\subsection{Closed-Set Training}
\boldparagraph{Loss Functions} Before applying any loss function, bipartite matching is used to match object queries to ground truth binary masks, where each mask contains all the pixels of a certain class. The matching cost is computed as a weighted sum of the individual losses. The classification is performed with the cross-entropy loss. A weighted combination of dice loss and binary cross-entropy is used to predict regions.

\boldparagraph{Hyper-Parameters} Following \cite{Cheng2022CVPR}, the model is trained for 90K iterations using a batch size of 16. AdamW~\cite{Loshchilov2019ICLR} optimizer is used with $0.05$ for weight decay and an initial learning rate of $10^{-4}$, which is reduced using a polynomial scheduler. The learning rate for the backbone is multiplied by $0.1$.

\boldparagraph{Data Augmentation} We use the same augmentations as in \cite{Cheng2022CVPR}. First, the short side of the input image is resized by a scale uniformly chosen between $[0.5 - 2]$. Then a random crop of size $512 \times 1024$ is applied. After that, large-scale jittering augmentation \cite{Du2021ARXIV, Ghiasi2021CVPR} is applied with a random horizontal flip.

\subsection{Outlier Supervision}
\boldparagraph{Data Sampling} We use AnomalyMix proposed in~\cite{Tian2022ECCV} for outlier supervision. After eliminating the samples that contain Cityscapes classes~\cite{Cordts2016CVPR}, around 40K images remain for outlier supervision on the COCO~\cite{Lin2014ECCV} dataset. For a single fine-tuning experiment, we randomly sample 300 images and fix them throughout the fine-tuning phase.

\boldparagraph{Fine-tuned Components} For all the fine-tuning experiments, we only fine-tune the 3-layer MLP and linear layers shown in pink in \figref{fig:full_mask2former}. Their weights together constitute approximately $0.21\%$ of the entire model parameters.

\boldparagraph{Hyper-Parameters} After the model is trained on the closed-set setting, we fine-tune it for 5K iterations on Cityscapes~\cite{Cordts2016CVPR} using the setting of the closed-set training; AdamW~\cite{Loshchilov2019ICLR} optimizer with $0.05$ weight decay and $10^{-4}$ initial learning with polynomial scheduling. For every Cityscapes image used in fine-tuning, an object from the 300 COCO samples is uniformly chosen and pasted on the Cityscapes image with probability $p_{out} = 0.1$, which is independent for each image. The NLS score for outlier pixels is optimized to be higher than $\alpha_{out} = -0.1$ and for inlier pixels to be lower than $\alpha_{out} = -1.0$ (see the Ablations in the main paper).

\section{Additional Quantitative Results}
\label{sec:add_quantitative}
\input{tab/fishyscapes.tex}
\input{tab/supp_ablation.tex}
\tabref{tab:fishy} shows additional quantitative results on the Fishyscapes Benchmark~\cite{Blum2021IJCV}.
Due to technical difficulties on the official benchmark management side, we could only evaluate the results of fine-tuning the pixel decoder for the outlier supervision result. As shown in \tabref{tab:fishy}, without outlier supervision, NLS achieves second-best AP in Fishyscapes LaF and best in all other metrics. With outlier supervision, NLS consistently improves AP on both sets with a wide margin and obtains the best FPR@95 on FS Static.

To avoid confusion about fine-tuning specific modules, we also report the results of an ablation study in \tabref{tab:supp_ablation}. We analyze the effect of fine-tuning different parts of the model on validation sets of Road Anomaly~\cite{Lis2019ICCV} and Fishyscapes Lost and Found~\cite{Blum2021IJCV}. While on the Road Anomaly, the best AP is obtained by fine-tuning the MLP and the linear layers, on Fishyscapes LaF, the best results are obtained by fine-tuning the pixel decoder. We chose to report the results in the main paper by fine-tuning the MLP and the linear layers since they amount to a smaller number of parameters to fine-tune and do not compromise the closed-set performance.

\section{Additional Qualitative Results}
\label{sec:add_qual}
In \figref{fig:qual_anomaly_track} and \figref{fig:qual_obstacle_track}, we show additional qualitative results of NLS both with and without outlier supervision, compared to the state-of-the-art methods PEBAL~\cite{Tian2022ECCV} and DenseHybrid~\cite{Grcic2022ECCV}. Our method with mask classification better preserves the objectness. The proposed scoring function NLS reduces the false positives on the boundaries of inliers and ambiguous background regions compared to the baselines. These improvements can be observed more prominently on the obstacle track (\figref{fig:qual_obstacle_track}) under adverse weather and lighting conditions. The anomaly maps of the baselines are extracted using the pre-trained models provided in the respective public repositories.

In \figref{fig:qual_nls_vs_ml} we show more examples from Fishyscapes LaF \cite{Blum2021IJCV} that demonstrate the effect of NLS in eliminating false positive compared to Max Logit \cite{Hendrycks2022ICML}.

\section{Failure Cases}
\label{sec:failure}
We analyze some failure cases of our method in this section. The main reason for the failure cases is the high similarity to the inlier classes.

\boldparagraph{Tractors and Boats} As shown in \figref{fig:failure_tractor_and_boat}, %
NLS fails to detect tractors and boats due to their similarity to truck and vehicle instances. While outlier supervision slightly improves the segmentation, the model's confusion is clear from a lack of smoothness over the object region. Our method produces competitive results with outlier supervision compared to state-of-the-art PEBAL \cite{Tian2022ECCV} and DenseHybrid \cite{Grcic2022ECCV} which also use outlier supervision.

\boldparagraph{Far away Animals} As shown in \figref{fig:failure_animals}, animals that are situated relatively far from the camera are confused as the inlier pedestrian class. This can be attributed to the dominance of pedestrian class in the training data as well as the similarity of legged animals to a pedestrian in appearance.

\boldparagraph{Toy Cars} \figref{fig:failure_toy_cars} shows that the model fails to detect a toy car on the road and predicts it confidently as the inlier car class. While the class assignment can be considered semantically correct, it is still a hazard in a real driving scenario. Note that a small car can either be a toy car or a real car that is far away. Therefore, distinguishing real cars from toy cars might require additional information such as depth or scale.

