text:
\section{Background: Encoder-Decoder Network for Semantic Segmentation}\label{back}
In recent years, convolutional neural networks have been driving a lot of success in computer vision. Following the early success of CNNs on image classification~\cite{krizhevsky12_nips}, there has been a line of work~\cite{badrinarayanan15_arxiv,long15_cvpr,noh15_iccv} on adapting CNNs for pixel-wise dense image labeling problems such as semantic image segmentation. Most of the CNN-based semantic segmentation algorithms can be characterized in terms of a generic encoder-decoder architecture~\cite{badrinarayanan15_arxiv,noh15_iccv}. The encoder network is usually a convolutional network that extracts features from an input image. Similar to the CNNs used for image classification, the encoder network typically consists of alternating layers of convolution, subsampling (e.g. via max-pooling), non-linear rectification, etc.

The subsampling layers in CNNs allow the networks to extract high-level features that are translation invariant, which are crucial for image classification. However, they reduce the spatial extent of the feature map at each layer in the network. Let $I\in\mathbb{R}^{h\times w\times d}$ be the input image~(where $h$, $w$ are spatial dimensions $d$ is the color channel dimension) and $f(I)\in\mathbb{R}^{h'\times w'\times d'}$ be the feature map volume at the end of the encoder network. The feature map $f(I)$ has smaller spatial dimensions than the original image, i.e. $h'<h$ and $w'<w$. In order to produce a full-sized segmentation result as the output, an associated decoder network is applied to the output of the encoder network to produce the result that has spatial size of the original image. The fundamental differences between our work and various research contributions in prior work mainly lie in choices of the structure and composition of the decoder network. The decoder in SegNet~\cite{badrinarayanan15_arxiv} progressively enlarges the feature map using an upsampling technique without learnable parameters. The decoder in DeconvNet~\cite{noh15_iccv} is similar, but has learnable parameters. FCN~\cite{long15_cvpr} uses a single layer interpolation for deconvolution.

At the end of the decoder network, the full-sized feature map is mapped to predict the class label for each pixel, and a loss function is defined to compare the predicted labels with the ground-truth. It is important to note that the network makes the prediction only at the end of the decoder network. As a result, the supervision only happens at the last layer of the entire architecture, with which the loss function is associated.

\section{Label Refinement Network}\label{app}
In this section, we propose a novel network architecture called the \emph{Label Refinement Network~(LRN)} for dense image labeling problems. An overview of the LRN architecture is shown in Fig.~\ref{fig:our_archi}. Similar to prior work~\cite{badrinarayanan15_arxiv,long15_cvpr,noh15_iccv}, LRN also uses an encoder-decoder framework. The encoder network of LRN is similar to that of SegNet~\cite{badrinarayanan15_arxiv} which is based on the VGG16 network~\cite{simonyan15_iclr}. The novelty of LRN lies in the decoder network. Instead of making the prediction at the end of the entire architecture, the decoder network in LRN makes predictions in a coarse-to-fine fashion at several stages. In addition, LRN introduces supervisions early in the network by adding loss functions at multiple stages (not just at the last layer) in the decoder network.

The LRN architecture is motivated by the following observations: Due to the convolution and subsampling operations, the feature map $f(I)$ obtained at the end of the encoder network mainly contains high-level information about the image~(e.g. objects). Spatially precise information is lost in the encoder network, and therefore $f(I)$ cannot be used directly to recover a full-sized semantic segmentation which requires pixel-precise information. However, $f(I)$ contains enough information to produce a \emph{coarse} semantic segmentation. In particular, we can use $f(I)$ to produce a segmentation map $s(I)$ of spatial dimensions $h'\times w'$, which is smaller than the original image dimensions $h\times w$. Our decoder network then progressively refines the segmentation map $s(I)$. Let $s_k(I)$ be the $k$-th segmentation map (left to right in Fig.~\ref{fig:our_archi}). If $k>k'$, the segmentation map $s_k(I)$ will have a larger spatial dimensions than $s_{k'}(I)$. Note that one can interpret $s_k(I)$ as the feature map in the decoder network in most work~(e.g. \cite{badrinarayanan15_arxiv,long15_cvpr,noh15_iccv}). However, our model enforces the channel dimension of $s_k(I)$ to be the same as the number of class labels, so $s_k(I)$ can be considered as a (soft) label map.

The network architecture in Fig.~\ref{fig:our_archi} has 5 convolution layers in the encoder. We use $f_{k}(I)\in\mathbb{R}^{h_k\times w_k \times d_k}$ ($k=1,2,...,5$) to denote the feature map after the $k$-th convolution layer. The decoder network generates 6 label maps $s_{k}$ ($k=1,2,...,6$). After the last convolution layer of the encoder network, we use a $3\times 3$ convolution layer to convert the convolution feature map $f_5(I)\in\mathbb{R}^{h_5\times w_5\times d_5}$ to $s_{1}(I)\in\mathbb{R}^{h_5\times w_5\times C}$, where $C$ is the number of class labels. We then define a loss function on $s_{1}(I)$ as follows. Let $Y\in\mathbb{R}^{h\times w \times C}$ be the ground-truth segmentation map, where the label on each pixel is represented as a $C$-dimensional vector using the one-shot representation. We use $R_{1}(Y)\in\mathbb{R}^{h_5\times w_5\times C_5}$ to denote the segmentation map obtained by resizing $Y$ to have the same spatial dimensions as $s_{1}(I)$. We can then define a loss function $\ell_1$ (cross entropy loss is used) to measure the difference between the resized ground-truth $R_{1}(Y)$ and the coarse prediction $s_{1}(I)$ (after softmax). In other words, these operations can be written as:
\begin{eqnarray}
s_{1}(I)=\textrm{conv}_{3\times 3}\big(f_5(I)\big) \nonumber \\
\qquad \ell_{1}=\textrm{Loss}\Big(R_{1}(Y),\textrm{softmax}\big(s_{1}(I)\big)\Big)
\end{eqnarray}
where $\textrm{conv}_{3\times 3}(\cdot)$ denotes the $3\times 3$ convolution and $\textrm{Loss}(\cdot)$ denotes the cross entropy loss function.

Now we explain how to get the subsequent segmentation map $s_{k}(I)$ ($k>1$) with larger spatial dimensions. One simple solution is to upsample the previous segmentation map $s_{k-1}(I)$. However, this upsampled segmentation map will be very coarse. In order to derive a more precise segmentation, we use the idea of skip-connections \cite{hariharan15_cvpr,long15_cvpr}. The basic idea is to make use of the outputs from one of the convolutional feature layers in the encoder network. Since the convolutional feature layer contains more precise spatial information, we can combine it with the upsampled segmentation map to obtain a refined segmentation map~(see Fig~\ref{fig:refinement} for detailed illustration of our refinement module). In our model, we simply concatenate the outputs from the skip layer with the upsampled decoder layer to create a larger feature map, then use $3\times 3$ convolution to convert the channel dimension to $C$. For example, the label map $s_{2}(I)$ is obtained from upsampled $s_{1}(I)$ and the convolutional feature map $f_{5}(I)$ (see Fig.~\ref{fig:our_archi} for details on these skip connections). We can then define a loss function on this (larger) segmentation map by comparing $s_{k}(I)$ with the resized ground-truth segmentation map $R_{k}(Y)$ of the corresponding size. These operations can be summarized as follows:
\begin{eqnarray}
  s_{k}(I)=\textrm{conv}_{3\times 3}\Big(\textrm{concat}\Big(\textrm{upsample}\big(s_{k}(I)\big), f_{7-k}(I)\Big)\Big)\\
  \ell_{k}=\textrm{Loss}\Big(R_{k}(Y),\textrm{softmax}\big(s_{k}(I)\big)\Big), \ \ \textrm{where } k=2,..,6
\end{eqnarray}
Our model is trained using back-propagation to optimize the summation of all the losses $\sum_{k=1}^{6} \ell_{k}$.