\clearpage
\begin{figure*}[t!]
    \centering
    \includegraphics[width=\linewidth] {gfx/qual_anomaly_track.pdf}
    \vspace{-0.1in}
    \caption{\textbf{Qualitative Results on SMIYC Anomaly Track.} We compare NLS with and without outlier (OoD) supervision to the state-of-the-art methods PEBAL \cite{Tian2022ECCV} and DenseHybrid \cite{Grcic2022ECCV} on the anomaly track of the SMIYC benchmark. Even without outlier supervision, NLS with mask classification produces more coherent anomaly maps compared to other methods. PEBAL achieves better coverage of anomalies compared to DenseHybrid but at the cost of a higher false positive rate.}
    \vspace{-.1in}
    \label{fig:qual_anomaly_track}
\end{figure*}

\clearpage
\begin{figure*}[t!]
    \centering
    \includegraphics[width=\linewidth] {gfx/qual_obstacle_track.pdf}
    \vspace{-0.1in}
    \caption{\textbf{Qualitative Results on SMIYC Obstacle Track.} We compare NLS with and without outlier supervision to the state-of-the-art methods PEBAL \cite{Tian2022ECCV} and DenseHybrid \cite{Grcic2022ECCV}. Under adverse weather and difficult lighting conditions, NLS can consistently detect anomalies compared to DenseHybrid and reduce false positives compared to PEBAL.}
    \vspace{-.1in}
    \label{fig:qual_obstacle_track}
\end{figure*}

\clearpage
\begin{figure*}[t!]
    \centering
    \includegraphics[width=\linewidth] {gfx/qual_nls_vs_ml.pdf}
    \vspace{-0.1in}
    \caption{\textbf{Qualitative Comparison of NLS and Max Logit.} We provide more qualitative samples from Fishyscapes LaF \cite{Blum2021IJCV} to highlight the advantages of NLS over Max Logit \cite{Hendrycks2022ICML} while using a mask classifier for both. Note that NLS can better suppress the anomaly scores on the boundaries separating inlier classes as well as in ambiguous background regions at a higher distance from the camera.}
    \vspace{-.1in}
    \label{fig:qual_nls_vs_ml}
\end{figure*}

\clearpage
\begin{figure*}[t!]
    \centering
    \includegraphics[width=\linewidth] {gfx/failure_tractor_and_boat.pdf}
    \caption{\textbf{Failure Cases: Tractors and Boats.} Due to their high similarity to inlier car and truck classes, unknown objects like tractors or boats can be predicted as inliers. Outlier supervision can moderately help mitigate this limitation.}
    \label{fig:failure_tractor_and_boat}
\end{figure*}

\begin{figure*}[t!]
    \centering
    \includegraphics[width=\linewidth] {gfx/failure_animals.pdf}
    \caption{\textbf{Failure Cases: Animals Confused As Pedestrians.} As the pedestrian is one of the most frequent classes on Cityscapes, the model sometimes predicts animals that appear at a distance as pedestrians (highlighted in circles) on images from SMIYC Anomaly Track.} %
    \label{fig:failure_animals}
\end{figure*}

\begin{figure*}[t!]
    \centering
    \includegraphics[width=\linewidth] {gfx/failure_toy_cars.pdf}
    \caption{\textbf{Failure Cases: Toy Cars Predicted As Real Cars.} One confusing anomaly case for our model is small toy cars placed in front of the vehicle. Even though they can be semantically considered as cars, they are considered obstacles in a real driving scenario.}
    \label{fig:failure_toy_cars}
\end{figure*}
\clearpage

redpajama_set_name: RedPajamaArXiv


text:
12-story hotel proposed at edge of Cherry Creek
Thomas Gounley August 27, 2019 0
The 12-story hotel would replace the former bakery building at the corner of Garfield Street and Dakota Avenue. (Thomas Gounley)
Another hotel has been proposed for Cherry Creek, although it's outside of the core business district.
A site-development plan was submitted to the city last week, proposing a 12-story AC Hotels by Marriott property with 150 rooms at 370 S. Garfield St., about three blocks west of Colorado Boulevard.
The 0.34-acre property is owned by 370 S Garfield LLC, an entity formed by Fritz Reichardt, according to public records. Related entities have owned the property for decades.
The one-story retail building on the site dates to 1980, and was once home to bakery Andre's Confiserie Suisse, which operated in Cherry Creek from 1967 through the end of 2015. The property is currently being used as a construction staging area for the apartment complex going up next door.
A rendering of the hotel included in development plans submitted to the city. (Public records)
Mesa, Arizona-based Sunridge Hotel Group would develop the hotel. The company, which did not respond to a request for comment, hopes to open the hotel in December 2021, according to its website.
Sunridge owns a Courtyard by Marriott hotel in Westminster, and is also developing a Springhill Suites hotel in Colorado Springs, according to its website. The company also owns hotels in Arizona, New Mexico and Utah.
The hotel rooms would be on floors six through 12, according to the plans, which were drawn up by The Richardson Design Partnership of Salt Lake City. The project would have 113 parking spaces on floors two through five above the lobby.
Cherry Creek began the decade with one traditional hotel, the JW Marriott. Since then, three more — the Moxy, Jacquard (a Marriott Autograph brand) and Halcyon — have opened in the business district.
The former Inn at Cherry Creek is also being expanded by BMC Investments. CEO Matt Joblon said Monday he hopes to reopen it in the first quarter of 2021.
POSTED IN Commercial Real Estate, Featured, News