Fig.~\ref{fig:skip_visualize} shows the advantage of progressively refinement of the segmentation map $s(I)$ in the decoder network. The finer feature maps from the convolution layers help to recover image details, and therefore the segmentation map produced at each stage of the decoder network becomes more accurate.

\begin{figure}
\center
 \includegraphics[width=2.5in, height=4.5in]{images/skip_visualize.pdf}
 \caption{Visualization of the refinement process in the network. Coarse segmentation map ($s_1$) produced by the encoder network $f_{enc}$ is fed to the decoder network (shown in dotted rectangular box). The decoder network then progressively refines the obtained segmentation map using the refinement module (RE) (see Fig.~\ref{fig:refinement}) to recover the image details step by step (see $s_1,s_2,s_3,...s_6$ in the figure). Basically, the refinement process is integrating the finer feature maps (activations shown as heat maps on the right) from the corresponding convolution layers with the segmentation maps. As a result, the segmentation map gets progressively refined throughout the decoder network.}
 \label{fig:skip_visualize}
\end{figure}

\section{Conclusion}\label{conclude}
We have proposed a novel CNN architecture for semantic segmentation. Our model produces segmentation labels in a coarse-to-fine manner. The segmentation labels at coarse levels are used to progressively refine the labeling produced at finer levels. We introduce multiple loss in the network to provide deep supervisions at multiple stages of the network. Our experimental results demonstrate that our proposed network improves the performance of several baseline approaches. Although we focus on semantic segmentation, the proposed architecture is quite general and can be applied for other pixel-wise labeling problems. In addition, experimental results based on ablation analysis suggest generality in the value of coarse-to-fine predictions, deep supervisions and skip connections respectively, with the implication that a wide range of CNNs may benefit from these architectural modifications.

\section{Experiments}\label{exp}
Section~\ref{sec:implementation} explains some implementation details and several baseline methods that we compare with. We then demonstrate experimental results on three challenging benchmark datasets: the PASCAL VOC 2012 dataset~(Sec.~\ref{sec:pascal}), the Cambridge Driving Labeled Video (CamVid) dataset~(Sec.~\ref{sec:camvid}), and the SUN RGB-D dataset~(Sec.~\ref{sec:sun}).
\begin{table*}[ht!]
\resizebox{\textwidth}{!}{%
    \centering
    \begin{tabular}{r|cccccccccccccccccccc|c}
        method&\multicolumn{1}{c}{\rotatebox{90}{aero}}&\multicolumn{1}{c}{\rotatebox{90}{bike}}&\multicolumn{1}{c}{\rotatebox{90}{bird}}&\multicolumn{1}{c}{\rotatebox{90}{boat}}&\multicolumn{1}{c}{\rotatebox{90}{bottle}}&\multicolumn{1}{c}{\rotatebox{90}{bus}}&\multicolumn{1}{c}{\rotatebox{90}{car}}&\multicolumn{1}{c}{\rotatebox{90}{cat}}&\multicolumn{1}{c}{\rotatebox{90}{chair}}&\multicolumn{1}{c}{\rotatebox{90}{cow}}&\multicolumn{1}{c}{\rotatebox{90}{table}}&\multicolumn{1}{c}{\rotatebox{90}{dog}}&\multicolumn{1}{c}{\rotatebox{90}{horse}}&\multicolumn{1}{c}{\rotatebox{90}{mbike}}&\multicolumn{1}{c}{\rotatebox{90}{person}}&\multicolumn{1}{c}{\rotatebox{90}{plant}}&\multicolumn{1}{c}{\rotatebox{90}{sheep}}&\multicolumn{1}{c}{\rotatebox{90}{sofa}}&\multicolumn{1}{c}{\rotatebox{90}{train}}&\rotatebox{90}{tv}&\rotatebox{90}{mean IoU}\\
       \hline
       \hline
       FCN-8s \cite{long15_cvpr}&76.8& 34.2& 68.9& 49.4& {\bf 60.3}& 75.3& 74.7& 77.6& 21.4 &62.5& 46.8& {\bf 71.8}& 63.9& 76.5& 73.9& 45.2& {\bf 72.4}& 37.4 &70.9& 55.1&62.2\\

       SegNet \cite{badrinarayanan15_arxiv}&74.5& 30.6& 61.4& {\bf 50.8}& 49.8& 76.2& 64.3&69.7& {\bf 23.8}& 60.8& {\bf 54.7}&62.0& 66.4& 70.2& 74.1& 37.5& 63.7& 40.6& 67.8& 53.0&59.1\\
       SegNet+ DS&70.0& 35.7& 72.8& 42.7& 53.0& 75.6& 71.6& 72.8& 23.2& {\bf 69.5}& 46.8& 64.6& 68.8& 78.9& 76.0& 41.9& 65.7& {\bf 44.1}& 65.7& 53.7& 61.1\Tstrut\\
       \textbf{LRN}&{\bf 79.3}&{\bf 37.5}&{\bf 79.7}&47.7&58.3&{\bf 76.5}&{\bf 76.1}&{\bf 78.5}&21.9&67.7 &47.6&71.2&{\bf 69.1}&{\bf 82.1}&{\bf 77.5}&{\bf 46.8} &70.1&40.3 &{\bf 71.5}&{\bf 57.4}&{\bf 64.2}  \Tstrut\\
       \hline
    \end{tabular}}
\caption{Quantitative results on the PASCAL VOC 2012 test set. We report the IoU score for each class and the mean IoU score.}
  \label{tab:quant_pascal}

\end{table*}
\begin{figure*}[ht!]
    \begin{center}
    \setlength\tabcolsep{0.7pt}
    \def2.5{0.5}
    \resizebox{\textwidth}{!}{
 \begin{tabular}{*{4}{c }}
    \includegraphics[width=0.2\textwidth]{images/pascal/original/2007_003349.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2007_003349.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/fcn/2007_003349.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/pascal/val/2007_003349.png}} \\
   \includegraphics[width=0.2\textwidth]{images/pascal/original/2007_000762.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2007_000762.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/fcn/2007_000762.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/pascal/val/2007_000762.png}} \\
   \includegraphics[width=0.2\textwidth]{images/pascal/original/2007_001284.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2007_001284.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/fcn/2007_001284.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/pascal/val/2007_001284.png}} \\
  \includegraphics[width=0.2\textwidth]{images/pascal/original/2008_002536.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2008_002536.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/fcn/2008_002536.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/pascal/val/2008_002536.png}} \\
  \includegraphics[width=0.2\textwidth]{images/pascal/original/2007_001630.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2007_001630.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/fcn/2007_001630.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/pascal/val/2007_001630.png}} \\
                  image & ground-truth & FCN-8s \cite{long15_cvpr} & LRN \Tstrut\\
  \end{tabular}
  \begin{tabular}{*{4}{c }}
   \includegraphics[width=0.2\textwidth]{images/pascal/original/2007_007235.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2007_007235.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/fcn/2007_007235.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/pascal/val/2007_007235.png}} \\
  \includegraphics[width=0.2\textwidth]{images/pascal/original/2007_002565.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2007_002565.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/fcn/2007_002565.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/pascal/val/2007_002565.png}} \\
   \includegraphics[width=0.2\textwidth]{images/pascal/original/2008_003141.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2008_003141.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/fcn/2008_003141.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/pascal/val/2008_003141.png}} \\
   \includegraphics[width=0.2\textwidth]{images/pascal/original/2007_006946.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2007_006946.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/fcn/2007_006946.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/pascal/val/2007_006946.png}} \\
   \includegraphics[width=0.2\textwidth]{images/pascal/original/2007_005608.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2007_005608.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/fcn/2007_005608.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/pascal/val/2007_005608.png}} \\
                  image & ground-truth & FCN-8s \cite{long15_cvpr} & LRN \Tstrut\\
 \end{tabular}}
    \caption{Qualitative comparisons between the FCN\cite{long15_cvpr} model and our LRN model on the PASCAL VOC 2012 dataset.}
      \label{fig:val_pascal}
    \end{center}
 \end{figure*}