redpajama_set_name: RedPajamaCommonCrawl


text:
Frank McGeorge, MD, Local 4's Good Health Medical Expert
Published: March 13, 2014, 7:02 pm
Tags: Food, Health, News
Celiac diagnosis inspires gluten-free family business
Pierogi gaining popularity with gluten-free fans
ST. CLAIR SHORES, Mich. – Eating gluten-free is one of the most popular diet trends of the year.
However, for people with celiac disease, gluten-free is not just a diet -- it's a lifesaving and life-altering daily challenge.
Celiac disease is the most serious form of gluten sensitivity. Gluten is the stuff in wheat that makes bread chewy and helps it rise. It's also a hidden additive in many foods beyond breads. When you have celiac disease, you must eliminate all gluten.
Four years ago, when he was 12, Charley Bemiss started to suffer from stomach pain and other symptoms.
"The headaches and stomach aches were the main things with the food," said Charley.
His mother Alicia, herself a two-time cancer survivor, also noticed other things.
"He was losing his hair. He'd take his hand and put it through his hair and show me his hand and it was full of hair that had come out," said Alicia. "He was having very bad migraines, which don't run in our family, but they were so bad that he would cry. He was only 12 at the time."
Ultimately Charley had blood tests and a biopsy of his intestines which confirmed the diagnosis of celiac disease. It wasn't easy.
"I accepted it eventually, but first coming back from the doctors, coming home I wasn't ready to live and change my whole lifestyle," said Charley.
Now 16 years old and a junior at De LaSalle, he's made adjustments. He's also inspired a new family business.
"My parents were born in Poland," said Alicia. "I was raised very Polish. My son loves his pierogi."
The problem was, pierogi are not normally gluten-free. But Alicia was determined to make this ultimate comfort food available to her son.
"I said, 'Well, let's make some of our own.' After making them at home and seeing his face and how much he loved them, I decided, you know what, there's got to be more people out there," said Alicia.
And so Old World Gluten-Free Pierogi was born. It took Alicia and her family many months of planning and lots of determination to build the business, but Charley's celiac disease was a driving force.
They started producing and selling their pierogi only 6 months ago out of a carpet cleaning storefront on Harper and Centennial in Saint Clair Shores. Through word of mouth and social media, they have already built a loyal following, and they are sending their first shipment of pierogi to Westborn Market this week.
To visit Old World Gluten-Free Pierogi's website and see their full pierogi selection and ingredients, click here.
Copyright 2014 by ClickOnDetroit.com. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.
Frank McGeorge, MD
Dr. McGeorge can be seen on Local 4 News helping Metro Detroiters with health concerns when he isn't helping save lives in the emergency room at Henry Ford Hospital.


text:
'9/11 planners' set for US trial
The US has formally sent to trial five suspected al-Qaeda militants believed to have planned the 9/11 terror attacks. The five Guantanamo Bay inmates, including alleged mastermind Khalid Sheikh Mohammed, will be tried by a military commission. They will face charges including terrorism, hijacking, conspiracy, murder anddestruction of property. They could face the death penalty if found guilty, the Pentagon confirmed. Khalid Sheikh Mohammed and the four others - Waleed bin Attash, Ramzi Binalshibh, Ali Abd al-Aziz Ali and Mustafa Ahmad al-Hawsawi - are expected to be tried together, the Pentagon added. They are accused of planning and executing the terror attacks of 11 September 2001, which saw hijacked planes strike New York, Washington and Shanksville, Pennsylvania. A total of 2,976 people died in the attacks. Khalid Sheik Mohammed, the most high profile of the suspects, has allegedly admitted to masterminding the 9/11 attacks and others. Captured in 2003, he has been at Guantanamo Bay since 2006 Ramzi Binalshibh, a Yemeni who allegedly helped locate flights schools for the hijackers Ali Abd al-Aziz Ali, allegedly helped nine of the hijackers enter the US Mustafa Ahmad al-Hawsawi, of Saudi Arabia, is said to have helped set up some of the hijackers with money, clothes and credit cards Waleed bin Attash, a Yemeni, is said to have been a bodyguard to Osama Bin Laden and trained some 9/11 hijackers US prosecutors allege that he was involved with a host of other terrorist activities. These include the 2002 nightclub bombing in Bali, Indonesia, the 1993 World Trade Center bombing, the murder of American journalist Daniel Pearl and a failed 2001 attempt to blow up an airliner using a shoe bomb. Khalid Sheikh Mohammed has alleged that he was repeatedly tortured during his detention in Cuba. CIA documents confirm that he was subjected to simulated drowning, known as waterboarding, 183 times. Also facing trial are: Waleed bin Attash, a Yemeni; Ramzi Binalshibh, a Yemeni who allegedly helped find flight schools for the hijackers; Ali Abd al-Aziz Ali, accused of helping nine of the hijackers travel to the US; and Mustafa Ahmad al-Hawsawi, a Saudi, accused of helping set up the hijackers with money, clothes and credit cards.
Написал - Breslin
Al-Qaeda bomber 'double agent'
US soldier to face murder charges
Anwar Al-Awlaki Dead: U.S.-Born Al Qaeda Cleric Killed In Yemen
Key Paris attacks suspect arrested
Suspect says he is Brussels man in hat
US 'foils new underwear bomb plot'
Underwear bomber given life term
UN condemns Yemen suicide bombing
Three charged after Belgium raids
Paris suspect changed suicide plan


text:
CLAWS (TNT) - Jimmy Jean-Louis is slated to recur on the series as Dr. Gregory Ruval, "a love interest of Niecy Nash's character." (Deadline.com)
HAPPY VALLEY (HBO) - The pay channel's long-in-the-works docudrama about the Penn State football scandal - starring Al Pacino as Joe Paterno - is back on track with Barry Levinson now on board to direct. He succeeds Brian De Palma on the project, which was originally set up in 2014. Debora Cahn, John C. Richards and David McKenna are now the credited co-writers with Levinson, Tom Fontana, Jason Sosnoff, Edward R. Pressman, Rick Nicita and Lindsay Sloane executive producing. (Deadline.com)
HOMECOMING (New!) - Julia Roberts is reportedly in talks to star in Sam Esmail's proposed drama - "a political thriller that centers on a caseworker at a secret government facility" - from Universal Cable Productions. (Deadline.com)
JACK RYAN (Amazon) - Amir El-Masry has scored a role on the upcoming drama as Ibrahim, "the most trusted member of Suleiman's (Ali Suliman) sect, though softer and more cerebral than his cohorts." (Deadline.com)
JALEN VS. EVERYBODY (ABC) - Sibling ESPN plans to broadcast the Jalen Rose-led comedy pilot, which didn't make the Alphabet's 2017-18 schedule, this Tuesday at 7:30/6:30c. (@djacoby)
LAW & ORDER TRUE CRIME (NBC) - Julianne Nicholson has been tapped to play Jill Lansing, "Abramson's (Edie Falco) whip-smart and trusted partner on the defense team, responsible for representing Lyle (Gaston Villanueva)." (Deadline.com)
MAN WITH A PLAN (CBS) - Ethan Sandler and Adrian Wenner ("New Girl") have joined the show's second season as co-executive producers. Said development is part of a new overall deal the duo signed with CBS Television Studios. (Deadline.com)
TAKEN (NBC) - Greg Plageman ("Person of Interest") has signed on as the new showrunner of the series. He takes over from Alexander Cary. (Deadline.com)
UNREAL (Lifetime) - The cable channel has quietly pushed the show's third season launch to the first part of 2018. (@ConstanceZimmer)
[june 2017]
· CLAWS (TNT)
· HOMECOMING (AMAZON)
· JALEN VS. EVERYBODY (ABC)
· LAW & ORDER TRUE CRIME (NBC)
· MAN WITH A PLAN (CBS)
· NEW GIRL (FOX)
· PATERNO (HBO)
· PERSON OF INTEREST (CBS)
· TAKEN (NBC)
· TOM CLANCY'S JACK RYAN (AMAZON)
· UNREAL (HULU)