\subsection{Implementation Details and Baselines}\label{sec:implementation}
Following \cite{badrinarayanan15_arxiv}, we train our network on the CamVid and SUN RGB-D datasets using the class balancing technique in \cite{eigen15_iccv} as there is a large variation in the number of pixels for each category. For example, most of the pixels on the CamVid dataset are labeled as roads and cars. Each class is assigned a weight in the loss function. The weights are calculated by taking the ratio of the median of the class frequencies to the individual class frequencies from the training dataset. For the CamVid and SUN RGB-D datasets, training images are resized to a common size of $360\times 480$ pixels. For the PASCAL VOC 2012 dataset, when fed into LRN, we randomly crop a $320\times 320$ region from each image during training and subtracted a mean pixel provided by VGG net at each position. During test, we generate a $512 \times 512$ segmentation map and crop the final label out from it similar to \cite{chen15_iclr}.

We implemented LRN using Caffe \cite{jia14_caffe}. A GTX Titan X GPU was used both in training and testing for acceleration. The whole network was trained end-to-end by using back propagation algorithm with the following hyper-parameters: mini-batch size (10), learning rate (0.001), momentum (0.9), weight decay (0.0005), number of iterations ($\sim80,000$). After each $50,000$ iterations we reduce the learning rate by a factor of $0.1$. The encoder network is fine-tuned from a pre-trained VGG-16 network.

To demonstrate the merit of individual component of our model, we perform an ablation study by comparing with the following baselines.

{\bf SegNet}: This is the SegNet model in \cite{badrinarayanan15_arxiv}. Whenever possible, we use the reported numbers in \cite{badrinarayanan15_arxiv} in our comparison.

{\bf SegNet+Deep Supervision~(SegNet+DS)}: This is the SegNet model with multiple loss functions to provide deep supervisions.
 \begin{figure*}[ht!]
    \begin{center}
    \setlength\tabcolsep{0.7pt}
    \def2.5{0.5}
    \resizebox{\textwidth}{!}{
 \begin{tabular}{*{8}{c }}
    \includegraphics[width=0.2\textwidth]{images/pascal/original/2008_003546.png}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2008_003546.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/stage/1/2008_003546.png}
  &
  \includegraphics[width=0.2\textwidth]{images/pascal/stage/2/2008_003546.png}
  &
    \includegraphics[width=0.2\textwidth]{images/pascal/stage/3/2008_003546.png}
  &
   \includegraphics[width=0.2\textwidth]{images/pascal/stage/4/2008_003546.png}
  &
   \includegraphics[width=0.2\textwidth]{images/pascal/stage/5/2008_003546.png}
  &
 \includegraphics[width=0.2\textwidth]{images/pascal/stage/6/2008_003546.png} \\
   \includegraphics[width=0.2\textwidth]{images/pascal/original/2007_004902.png}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2007_004902.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/stage/1/2007_004902.png}
  &
  \includegraphics[width=0.2\textwidth]{images/pascal/stage/2/2007_004902.png}
  &
    \includegraphics[width=0.2\textwidth]{images/pascal/stage/3/2007_004902.png}
  &
   \includegraphics[width=0.2\textwidth]{images/pascal/stage/4/2007_004902.png}
  &
   \includegraphics[width=0.2\textwidth]{images/pascal/stage/5/2007_004902.png}
  &
 \includegraphics[width=0.2\textwidth]{images/pascal/stage/6/2007_004902.png}\\
   \includegraphics[width=0.2\textwidth]{images/pascal/original/2007_000925.png}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2007_000925.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/stage/1/2007_000925.png}
  &
  \includegraphics[width=0.2\textwidth]{images/pascal/stage/2/2007_000925.png}
  &
    \includegraphics[width=0.2\textwidth]{images/pascal/stage/3/2007_000925.png}
  &
   \includegraphics[width=0.2\textwidth]{images/pascal/stage/4/2007_000925.png}
  &
   \includegraphics[width=0.2\textwidth]{images/pascal/stage/5/2007_000925.png}
  &
   \includegraphics[width=0.2\textwidth]{images/pascal/stage/6/2007_000925.png}\\
   \includegraphics[width=0.2\textwidth]{images/pascal/original/2008_001231.png}
   &
   \includegraphics[width=0.2\textwidth]{images/pascal/gt/2008_001231.png}
   &
  \includegraphics[width=0.2\textwidth]{images/pascal/stage/1/2008_001231.png}
  &
  \includegraphics[width=0.2\textwidth]{images/pascal/stage/2/2008_001231.png}
  &
    \includegraphics[width=0.2\textwidth]{images/pascal/stage/3/2008_001231.png}
  &
   \includegraphics[width=0.2\textwidth]{images/pascal/stage/4/2008_001231.png}
  &
   \includegraphics[width=0.2\textwidth]{images/pascal/stage/5/2008_001231.png}
  &
   \includegraphics[width=0.2\textwidth]{images/pascal/stage/6/2008_001231.png}\\
   image & ground-truth & $s_1$ & $s_2$ & $s_3$ & $s_4$ & $s_5$ &$s_6$\Tstrut\\
 \end{tabular}}
    \caption{Stage-wise visualization of semantic segmentation results on PASCAL VOC 2012. We show the segmentation result obtained from each of the 6 label maps $s_k$ ($k=1,2,...,6$) produced by the decoder of our model.}
    \label{fig:stage_pascal}
 \end{center}
 \end{figure*}
\begin{table*}[ht!]
\resizebox{\textwidth}{!}{%
    \centering
    \begin{tabular}{l|c|c|c|c|c|c|c|c|c|c|c|c|c}
        Method&\multicolumn{1}{c}{\rotatebox{90}{Building}}&\multicolumn{1}{c}{\rotatebox{90}{Tree}}&\multicolumn{1}{c}{\rotatebox{90}{Sky}}&\multicolumn{1}{c}{\rotatebox{90}{Car}}&\multicolumn{1}{c}{\rotatebox{90}{Sign}}&\multicolumn{1}{c}{\rotatebox{90}{Road}}&\multicolumn{1}{c}{\rotatebox{90}{Pedestrian}}&\multicolumn{1}{c}{\rotatebox{90}{Fence}}&\multicolumn{1}{c}{\rotatebox{90}{Pole}}&\multicolumn{1}{c}{\rotatebox{90}{Sidewalk}}&\multicolumn{1}{c|}{\rotatebox{90}{Bicyclist}} & \rotatebox{90}{Class avg} & \rotatebox{90}{Mean IoU}\\
       \hline
       \hline
       \cite{zhang10_eccv}&85.3&57.3&95.4&69.2&46.5&{\bf 98.5}&23.8&44.3&22.0&38.1&28.7&55.4&-\\
       \hline
       \cite{tighe13_ijcv}&87.0&67.1&{\bf 96.9}&62.7&30.1&95.9&14.7&17.9&1.7&70.0&19.4&51.2&-\\
       \hline
       \cite{ladicky10_eccv}&81.5&76.6&96.2&78.7&40.2&93.9&43.0&47.6&14.3&81.5&33.9&62.5&-\\
       \hline
       \hline
       SegNet~\cite{badrinarayanan15_arxiv}&88.0&87.3&92.3&80.0&29.5&97.6&57.2&49.4&27.8&84.8&30.7&65.9&50.2\\
       SegNet+DS&87.0&85.1&77.1&79.8& 56.3&94.8&71.1&50.5&39.7&88.6&50.6& 70.1&53.7 \\
        \textbf{LRN}&{\bf 89.8}&{\bf 88.1}&78.5&{\bf 86.3}&{\bf 61.2}&96.8&{\bf 82.1}&{\bf 59.0}&{\bf 45.4}&{\bf 92.6}&{\bf 69.7}&\textbf{77.2}&\textbf{61.7}  \\
       \hline
    \end{tabular}}
\caption{Quantitative results on the CamVid~\cite{brostow09_prl} dataset. For each method, we report the accuracy for each class and the average class accuracy. We also report the mean IoU of all classes if it is available. Our model (LRN) outperforms all the other methods. }
  \label{table:quant_camvid}
\end{table*}
\subsection{PASCAL VOC 2012}\label{sec:pascal}
PASCAL VOC 2012 is a challenging segmentation benchmark~\cite{everingham10_ijcv}. It consists of 20 object categories and the background. Following previous works such as ~\cite{chen15_iclr, long15_cvpr, badrinarayanan15_arxiv}, we employ 10,582 images for training, 1,449 images for validation, and 1,456 images for testing. We train our model on the trainval set and evaluate our model on the test set. Quantitative results are shown in Table \ref{tab:quant_pascal}. We can see that SegNet+DS performs better than SegNet. This shows that deep supervisions (i.e. multiple losses) help boosting the performance. In addition, Table \ref{tab:quant_pascal} shows that our model (LRN) outperforms SegNet+DS. This demonstrates that the refinement modules give additional performance improvement. Note that we also perform better than the popular FCN~\cite{long15_cvpr} model for semantic segmentation. We present qualitative results in Fig.~\ref{fig:val_pascal}. The results show that LRN can preserve subtle details of objects. To further illustrate the effectiveness of the coarse-to-fine segmentation in our model, Fig.~\ref{fig:stage_pascal} shows each of the label maps produced by the refinement network in our model. We can see that the coarse-to-fine refinement scheme can progressively improve the details of segmentation maps by recovering the missing parts (e.g., the leg of the person and sheep in the top and $3^{rd}$ row respectively).

\subsection{CamVid}\label{sec:camvid}
The CamVid dataset~\cite{brostow09_prl} consists of a ten minutes video footage with 701 images. The video footage was recorded from driving around various urban settings and is particularly challenging given variable lighting settings (e.g. dusk, dawn). The dataset provides ground-truth labels that associate each pixel with one of 32 semantic class. Following \cite{badrinarayanan15_arxiv}, we consider 11 larger semantic classes (road, building, sky, tree, sidewalk, car, column-pole, fence, pedestrian, bicyclist, and sign-symbol) among the 32 semantic classes in our experiment. Table~\ref{table:quant_camvid} shows the comparison of our model with other state-of-the-art approaches on this dataset. For each method, we report the accuracy for each class and the average class accuracy. We also report the mean IoU score if it is available. Again, our LRN model outperforms SegNet+DS, which in turn outperforms SegNet. We show some qualitative results on this dataset in Fig.~\ref{fig:eval_camvid}.
\begin{figure*}
    \begin{center}
    \setlength\tabcolsep{1pt}
      \def2.5{0.5}
    \resizebox{\textwidth}{!}{
 \begin{tabular}{*{4}{c }}
    \includegraphics[width=0.2\textwidth]{images/camvid/original/0_0.png}
   &
   \includegraphics[width=0.2\textwidth]{images/camvid/gt/0_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/camvid/segnet/0_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/camvid/skip/0_segnet.png}} \\
  \includegraphics[width=0.2\textwidth]{images/camvid/original/0_36.png}
   &
   \includegraphics[width=0.2\textwidth]{images/camvid/gt/36_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/camvid/segnet/36_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/camvid/skip/36_segnet.png}}\\
  \includegraphics[width=0.2\textwidth]{images/camvid/original/0_215.png}
   &
   \includegraphics[width=0.2\textwidth]{images/camvid/gt/215_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/camvid/segnet/215_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/camvid/skip/215_segnet.png}}\\
  \includegraphics[width=0.2\textwidth]{images/camvid/original/0_92.png}
   &
   \includegraphics[width=0.2\textwidth]{images/camvid/gt/92_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/camvid/segnet/92_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/camvid/skip/92_segnet.png}}\\
                  image & ground-truth & SegNet \cite{badrinarayanan15_arxiv} & LRN \Tstrut\\
  \end{tabular}
  \begin{tabular}{*{4}{c }}
   \includegraphics[width=0.2\textwidth]{images/camvid/original/0_28.png}
   &
   \includegraphics[width=0.2\textwidth]{images/camvid/gt/28_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/camvid/segnet/28_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/camvid/skip/28_segnet.png}}\\
  \includegraphics[width=0.2\textwidth]{images/camvid/original/0_92.png}
   &
   \includegraphics[width=0.2\textwidth]{images/camvid/gt/92_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/camvid/segnet/92_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/camvid/skip/92_segnet.png}}\\
  \includegraphics[width=0.2\textwidth]{images/camvid/original/0_232.png}
   &
   \includegraphics[width=0.2\textwidth]{images/camvid/gt/232_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/camvid/segnet/232_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/camvid/skip/232_segnet.png}}\\
   \includegraphics[width=0.2\textwidth]{images/camvid/original/0_1.png}
   &
   \includegraphics[width=0.2\textwidth]{images/camvid/gt/1_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/camvid/segnet/1_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/camvid/fusion/0_1.png}}\\
                  image & ground-truth & SegNet \cite{badrinarayanan15_arxiv} & LRN \Tstrut\\
 \end{tabular}}
    \caption{Qualitative results on the CamVid dataset. LRN predictions capable of retaining the shape of smaller object categories (e.g. column-pole, side-walk, bicyclist, and sign-symbols) accurately compared to SegNet \cite{badrinarayanan15_arxiv}. }
    \label{fig:eval_camvid}
    \end{center}
 \end{figure*}

 \subsection{SUN RGB-D}\label{sec:sun}
 We also evaluate our model on the SUN RGB-D dataset~\cite{song15_cvpr} which contains 5,285 training and 5,050 test images. The images consist of indoor scenes of varying resolution and aspect ratio. There are 37 indoor scene classes with corresponding segmentation labels (background is not considered as a class and is ignored during training and testing). The segmentation labels are instance-wise, i.e. multiple instances of same class in an image have different labels. We convert the ground-truth labels into class-wise segmentation labels so that all instances of the same class have the same corresponding label. Although the dataset also contains depth information, we only use the RGB images to train and test our model. Quantitative results on this dataset are shown in Table~\ref{tab:quant_cat_sun}. Our LRN model achieves better mean IoU than other baselines on this dataset. We show some qualitative results in Fig.~\ref{fig:sun_example}.