text:
The New International Version
Jesus Goes to the Festival of Tabernacles
7 After this, Jesus went around in Galilee. He did not wanta to go about in Judea because the Jewish leaderse there were looking for a way to kill him.f 2 But when the Jewish Festival of Tabernaclesg was near, 3 Jesus' brothersh said to him, "Leave Galilee and go to Judea, so that your disciples there may see the works you do. 4 No one who wants to become a public figure acts in secret. Since you are doing these things, show yourself to the world." 5 For even his own brothers did not believe in him.i
6 Therefore Jesus told them, "My timej is not yet here; for you any time will do. 7 The world cannot hate you, but it hates mek because I testify that its works are evil.l 8 You go to the festival. I am notb going up to this festival, because my timem has not yet fully come." 9 After he had said this, he stayed in Galilee.
10 However, after his brothers had left for the festival, he went also, not publicly, but in secret. 11 Now at the festival the Jewish leaders were watching for Jesusn and asking, "Where is he?"
12 Among the crowds there was widespread whispering about him. Some said, "He is a good man."
Others replied, "No, he deceives the people."o 13 But no one would say anything publicly about him for fear of the leaders.p
Jesus Teaches at the Festival
14 Not until halfway through the festival did Jesus go up to the temple courts and begin to teach.q 15 The Jewsr there were amazed and asked, "How did this man get such learnings without having been taught?"t
16 Jesus answered, "My teaching is not my own. It comes from the one who sent me.u 17 Anyone who chooses to do the will of God will find outv whether my teaching comes from God or whether I speak on my own. 18 Whoever speaks on their own does so to gain personal glory,w but he who seeks the glory of the one who sent him is a man of truth; there is nothing false about him. 19 Has not Moses given you the law?x Yet not one of you keeps the law. Why are you trying to kill me?"y
20 "You are demon-possessed,"z the crowd answered. "Who is trying to kill you?"
21 Jesus said to them, "I did one miracle,a and you are all amazed. 22 Yet, because Moses gave you circumcisionb (though actually it did not come from Moses, but from the patriarchs),c you circumcise a boy on the Sabbath. 23 Now if a boy can be circumcised on the Sabbath so that the law of Moses may not be broken, why are you angry with me for healing a man's whole body on the Sabbath? 24 Stop judging by mere appearances, but instead judge correctly."d
Division Over Who Jesus Is
25 At that point some of the people of Jerusalem began to ask, "Isn't this the man they are trying to kill?e 26 Here he is, speaking publicly, and they are not saying a word to him. Have the authoritiesf really concluded that he is the Messiah?g 27 But we know where this man is from;h when the Messiah comes, no one will know where he is from."
28 Then Jesus, still teaching in the temple courts,i cried out, "Yes, you know me, and you know where I am from.j I am not here on my own authority, but he who sent me is true.k You do not know him, 29 but I know himl because I am from him and he sent me."m
30 At this they tried to seize him, but no one laid a hand on him,n because his hour had not yet come.o 31 Still, many in the crowd believed in him.p They said, "When the Messiah comes, will he perform more signsq than this man?"
32 The Pharisees heard the crowd whispering such things about him. Then the chief priests and the Pharisees sent temple guards to arrest him.
33 Jesus said, "I am with you for only a short time,r and then I am going to the one who sent me.s 34 You will look for me, but you will not find me; and where I am, you cannot come."t
35 The Jews said to one another, "Where does this man intend to go that we cannot find him? Will he go where our people live scatteredu among the Greeks,v and teach the Greeks? 36 What did he mean when he said, 'You will look for me, but you will not find me,' and 'Where I am, you cannot come'?"w
37 On the last and greatest day of the festival,x Jesus stood and said in a loud voice, "Let anyone who is thirsty come to me and drink.y 38 Whoever believesz in me, as Scripture has said,a rivers of living waterb will flow from within them."c c 39 By this he meant the Spirit,d whom those who believed in him were later to receive.e Up to that time the Spirit had not been given, since Jesus had not yet been glorified.f
40 On hearing his words, some of the people said, "Surely this man is the Prophet."g
41 Others said, "He is the Messiah."
Still others asked, "How can the Messiah come from Galilee?h 42 Does not Scripture say that the Messiah will come from David's descendantsi and from Bethlehem,j the town where David lived?" 43 Thus the people were dividedk because of Jesus. 44 Some wanted to seize him, but no one laid a hand on him.l
Unbelief of the Jewish Leaders
45 Finally the temple guards went back to the chief priests and the Pharisees, who asked them, "Why didn't you bring him in?"
46 "No one ever spoke the way this man does,"m the guards replied.
47 "You mean he has deceived you also?"n the Pharisees retorted. 48 "Have any of the rulers or of the Pharisees believed in him?o 49 No! But this mob that knows nothing of the law—there is a curse on them."
50 Nicodemus,p who had gone to Jesus earlier and who was one of their own number, asked, 51 "Does our law condemn a man without first hearing him to find out what he has been doing?"
52 They replied, "Are you from Galilee, too? Look into it, and you will find that a prophet does not come out of Galilee."q
About The New International Version
The NIV is the world's most read and trusted contemporary English Bible translation. It follows the principle of "dynamic equivalence" to ensure crystal clear understandable English. The NIV is the most readable English Bible ever produced.
Copyright 1973, 1978, 1984, 2011 Biblica, Inc.™
Used by Permission. All rights reserved worldwide.
"New International Version" and "NIV" are registered trademarks of Biblica, Inc.™ Used by permission.
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Notice of copyright must appear on the title or copyright page as follows:
"Scripture quotations taken from The Holy Bible, New International Version® NIV®. Copyright © 1973, 1978, 1984, 2011 by Biblica, Inc.™Used by permission. All rights reserved worldwide."
The "NIV" and "New International Version" are trademarks registered in the United States Patent and Trademark Office by Biblica, Inc.™
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Any commentary or other biblical reference work produced for commercial sale, that uses the NIV® text must obtain written permission for use of the NIV® text.
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niv2011