 \begin{figure*}[ht!]
    \begin{center}
    \setlength\tabcolsep{1pt}
      \def2.5{0.5}
    \resizebox{\textwidth}{!}{
  \begin{tabular}{cccc}
   \includegraphics[width=0.2\textwidth]{images/sun/original/26_segnet.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/sun/gt/26_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/sun/segnet/26_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/sun/pred/26_segnet.png}} \\
  \includegraphics[width=0.2\textwidth]{images/sun/original/91_segnet.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/sun/gt/91_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/sun/segnet/91_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/sun/pred/91_segnet.png}} \\
  \includegraphics[width=0.2\textwidth]{images/sun/original/75_segnet.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/sun/gt/75_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/sun/segnet/75_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/sun/pred/75_segnet.png}} \\
   image & ground-truth & SegNet \cite{badrinarayanan15_arxiv} & LRN \Tstrut\\
   \end{tabular}
   \begin{tabular}{cccc}
   \includegraphics[width=0.2\textwidth]{images/sun/original/1313_segnet.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/sun/gt/1313_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/sun/segnet/1313_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/sun/pred/1313_segnet.png}} \\
  \includegraphics[width=0.2\textwidth]{images/sun/original/28_segnet.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/sun/gt/28_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/sun/segnet/28_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/sun/pred/28_segnet.png}} \\
  \includegraphics[width=0.2\textwidth]{images/sun/original/270_segnet.jpg}
   &
   \includegraphics[width=0.2\textwidth]{images/sun/gt/270_segnet.png}
   &
  \includegraphics[width=0.2\textwidth]{images/sun/segnet/270_segnet.png}
  &
  {\includegraphics[width=0.2\textwidth]{images/sun/pred/270_segnet.png}} \\
   image & ground-truth & SegNet \cite{badrinarayanan15_arxiv} & LRN \Tstrut\\
   \end{tabular}}
    \caption{Qualitative results on the SUN RGB-D dataset.}
    \label{fig:sun_example}
    \end{center}
 \end{figure*}

 \begin{table*}[ht!]
\resizebox{\textwidth}{!}{%
    \centering
  \def2.5{2.5}
    \begin{tabular}{r|ccccccccccccccccccccccccccccccccccccc|c|c}
     Method&\multicolumn{1}{c}{\rotatebox{90}{Wall}}&\multicolumn{1}{c}{\rotatebox{90}{Floor}}&\multicolumn{1}{c}{\rotatebox{90}{Cabinet}}&\multicolumn{1}{c}{\rotatebox{90}{Bed}}&\multicolumn{1}{c}{\rotatebox{90}{Chair}}&\multicolumn{1}{c}{\rotatebox{90}{Sofa}}&\multicolumn{1}{c}{\rotatebox{90}{Table}}&\multicolumn{1}{c}{\rotatebox{90}{Door}}&\multicolumn{1}{c}{\rotatebox{90}{Window}}&\multicolumn{1}{c}{\rotatebox{90}{Bookshelf}}&\multicolumn{1}{c}{\rotatebox{90}{Picture}}&\multicolumn{1}{c}{\rotatebox{90}{Counter}}&\multicolumn{1}{c}{\rotatebox{90}{Blinds}}&\multicolumn{1}{c}{\rotatebox{90}{Desk}}&\multicolumn{1}{c}{\rotatebox{90}{Shelves}}&\multicolumn{1}{c}{\rotatebox{90}{Curtain}}&\multicolumn{1}{c}{\rotatebox{90}{Dresser}}&\multicolumn{1}{c}{\rotatebox{90}{Pillow}}&\multicolumn{1}{c}{\rotatebox{90}{Mirror}}&\multicolumn{1}{c}{\rotatebox{90}{Floor Mat}} &\multicolumn{1}{c}{\rotatebox{90}{Clothes}}&\multicolumn{1}{c}{\rotatebox{90}{Ceiling}}&\multicolumn{1}{c}{\rotatebox{90}{Books}}&\multicolumn{1}{c}{\rotatebox{90}{Fridge}}&\multicolumn{1}{c}{\rotatebox{90}{TV}}&\multicolumn{1}{c}{\rotatebox{90}{Paper}}&\multicolumn{1}{c}{\rotatebox{90}{Towel}}&\multicolumn{1}{c}{\rotatebox{90}{Curtain}}&\multicolumn{1}{c}{\rotatebox{90}{Box}}&\multicolumn{1}{c}{\rotatebox{90}{Whiteboard}}&\multicolumn{1}{c}{\rotatebox{90}{Person}}&\multicolumn{1}{c}{\rotatebox{90}{Night Stand}}&\multicolumn{1}{c}{\rotatebox{90}{Toilet}}&\multicolumn{1}{c}{\rotatebox{90}{Sink}}&\multicolumn{1}{c}{\rotatebox{90}{Lamp}}&\multicolumn{1}{c}{\rotatebox{90}{Bathtub}}&\multicolumn{1}{c|}{\rotatebox{90}{Bag}}& \rotatebox{90}{Class avg}&\rotatebox{90}{mean IoU}\\
       \hline
  \cite{kendall15_arxiv} &\multicolumn{1}{c}{\rotatebox{90}{80.2}}&\multicolumn{1}{c}{\rotatebox{90}{90.9}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 58.9}}} &\multicolumn{1}{c}{\rotatebox{90}{64.8}} &\multicolumn{1}{c}{\rotatebox{90}{76.0}}&\multicolumn{1}{c}{\rotatebox{90}{58.6}}&\multicolumn{1}{c}{\rotatebox{90}{62.6}}&\multicolumn{1}{c}{\rotatebox{90}{47.7}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 66.4}}}&\multicolumn{1}{c}{\rotatebox{90}{31.2}}&\multicolumn{1}{c}{\rotatebox{90}{63.6}}&\multicolumn{1}{c}{\rotatebox{90}{33.8}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 46.7}}}&\multicolumn{1}{c}{\rotatebox{90}{19.7}}&\multicolumn{1}{c}{\rotatebox{90}{16.2}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 67.0}}}&\multicolumn{1}{c}{\rotatebox{90}{42.3}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 57.1}}}&\multicolumn{1}{c}{\rotatebox{90}{39.1}}&\multicolumn{1}{c}{\rotatebox{90}{0.1}}&\multicolumn{1}{c}{\rotatebox{90}{24.4}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 84.0}}}&\multicolumn{1}{c}{\rotatebox{90}{48.7}}&\multicolumn{1}{c}{\rotatebox{90}{21.3}}&\multicolumn{1}{c}{\rotatebox{90}{49.5}}&\multicolumn{1}{c}{\rotatebox{90}{30.6}}&\multicolumn{1}{c}{\rotatebox{90}{18.8}}&\multicolumn{1}{c}{\rotatebox{90}{0.1}}&\multicolumn{1}{c}{\rotatebox{90}{24.1}}&\multicolumn{1}{c}{\rotatebox{90}{56.8}}&\multicolumn{1}{c}{\rotatebox{90}{17.9}}&\multicolumn{1}{c}{\rotatebox{90}{42.9}}&\multicolumn{1}{c}{\rotatebox{90}{73.0}}&\multicolumn{1}{c}{\rotatebox{90}{66.2}}&\multicolumn{1}{c}{\rotatebox{90}{48.8}}&\multicolumn{1}{c}{\rotatebox{90}{45.1}}&\multicolumn{1}{c|}{\rotatebox{90}{24.1}}&{\rotatebox{90}{45.9}}&\multicolumn{1}{c}{\rotatebox{90}{30.7}}\\
       \hline

      SegNet~\cite{badrinarayanan15_arxiv}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 86.6|}}}&\multicolumn{1}{c}{\rotatebox{90}{92.0}}&\multicolumn{1}{c}{\rotatebox{90}{52.4}} &\multicolumn{1}{c}{\rotatebox{90}{{\bf 68.4}}} &\multicolumn{1}{c}{\rotatebox{90}{76.0}}&\multicolumn{1}{c}{\rotatebox{90}{54.3}}&\multicolumn{1}{c}{\rotatebox{90}{59.3}}&\multicolumn{1}{c}{\rotatebox{90}{37.4}}&\multicolumn{1}{c}{\rotatebox{90}{53.8}}&\multicolumn{1}{c}{\rotatebox{90}{29.2}}&\multicolumn{1}{c}{\rotatebox{90}{49.7}}&\multicolumn{1}{c}{\rotatebox{90}{32.5}}&\multicolumn{1}{c}{\rotatebox{90}{31.2}}&\multicolumn{1}{c}{\rotatebox{90}{17.8}}&\multicolumn{1}{c}{\rotatebox{90}{5.3}}&\multicolumn{1}{c}{\rotatebox{90}{53.2}}&\multicolumn{1}{c}{\rotatebox{90}{28.8}}&\multicolumn{1}{c}{\rotatebox{90}{36.5}}&\multicolumn{1}{c}{\rotatebox{90}{29.6}}&\multicolumn{1}{c}{\rotatebox{90}{0.0}}&\multicolumn{1}{c}{\rotatebox{90}{14.4}}&\multicolumn{1}{c}{\rotatebox{90}{67.7}}&\multicolumn{1}{c}{\rotatebox{90}{32.4}}&\multicolumn{1}{c}{\rotatebox{90}{10.2}}&\multicolumn{1}{c}{\rotatebox{90}{18.3}}&\multicolumn{1}{c}{\rotatebox{90}{19.2}}&\multicolumn{1}{c}{\rotatebox{90}{11.5}}&\multicolumn{1}{c}{\rotatebox{90}{0.0}}&\multicolumn{1}{c}{\rotatebox{90}{8.9}}&\multicolumn{1}{c}{\rotatebox{90}{38.7}}&\multicolumn{1}{c}{\rotatebox{90}{4.9}}&\multicolumn{1}{c}{\rotatebox{90}{22.6}}&\multicolumn{1}{c}{\rotatebox{90}{55.6}}&\multicolumn{1}{c}{\rotatebox{90}{52.7}}&\multicolumn{1}{c}{\rotatebox{90}{27.9}}&\multicolumn{1}{c}{\rotatebox{90}{29.9}}&\multicolumn{1}{c|}{\rotatebox{90}{8.1}}&{\rotatebox{90}{35.6}}&\multicolumn{1}{c}{\rotatebox{90}{26.3}}\\

      SegNet+DS& \multicolumn{1}{c}{\rotatebox{90}{78.9}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 92.4}}}&\multicolumn{1}{c}{\rotatebox{90}{52.5}}& \multicolumn{1}{c}{\rotatebox{90}{58.1}}& \multicolumn{1}{c}{\rotatebox{90}{74.1}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 61.9}}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 62.6}}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 64.1}}}& \multicolumn{1}{c}{\rotatebox{90}{60.6}}& \multicolumn{1}{c}{\rotatebox{90}{12.1}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 70.7}}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 38.7}}}& \multicolumn{1}{c}{\rotatebox{90}{37.5}}& \multicolumn{1}{c}{\rotatebox{90}{13.1}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 21.7}}}& \multicolumn{1}{c}{\rotatebox{90}{62.3}}& \multicolumn{1}{c}{\rotatebox{90}{41.2}}& \multicolumn{1}{c}{\rotatebox{90}{55.1}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 40.1}}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 25.0}}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 42.4}}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 84.0}}}& \multicolumn{1}{c}{\rotatebox{90}{58.1}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 44.6}}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 51.4}}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 44.6}}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 27.6}}}&\multicolumn{1}{c}{\rotatebox{90}{4.9}}&\multicolumn{1}{c}{\rotatebox{90}{24.8}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 60.9}}}&\multicolumn{1}{c}{\rotatebox{90}{14.4}}& \multicolumn{1}{c}{\rotatebox{90}{41.1}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 77.3}}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 68.7}}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 60.3}}}& \multicolumn{1}{c}{\rotatebox{90}{63.4}}& \multicolumn{1}{c|}{\rotatebox{90}{{\bf 28.6}}}& {\rotatebox{90}{\textbf{49.2}}}&\multicolumn{1}{c}{\rotatebox{90}{31.2}}\\

       \textbf{LRN}& \multicolumn{1}{c}{\rotatebox{90}{84.6}}&\multicolumn{1}{c}{\rotatebox{90}{90.8}}&\multicolumn{1}{c}{\rotatebox{90}{55.6}}& \multicolumn{1}{c}{\rotatebox{90}{65.2}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 79.4}}}&\multicolumn{1}{c}{\rotatebox{90}{46.2}}&\multicolumn{1}{c}{\rotatebox{90}{59.8}}&\multicolumn{1}{c}{\rotatebox{90}{44.6}}&\multicolumn{1}{c}{\rotatebox{90}{63.6}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 32.6}}}& \multicolumn{1}{c}{\rotatebox{90}{66.1}}& \multicolumn{1}{c}{\rotatebox{90}{37.7}}&\multicolumn{1}{c}{\rotatebox{90}{39.8}}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 21.0}}}& \multicolumn{1}{c}{\rotatebox{90}{15.7}}& \multicolumn{1}{c}{\rotatebox{90}{60.2}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 42.9}}}& \multicolumn{1}{c}{\rotatebox{90}{46.5}}& \multicolumn{1}{c}{\rotatebox{90}{30.4}}& \multicolumn{1}{c}{\rotatebox{90}{20.5}}& \multicolumn{1}{c}{\rotatebox{90}{23.0}}& \multicolumn{1}{c}{\rotatebox{90}{82.8}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 59.9}}}& \multicolumn{1}{c}{\rotatebox{90}{26.4}}& \multicolumn{1}{c}{\rotatebox{90}{44.4}}& \multicolumn{1}{c}{\rotatebox{90}{32.5}}& \multicolumn{1}{c}{\rotatebox{90}{23.4}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 10.8}}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 30.9}}}& \multicolumn{1}{c}{\rotatebox{90}{51.6}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 21.7}}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 43.0}}}& \multicolumn{1}{c}{\rotatebox{90}{75.7}}& \multicolumn{1}{c}{\rotatebox{90}{61.8}}& \multicolumn{1}{c}{\rotatebox{90}{51.3}}& \multicolumn{1}{c}{\rotatebox{90}{{\bf 63.9}}}& \multicolumn{1}{c|}{\rotatebox{90}{28.4}}&\rotatebox{90}{46.8}&\multicolumn{1}{c}{\rotatebox{90}{{\bf 33.1}}} \\
       \hline
    \end{tabular}}
\caption{Quantitative results on the SUN RGB-D dataset. We report the accuracy for each class, average class accuracy, and the mean IoU of all classes.}
  \label{tab:quant_cat_sun}
\end{table*}

\subsection{Ablation Analysis}
We perform further ablation analysis to demonstrate the benefit of our coarse-to-fine approach. Our LRN model produces 6 label maps $s_k$ ($k=1,2,...,6$) of different spatial dimensions (see Fig.~\ref{fig:our_archi}). It is possible to obtain the final full-sized label map from any of these $s_k$ by upsamling $s_k$~(using bilinear interpolation) to the spatial dimensions of the input image. Table~\ref{table:stages_pascal} shows the results of this stage-wise analysis on the three datasets. Note that on the PASCAL 2012 dataset, we perform this analysis by learning the model on the training set, and test on the validation set. The reason is that we do not have direct access to the ground-truth labels on the PASCAL 2012 test set. The evaluation on the test set has to be done on the PASCAL evaluation server. The results in Table~\ref{table:stages_pascal} show that if we simply take a label map at one of the early stages of the refinement network and upsample it as the final prediction, the result is not as good as using the label map produced at the last stage. This is reasonable since the label map at an early stage only provides labels corresponding to smaller spatial dimensions.