text:
#ifndef GDCMSTRINGFILTER_H
#define GDCMSTRINGFILTER_H

#include "gdcmDataElement.h"
#include "gdcmDicts.h"
#include "gdcmFile.h"

namespace gdcm
{

/**
 * \brief StringFilter
 * StringFilter is the class that make gdcm2.x looks more like gdcm1 and transform the binary blob
 * contained in a DataElement into a string, typically this is a nice feature to have for wrapped language
 */
class GDCM_EXPORT StringFilter
{
public:
  StringFilter();
  ~StringFilter();

  ///
  void UseDictAlways(bool) {}

  /// Allow user to pass in there own dicts
  void SetDicts(const Dicts &dicts);

  /// Convert to string the ByteValue contained in a DataElement. The
  /// DataElement must be coming from the actual DataSet associated with File
  /// (see SetFile).
  std::string ToString(const DataElement& de) const;

  /// Directly from a Tag:
  std::string ToString(const Tag& t) const;

  /// Convert to string the ByteValue contained in a DataElement
  /// the returned elements are:
  /// pair.first : the name as found in the dictionary of DataElement
  /// pari.second : the value encoded into a string (US,UL...) are properly converted
  std::pair<std::string, std::string> ToStringPair(const DataElement& de) const;
  /// Directly from a Tag:
  std::pair<std::string, std::string> ToStringPair(const Tag& t) const;

  GDCM_LEGACY(std::string FromString(const Tag&t, const char * value, VL const & vl))

  /// Convert to string the char array defined by the pair (value,len)
  std::string FromString(const Tag&t, const char * value, size_t len);

  /// Set/Get File
  void SetFile(const File& f) { F = f; }
  File &GetFile() { return *F; }
  const File &GetFile() const { return *F; }

  /// Execute the XPATH query to find a value (as string)
  /// return false when attribute is not found (or an error in the XPATH query)
  /// You need to make sure that your XPATH query is syntatically correct
  bool ExecuteQuery(std::string const &query, std::string & value) const;

protected:
  std::pair<std::string, std::string> ToStringPair(const Tag& t, DataSet const &ds) const;
  bool ExecuteQuery(std::string const &query, DataSet const &ds, std::string & value) const;

private:
  std::pair<std::string, std::string> ToStringPairInternal(const DataElement& de, DataSet const &ds) const;
  SmartPointer<File> F;
};

} // end namespace gdcm

#endif //GDCMSTRINGFILTER_H

redpajama_set_name: RedPajamaGithub


text:
Somalia's SJS completes its fourth course in human rights journalism in Garowe
By Balaleti	 Last updated Dec 30, 2021
MOGADISHU, Somalia December 30, 2021 – Somali Journalists Syndicate (SJS) completes its fourth Human Rights Journalism training in Garowe, the capital of Puntland State of Somalia on Wednesday, December 29.
The three-day training in reporting on human rights through documentation and opinion formation benefited 25 local reporters from independent media stations and state media. 10 of the trainees were female reporters. The activity is part of ongoing journalism training projects supported by the National Endowment for Democracy (NED).
Trainees pose for a group photo at the end of a three-day human rights training in Garowe, Puntland, Wednesday, December 29, 2021. | PHOTO / SJS.
"In Garowe, we have selected very talented journalists from the local media and we are glad that many of them had the passion to report and advocate for human rights issues. The Garowe Training is part six training initiative with a goal of 150 journalists trained in the subject of human rights throughout Somalia, thanks to the useful financial support we had received from the National Endowment for Democracy, says SJS's Secretary General Abdalle Ahmed Mumin.
The 25 journalists will receive mentorship support when they join SJS's network of human rights reporters who are currently active in four regions.
Chairman of the Puntland Human Rights Defender's Office, Sa'id Abdi Muumin, speaks at the conclusion of a three-day human rights journalism training course in Garowe, Puntland, on Wednesday, December 29, 2021. | PHOTO / SJS.
Based on past experience, the chair of the Office of Puntland Human Rights Defender, Sa'id Abdi Muumin, noted that learning about human rights would make it easy for the journalist to distinguish between stories that fall into the category of human rights violations and those that do not. .
"Therefore, journalists must learn international standards and local rules that they must follow when reporting and disseminating human rights information. I encourage you to implement what you have learned, to be balanced and credible," Sa'id Muumin added. Our office is ready to work with the journalists and we appeal your cooperation with us. "
Somali minister clashes with UK lawmaker over North…
On behalf of the Ministry of Information in Puntland, Director-General Mohamed Isse Osman praised the training opportunities for local journalists and appealed for more skills-based training, including the area of human rights and humanitarian surveillance.
Director General of Puntland's Ministry of Information, Mohamed Isse Osman, speaks at the start of a three – day training course in human rights journalism in Garowe, Puntland, on Monday 27 December 2021. | PHOTO / SJS.
"Human rights related training has been carried out in the past but we are still demanding more and more to upgrade the skills of our journalists. We realize that the number of journalists given this opportunity is equal to 10% of the total number of journalists in Garowe. good start ", added CEO Mohamed Isse Osman.
Puntland's Deputy Minister for Women and the Family, Sahra-Kin Abdi Hersi, acknowledged for her part the importance of specialized training to improve the skills of journalists as it is necessary to rebuild the country and society.
"We thank SJS for their commitment to improving the skills of journalists as part of improving the society that is necessary for the reconstruction of the country. We can imagine the state in which journalists work in Somalia. It is very difficult and I greet them. Our journalists must however, reconsider the situation they are working in, "said Deputy Minister Hersi.
Deputy Minister for Women and the Family, Sahra-Kin Abdi Hersi, speaks at the start of a three-day human rights journalism training program in Garowe, Puntland, on Monday 27 December 2021. | PHOTO / SJS.
"Although I am a friend of the media, I was deeply disappointed with how the media covered the recent conflict in Bosaso. Therefore, let our journalists be ambassadors for peace and human rights," Mrs Hersi added.
During the opening and closing of the three-day training, the SJS team was joined by representatives of the local media associations: Media Association of Puntland (MAP) and Women in Media Initiative Somalia, Office of Puntland Human Rights Defender as well as human rights lawyers and local civilians society.
(This story #Somalia's SJS completes its fourth course in human rights journalism in Garowe. Has not been edited by Axadle Staff and has been auto-generated from the Syndicate feed.) while we endeavor to keep the information up to date and correct, we make no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability or availability with respect to the website or the information or related graphics contained on the post for any purpose.
Balaleti 547 posts