\begin{table*}[ht!]
\scriptsize
\begin{center}
\resizebox{0.85\textwidth}{!}{
\begin{tabular}{r|c}
 Stage & Mean IoU (\%)\\
\hline\hline
$s_1$ &58.4\\
$s_2$ &61.6\\
$s_3$ &61.9\\
$s_4$ &62.6\\
$s_5$ &62.5\\
$s_6$ &\textbf{62.8}\\
\hline
\multicolumn{2}{c}{(a) PASCAL VOC 2012} \Tstrut \\
\end{tabular}
\hspace{1cm}
\begin{tabular}{r|c}
 Stage & Mean IoU (\%)\\
\hline\hline
$s_1$ &50.9\\
$s_2$ &55.5\\
$s_3$ &59.1\\
$s_4$ &60.9\\
$s_5$ &61.4\\
$s_6$ &\textbf{61.7}\\
\hline
\multicolumn{2}{c}{(b) CamVid} \Tstrut \\
\end{tabular}
\hspace{1cm}
\begin{tabular}{r|c}
 Stage & Mean IoU (\%)\\
\hline\hline
$s_1$ &31.0\\
$s_2$ &31.6\\
$s_3$ &32.4\\
$s_4$ &32.7\\
$s_5$ &32.8\\
$s_6$ &\textbf{33.1}\\
\hline
\multicolumn{2}{c}{(c) SUN RGB-D}\Tstrut \\
\end{tabular}}
\caption{Stage-wise mean IoU on (a) PASCAL VOC 2012 validation set; (b) CamVid dataset; (c) SUN RGB-D dataset. We can see that, from $s_1$ to $s_6$, mean IoU is progressively enhanced in all the dataset. Note that $s_1..s_6$ are not results of separate training. They are obtained from different stage of the decoder in our model. The result from $s_1$ is implicitly affected by the supervisions provided at $s_2..s_6$, so in Camvid and SUN-RGBD dataset it is reasonable for $s_1$ to produce better performance than SegNet \cite{badrinarayanan15_arxiv}.}
\label{table:stages_pascal}
\end{center}
\end{table*}

\subsection{Discussion}
\begin{figure*}[ht!]
\setlength\tabcolsep{1pt}
\resizebox{\textwidth}{!}{
 \begin{tabular}{ccc}
   \includegraphics[width=0.2\textwidth,height=3cm]{images/camvid/discussion/37_original.png}
   &
  \includegraphics[width=0.2\textwidth,height=3cm]{images/camvid/discussion/37_gt.png}
  &
  {\includegraphics[width=0.2\textwidth,height=3cm]{images/camvid/discussion/36_our.png}}\\
  \multicolumn{3}{c}{(a)}\\
  \end{tabular}
  \hspace{0.1cm}

  \begin{tabular}{ccc}
  \includegraphics[width=0.2\textwidth,height=3cm]{images/pascal/original/2007_000862.png}
   &
  \includegraphics[width=0.2\textwidth,height=3cm]{images/pascal/gt/2007_000862.png}
  &
  {\includegraphics[width=0.2\textwidth,height=3cm]{images/pascal/stage/6/2007_000862.png}}\\
  \multicolumn{3}{c}{(b)}\\
   \end{tabular}
   \hspace{0.1cm}

   \begin{tabular}{ccc}
   \includegraphics[width=0.2\textwidth,height=3cm]{images/pascal/large/image/2007_000491.jpg}
   &
  \includegraphics[width=0.2\textwidth,height=3cm]{images/pascal/large/gt/2007_000491.png}
  &
  {\includegraphics[width=0.2\textwidth,height=3cm]{images/pascal/large/pred/2007_000491.png}}\\
  \multicolumn{3}{c}{(c)}\\
 \end{tabular}}
\caption{Qualitative analysis of dense predictions on a few problematic images of CamVid \cite{brostow09_prl} and Pascal VOC 2012 \cite{everingham10_ijcv} datasets. Our predictions are more refined and sensitive to smaller and thinner objects (see (a) and (b)). Performance is also better for larger objects (see (c)) because of the refinement modules and guidance driven by early constraints provided by deep supervision.}
  \label{fig:quanlity_analysis}
\end{figure*}

The main focus of our paper is the coarse-to-fine label refinement network architecture. Smaller objects are hard to classify due to the limitation of having a fixed size kernel window. For instance, the poles and sign-symbols in the CamVid dataset \cite{brostow09_prl} often carry significant importance and are important to scene understanding for many applications. This is where our model performs especially well considering the baseline \cite{badrinarayanan15_arxiv}. Table \ref{table:quant_camvid} shows our improvement for classifying column-pole or sign symbols. Figure \ref{fig:quanlity_analysis} shows visually that the prediction of finer details such as the column pole are improved. This improvement is mainly due to the recurrent connection of the weights in the encoder and decoder networks respectively. In addition, the weighted summation of pixels through skip connections help significantly to recover details of smaller objects even after lowering the resolution through several pooling layers.

\section{Introduction}
\begin{figure*}[t]
    \centering
    \includegraphics[width=\textwidth]{images/network.pdf}
    \caption{Overall architecture of the Label Refinement Network (LRN). LRN is based on encoder-decoder framework. The encoder network produces a sequence of feature maps with decreasing spatial dimensions. The decoder network produces label maps with increasing spatial dimensions. A larger label map is obtained by combining the previous (smaller) label map and the corresponding convolutional features from a layer in the encoder network indicated by the solid line. We use down-sampled ground-truth label maps to provide the supervision at each stage of the decoder network.}
 \label{fig:our_archi}
\end{figure*}
We consider the problem of semantic image segmentation, where the goal is to densely label each pixel in an image according to the object class that it belongs to. We propose a convolutional neural network architecture called the \emph{label refinement network}~(LRN) that performs semantic segmentation in a coarse-to-fine fashion.

Deep convolutional neural networks (CNNs) have been successfully applied to a wide variety of visual recognition problems, such as image classification~\cite{krizhevsky12_nips}, object detection~\cite{ren15_nips}, action recognition~\cite{tran15_iccv}, etc. CNNs extract deep feature hierarchies using alternating layers of operations, such as convolution, pooling, etc. Features from the top layers of CNNs tend to be invariant to ``nuisance factors'' such as pose, illumination, small translations, etc. The invariance properties of these features make them particularly useful for vision tasks such as whole image classification. However, these features are not well suited for other vision tasks~(e.g. semantic segmentation) that require precise pixel-wise information.

There have been some recent efforts~\cite{badrinarayanan15_arxiv,chen15_iclr,farabet13_pami,hariharan15_cvpr,long15_cvpr,mostajabi15_cvpr,noh15_iccv} on adapting CNNs for semantic segmentation. Some of these approaches~(e.g.~\cite{hariharan15_cvpr,long15_cvpr,mostajabi15_cvpr}) are based on combining the convolutional features from multiple layers in a CNN. Another popular approach~(e.g.~\cite{badrinarayanan15_arxiv,noh15_iccv} is to use upsampling (also known as deconvolution) to enlarge the spatial dimensions of the feature map at the top layer of a CNN, e.g. to the same spatial dimensions as the original image, then predict the pixel-wise labels from the enlarged feature map. In both the cases, the final full-sized semantic segmentation result is obtained in a ``single shot'' at the very end of the network architecture.