recommend it for a live environment at this stage.
> ahhh, i see, is there any way to use services with inspircd?
> > I'm not too sure what would cause the "unknown message from server"
> > > problem, can anyone help me?
> > > ALPS Technologies INC.


Now many of our readers must be wondering what Manual Therapy is? Manual therapy, also known as manipulative therapy, is a method of physical treatment to help treat various illnesses and conditions. This form of treatment is mainly used by chiropractors, physiotherapists, physiatrists, occupational therapists, osteopathic physicians, and osteopaths, as manual therapy is highly beneficial in treating pain and disability related to musculoskeletal system. Manual therapy usually involves massaging, kneading and manipulation of the joints and muscles with joint manipulation and joint mobilization.
It helps in pain reduction (acute and chronic).
It gives you an increased range of motion (ROM).
Brings down the inflammation of the soft tissue.
Enhances the healing of contractile and non-contractile tissue.
Helps in easy and fluid body movements.
The special thing about Manual Therapy is, as the name itself suggests, it utilizes hands instead of machines/devices. The practitioners use their hands to apply pressure on the muscles, tissues and for joint manipulation in order to decrease pain which can be caused by muscle stiffness, spasm and dysfunction of the joints.
We have quite a number of the techniques of this therapy covered under our "Manual Therapy" section, such as Chiropractic Treatment, Physical Therapy, Massage Therapy and Spa Therapy. Each of these topics further has a vast array of additional sub topics. You can read all this in detail under our Manual Therapy section.
NOTE: It is our request to the readers to get these therapies and different techniques mentioned here done by a professional only. Readers should not attempt to try out these techniques on their own and without prior medical consult. The information given here is for the sole purpose of imparting knowledge and to create awareness of the different treatment options available to a patient and is not a substitute to actual treatment or a visit to the doctor.


Everyone goes through hard times—but how do you get through life's darkest storms? You will hear from one man who faced a terrifying accident, and is still recovering. He and his wife describe what THEY did when God didn't make sense to their family. Don't miss their amazing story.
John Ramstead's expertise is in complex estate planning, generational wealth transfer and asset protection. He functions as the client's trusted advisor on all matters affecting any related issues critical to assuring the smooth financial functioning of a multigenerational family or philanthropic plan.
Prior to joining Financial Designs, Ltd., Mr. Ramstead was a wealth manger with Bernstein Global Wealth Management. Before that he was an executive with a Fortune 500 company. Prior to that he helped to successfully start and sell two different businesses. Mr. Ramstead's early career was as a U.S. Navy F-14 pilot and flew combat sorties in Desert Storm and was an instructor at the Navy Strike Fighter Weapons School at the Miramar Naval Air Station in San Diego, CA.
Mr. Ramstead is a Minnesota native and graduated high school from St. Thomas Academy. He received a B.S. in Electrical Engineering from Rensselaer Polytechnic Institute with a full Navy ROTC scholarship.
John and his wife Donna live in Denver with their three boys. He enjoys time with his family, coaching, travel, reading and any outdoor activity. He is active in the community and serves on the board of the Colorado Make A Wish Foundation, Colorado Faith and Freedom Coalition and the Colorado Rough Riders.


What comes to mind when we think of rivers and river systems? Do we imagine swimming in pristine waters beneath overhanging and majestic trees? Are we picturing fully functioning ecosystems or are contemporary worlds focussed on the economic fortunes to be gained from exploiting rivers, depriving them of their life's blood?
When I started this series in 2017 my focus was the muted beauty of the Darling Barka. The sombre colours, the slope of banks and the light at different times of day. Since September 2018 when ABC's Four Corners 'Pumped' exposed political scandals, government corruption, water theft - and an avalanche of media attention - these escalating tensions have seeped into my images. A shift to inverted views; the mirror reflection of the river above, as seen on the water's surface. And in response to the latest scandals; viewed through dark branches, the expanse of the river reduced to stagnant green pools with increasing stretches of dry river bed.


a blue cheese made in cow's milk and originating in Gorgonzola in Lombardia. It melts well and is often used in sauces. Gorgonzola dolce is younger and milder. Picante is older and stronger. Dolcelatte is a particular brand of Gorgonzola and is the mildest available. If unable to find this cheese, use blue cheese.


text:
TV interview: Why the producer of Walter Presents streaming service is moving into books
Eccentric telly curator Walter Iuzzolino is to become an 'editor at large' for a publishing house. By Stephen Armstrong
Stephen Armstrong
Sunday May 24 2020, 12.01am, The Sunday Times
Walter Iuzzolino
DAVID DETTMANN
The lockdown is a time of seething potential. What if you stepped up your knowledge of modern European literature? But where to start? Unesco estimates there are 2.2m new books published worldwide every year. What's needed is, say, Walter Iuzzolino, the charmingly eccentric Italian producer behind the Walter Presents streaming service, to help you. Which, as of this month, with the publication of David Foenkinos's French bestseller The Mystery of Henri Pick, is exactly what he's doing.
The Mystery of Henri Pick is so ridiculously French that if it started smoking Gitanes while you read it, you wouldn't be entirely surprised. It's a lightly comedic teaser about an astonishingly successful novel that's accidentally discovered in a library of rejected manuscripts. That leads to a


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and long term challenges you face to school improvement.


text:
Jolie Justus
Non-discrimination bill gets another hearing in Missouri legislature
Mike Colona is getting his wish. Two months ago, the Democratic representative from St. Louis attended a Senate committee hearing on Senate Bill 237. The…
St. Louis' Newly Wed Gay Couples Reflect On Why Attitudes On Gay Marriage Have Shifted
Jason Rosenbaum
Tod Martin wasn't going to let 20 words keep him from marrying David Gray.While it took more than 20 years, St. Louis officials last week issued Martin…
In Missouri Senate, Justus Went From Partisan To Pragmatist
The Missouri Senate had seven new members after the smoke cleared from the 2006 election cycle. Only two served for the maximum time allowed under term…
Missouri Senate Passes 'Fix' To Criminal Code Demanded By Nixon
The Missouri Senate has passed legislation containing the so-called "fixes" that Gov. Jay Nixon wants added to the criminal code revision.Nixon told…
Nixon Allows Criminal Code Rewrite To Become Law
Gov. Jay Nixon has allowed a comprehensive rewrite of Missouri's criminal code to become law without his signature.Nixon says the 645-page bill contains…
Missouri Senate Passes State Budget, Negotiations Over Final Version Are Next
The Missouri Senate passed the rest of the state budget Tuesday, after taking care of the first five bills on Monday. Those debates were routine for the…
Comprehensive Rewrite Of Missouri Criminal Code Passes Easily But Future Unsure
Missouri lawmakers have sent Gov. Jay Nixon a bill to rewrite the state's criminal code for the first time in more than 30 years. The wide-ranging…
Five Questions To Ask In Legislative Session's Waning Weeks
With roughly a month left to go before adjournment, many of the Missouri General Assembly's big issues remain unresolved. That's not too surprising.…
Nixon Expresses Caution About Criminal Code Overhaul
Gov. Jay Nixon says he's wary about signing a wholesale revision of the state's criminal code. For the past few years, the state's legal community has…
Second Half Of Missouri 2014 Legislative Session Underway
The second half of Missouri's 2014 regular session is underway. Leaders in both chambers and from both parties remain focused on crafting a state budget…


text:
The Best Mountain Bike Tires of 2016 – Readers' Choice Awards
By Aaron Chamberlain
October 17, 2016 Updated April 19, 2022
Singletracks may receive compensation for purchases made through any affiliate links in this article.
Our staff has often argued that a fresh set of mountain bike tires with an appropriate tread pattern for your trails is one of the best–and most affordable–upgrades you can make to your bike. However, with all the tire companies and their various offerings out there, it can be hard to know where to start. Well, wonder no more! During our reader's survey conducted this summer, we asked you to tell us what your favorite treads are. Below are the top 10 picks, according to the results from that survey. While one company in particular dominated this list, the mountain bike tires our readers picked span the spectrum of trail conditions. Some of the tread patterns may be better suited to your trails or riding style than others, but you can't really go wrong with any of them.
Without further ado, here are the best mountain bike tires of 2016, as chosen by our readers.
10. Schwalbe Racing Ralph Tire: $45-$100
It doesn't take an astute reader to discern the intentions of this tire–the Racing Ralph is all about maximizing speed. To do so, the center tread blocks are low and closely-spaced. Currently in its third generation, Schwalbe offers the Racing Ralph in a dizzying array of casing, compound, and size options.
Diameters available: 26″, 27.5″, 29″
Widths available: 2.10″, 2.25″, 2.35″
9. Kenda Nevegal Tire: $40-$65
The Nevegal has been the do it all, aggressive trail tire in Kenda's line for about as long as I can remember. As such, the Nevegal has chunky cornering knobs and large center knobs which are heavily ramped in an effort to reduce rolling resistance. Kenda also offers a DH version of the Nevegal for those that need ultimate durability.
Diameters available: 24″, 26″, 27.5″, 29″
Widths available: 2.10″, 2.20″, 2.35″, 2.50″, 2.70″
8. Maxxis Ikon Tire: $55-$100
With small, tightly-spaced knobs, the Ikon from Maxxis is another mountain bike tire intended for XC racing. It's certainly not limited to buff trails, though, particularly if you opt for the larger, 2.35″ width. The high volume casing gives a comfortable ride, which is especially beneficial on a hardtail. One of the traits I like best about the Ikon is that it's very predictable. The tire loses traction gradually, which allows you to correct before you end up on the ground.
Widths available: 2.00″, 2.20″, 2.35″, 2.80″
7. Maxxis Minion DHR II Tire: $61-$105
How can I say this? The original Minion DHR was, um, well, it sucked. Thankfully though, the second generation DHR II is an excellent tire. Essentially, the tread pattern is a mashup of two of Maxxis' other standout tires–the High Roller II and Minion DHF. The center tread is similar to that of the HR II and the side knobs are copied straight from the DHF, with a little boost in size. If you have shied away from the DHR II because of a bad experience with the original, fear not. It's a wholly different tire, and that's a good thing.
6. Schwalbe Hans Dampf Tire: $46-$97
Schwalbe bills the Hans Dampf as a jack-of-all-trades mountain bike tire. It's not the fastest-rolling or the best-cornering, but it does both fairly well. Since it works across a variety of conditions, many companies who use Schwalbe spec the Hans Dampf on their trail bikes. Personally, I like it better as a rear tire than a front since it has a tendency to feel vague at the limit.
Widths available: 2.25″, 2.35″
5. Continental Trail King Tire: $30-$70
The Continental Trail King mountain bike tire took the third spot on our list last year, but slips a couple spots to number five for 2016. As the name suggests, the Trail King is an all-purpose trail tire. With tall blocks and plenty of space between them, the Trail King can claw its way over trail features such as roots and rocks. Continental offers two casing options: RaceSport and ProTection, the latter of which offers the most puncture resistance. It's also available with Continental's Black Chili rubber compound, which has excellent grip and tread wear–a tough feat to accomplish.
4. Schwalbe Nobby Nic Tire: $30-$102
Schwalbe's Nobby Nic might just be the most commonly spec'd tire on new mountain bikes. It's available in a wide variety of configurations, price points, and the tread pattern itself is versatile. Many companies chose the Nobby Nic because it is light, which keeps complete bike weights down, but that often came with a serious tradeoff in durability. However, Schwalbe has just revamped the Nic, making the knobs larger and the casing more robust. After riding it on a handful of test bikes, I have to say I was impressed with the updated version.
Widths available: 2.10″, 2.25″, 2.35″, 2.60″, 2.80, 3.00″
3. Maxxis Minion DHF Tire: $51-$105
There's a reason the Minion DHF hasn't seen a redesign à la the DHR. It's because the DHF is damn-near perfect as it is. Many riders–myself included–consider it the gold standard for aggressive treads. It rolls well for such a burly tire thanks to the heavily-ramped center tread, and it has a predictable transition to the side knobs. And once you're on those side knobs, you can really lay into the tire. If you want more confidence in the corners, mount up a set of these.
2. Maxxis Ardent Tire: $61-$65
The Maxxis Ardent is another mountain bike tire you often see spec'd stock. It bridges the gap nicely between the XC-focused Ikon and the more aggressive Minion DHF, which makes the Ardent ideal for short travel trail bikes. The tread pattern works well in a variety of terrain, but does struggle in loose over hard–tough conditions for any tire. I personally am not a fan of the Ardent, as I find it to be vague at the limit like the Schwalbe Hans Dampf. However, if you can ride just inside that limit, it's a ripping trail tire.
1. Maxxis High Roller II Tire: $62-$105
Messing with a mountain bike tire as legendary as the original High Roller was risky. After all, humans in general are averse to change. However, once most people tried the High Roller II, they forgot all about its predecessor. That's because the High Roller II does everything better–it rolls faster, brakes better, and has a slightly better transition to the side knobs. I say slightly because there is still a little bit of vagueness in that transition zone going from the center to side knobs. It's best to use a firm, deliberate hand to get over on those knobs quickly. Once you get used to that quirk, you'll be schralping berms with the best of them.
Aaron Chamberlain
I love any kind of mountain biking, but especially all-day rides with plenty of climbing and descending. Used to race a lot and I still do the occasional endurance event or stage race. Most of my rides now are just about enjoying my time in the woods.
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Cool to see the HRII on top. Great tire. True about getting the side knobs hooked up. I'm not good enough to intentionally lean it over far enough, but instead, it usually bails me out of a messy corner (usually one that I've messed up).
Greg Heil
This spring I switched my front tire over to the DHF and man, I've been cornering like there's no tomorrow all year! I have to totally agree on the high marks it received. Also, I'm running a HRII in the rear, so I guess I have two of the all-time-best tires covered.
mongwolf
Reply to Greg Heil
I've tried a variety of Specialized tires and also the Nevegals (old and new). I guess it's time to step over and give the Maxxis a try — the DHF in front and HRII in the rear. I've been considering this for while. In Specialized I had settled with the Butcher Grid in front and have liked that tire a lot. So I think jumping over to the DHF shouldn't produce too much surprise. On the rear I currently have the newer Nevegal (but not the XPro). When I bought the tire, I put it on front at first and hated it, but once on the rear, it shines imo — very consistent and good follower. The biggest issue with it – and it's a big issue – is that I couldn't get it to set up tubeless and neither could a couple of mechanics. So I'm running tubeless in front and tubes in the rear this season. Yuck. I'll be really happy to get out of the rear tubes next year.
Does anyone have experience with both the Butcher and DHF as front tires? They are so similar it seems with the Butch being a copy cat essentially — at least in the tread pattern. As I mentioned above I am running a Butcher Grid in front right now and like it. But I think I'll give the DHF a try next.
Joel DH
Um…Where, the heck, is Specialized on this list? This is a list of the most popular tires, not necessarily the best. To determine the best, look at the tires DH racers are riding. Then again, everybody seems to have their own definition on what a good tire is. Some prefer speed, others grip, some mud clearance. One would have to decide which factor is the most important in a tire, and then test many different tires to assess the capability of each product. Only then will we have the true "best" tire.
One of my personal favorites is the Specialized Captain tire. It rarely "steers" me wrong.
Reply to Joel DH
Okay let's do that. Here are the results from the 2016 World Cup Elite Men by tire:
Round 1:
1. Maxxis
1. Specialized
3. Hutchinson
World Championships:
3. Schwalbe
Final tally:
Maxxis – 16 podium spots (67% of the time)
Specialized – 6 podium spots (25% of the time)
Hutchinson – 1 podium spot (4.2% of the time)
Schwalbe – 1 podium spot (4.2% of the time)
So, even by your criteria, Maxxis comes out on top.
Reply to Aaron Chamberlain
Hahaha yes!
You got me. You have exposed my Specialized bias. I think (gasp!) I may try a Maxxis tire. We shall see…
Deeperrin
Think the author was going for a more XC list and not a DH comparison or there wold be more Specialized on the list.
Reply to Deeperrin
The operative words here are READERS' CHOICE. This has nothing to do with the author's opinion on tires. And no, it's not a DH comparison either.
Jeffrey Chen
Here's my vote for trail riding and enduro racing: Maxxis DH-F front and DH-R rear with a Nobby Nic as an alternate rear tire choice if lower rolling resistance is important. Data and subjective evals are at: https://www.dirtmerchantbikes.com/special-events/2016/5/13/pacific-northwest-spring-2016-endurotrail-tire-test-hans-dampfnobby-nic-dh-fdh-r-butcherpurgatory
Tagged: 2016, 26 tire, 27.5 tire, 29 tire, best, best of, continental, continental mountain bike tire, continental mtb tire, continental tire, continental trail king, dh tire, gear, kenda, kenda mountain bike tire, kenda mountain bike tires, kenda nevegal, kenda tire, kenda tires, maxxis, maxxis ardent, maxxis high roller, maxxis ikon, maxxis minion, maxxis mountain bike tire, maxxis mtb tire, maxxis tire, maxxis tires, mountain bike tire, mountain bike tires, mountain bike tires, mtb tire, mtb tires, results, schwalbe, schwalbe hans dampf, schwalbe nobby nic, schwalbe tire, schwalbe tires, scwalbe racing ralph, survey, tire, trail tire, tyre, tyres, xc tire, gear
First published October 17, 2016 @ 4:05:12
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