In this paper, we introduce a novel CNN-based architecture for semantic segmentation. Different from previous approaches, our proposed model predicts semantic labels at several different resolutions in a coarse-to-fine fashion. We use resized ground-truth segmentation labels as the supervision at each resolution level. The segmentation labels at a coarse scale are combined with convolutional features to produce segmentation labels at a finer scale. See Fig.~\ref{fig:our_archi} for an illustration of our model architecture.

We make threefold contributions in this paper which are as follows:
\begin{itemize}
 \item We introduce a new perspective on the semantic segmentation (or more generally, pixel-wise labeling) problem. Instead of predicting the final segmentation result in a single shot, we propose to solve the problem in a coarse-to-fine fashion by first predicting a coarse labeling, then progressively refine the result to get the finer scale results.
 \item We propose an end-to-end CNN architecture to learn to predict the segmentation labels at multiple resolutions. Unlike most of the previous methods that only have supervision at the end of their network, our model has supervision at multiple resolutions in the network. Although we focus on semantic image segmentation in this paper, our network architecture is general enough to be used for any pixel-wise labeling task.
 \item We perform extensive experiments on several standard datasets to demonstrate the effectiveness of our proposed model.
\end{itemize}

The rest of the paper is organized as follows. Section~\ref{related} presents related work. Section~\ref{back} discusses the essential background of encoder-decoder architecture. Section~\ref{app} introduces our label refinement network. Section~\ref{exp} describes the experiment details and also presents some analysis. Finally, we conclude the paper in Section~\ref{conclude}.

\section{Related Work}\label{related}
CNNs have shown tremendous success in various visual recognition tasks, e.g. image classification~\cite{krizhevsky12_nips}, object detection~\cite{ren15_nips}, action recognition~\cite{tran15_iccv}. Recently, there has been work~\cite{badrinarayanan15_arxiv,chen15_iclr,farabet13_pami,hariharan15_cvpr,long15_cvpr,mostajabi15_cvpr,noh15_iccv} on adapting CNNs for pixel-wise image labeling problems such as semantic segmentation.

Directly related to our work is the idea of multi-scale processing in computer vision. Early work on Laplacian pyramids~\cite{burt83} is an example of capturing image structures at multiple scales. Eigen et al.~\cite{eigen15_iccv} use a multi-scale CNN for predicting depths, surface normals and semantic labels from a single image. Denton et al. \cite{denton15_nips} use coarse-to-fine idea in the context of image generation. Honari et al.~\cite{honari16_cvpr} combine coarse-to-fine features in CNNs for facial keypoint localization.

Similar to our proposed model, some papers have also used the idea of defining loss functions at multiple stages in a CNN to provide more supervisions in learning. The Inception model~\cite{szegedy15_cvpr} use auxiliary classifiers at the lower stages of the network to encourage the network to learn discriminative features. Lee et al.~\cite{lee15_aistats} propose a deeply-supervised network for image classification. Xie et al.~\cite{xie15_iccv} use a similar idea for edge detection.

redpajama_set_name: RedPajamaArXiv


text:
Die BSG Einheit Ost Leipzig war eine Betriebssportgemeinschaft aus Leipzig, die 1950 neben der BSG Einheit Zentrum Leipzig aus der BSG Erich Zeigner Leipzig hervorgegangen war. Nach der Wiedervereinigung wurde mit dem SV Einheit Leipzig Ost der Nachfolgeverein der Betriebssportgemeinschaft gegründet.

Strukturentwicklung
Als nach dem Ende des Zweiten Weltkriegs in der Sowjetischen Besatzungszone dauerhaft alle Sportvereine aufgelöst wurden, bildeten sich zunächst lose organisierte Sportgemeinschaften, die in der ersten Zeit nur auf lokaler Ebene Sportwettkämpfe austragen durften. In Leipzig entstanden zahlreiche solcher Sportgemeinschaften, allein elf beteiligten sich 1946/47 an der Leipziger Fußballmeisterschaft. Eine dieser Sportgemeinschaften war die SG Probstheida, auch als SG Leipzig-Probstheida bezeichnet. Die SG Probstheida wurde von ehemaligen Mitgliedern des VfB Leipzig und des ATV Leipzig gegründet. Im Jahr 1949 wurde die Sportgemeinschaft in die Betriebssportgemeinschaft BSG Erich Zeigner Leipzig umgegründet, aber nur ein Jahr nach ihrer Gründung schon wieder aufgelöst. Aus ihr entstanden die BSG Einheit Ost Leipzig und die BSG Einheit Zentrum Leipzig, aus der sich wieder der ATV Leipzig entwickelte. Die BSG Einheit Ost übernahm unter anderem von der BSG Erich Zeigner die erfolgreiche Fußballsektion, die sich in der Saison 1949/50 für die neugegründete DDR-Liga qualifizierte.

Sektion Fußball

Die BSG Einheit Ost übernahm den DDR-Liga-Startplatz der BSG Erich Zeigner, nachdem sich diese in der Saison 1949/50 für die neugegründete DDR-Liga qualifizierte. In der Staffel Süd einsortiert belegten die Leipziger hinter der BSG Zentra Wismut Aue und der BSG Chemie Zeitz den dritten Platz. In der darauffolgenden Spielzeit, wo die Gruppen nicht mehr nach der geographischen Lage eingeteilt wurde, belegte die BSG Einheit Ost mit den sechsten Platz einen Mittelfeldplatz in der Staffel 1.

In der Saison 1952/53 sicherte man sich vor dem DDR-Oberliga-Absteiger BSG Motor Wismar den Sieg in der Staffel 2 der DDR-Liga und qualifizierte sich damit für die DDR-Oberliga. Zudem qualifizierte man sich für die Meisterschaftsendspiele gegen die BSG Fortschritt Meerane. Nachdem man im Hinspiel nicht über ein 1:1 hinauskam, siegten die Leipziger im Rückspiel mit 4:1 und sicherten sich damit die DDR-Liga-Meisterschaft.

Obwohl man mit bekannten Spielern wie Rudolf Hecker, Helmut Jacob und Rolf Sommer in die erste Oberligasaison der Vereinsgeschichte ging, musste man lange um den Klassenerhalt zittern. Erst am letzten Spieltag entschied sich, wer die Klasse hält und wer nicht. Am Ende des Spieltags waren die Leipziger punktgleich mit dem BSG Lokomotive Stendal und der BSG Motor Dessau. Die Leipziger schafften den Klassenerhalt, weil zu dieser Zeit bei Punktgleichheit die Torquotienten, also das Verhältnis aus erzielten Toren und Gegentoren, entscheidend war. Während Dessau einen Torquotienten von 0,691 und Stendal einen von 0,745 hatte, hatten die Leipziger mit 0,754 den besten Quotienten und konnten dadurch als Zwölfter die Klasse halten.

Während der Saison 1954/55 kam es zu tiefgreifenden Veränderungen für den DDR-Fußball. Zur gezielten Entwicklung des Hochleistungssports erfolgte in den Bezirken die Gründung sogenannter Sportclubs, deren Sektionen als Leistungsstützpunkte verschiedener Sportarten fungierten. Im Fußball wurden in diesem Zuge ein Großteil der Oberligamannschaften – die bisher fast ausschließlich Abteilungen von Betriebssportgemeinschaften waren – als Fußballsektionen in die Sportclubs eingegliedert. Für die BSG Einheit Ost Leipzig und ihre Fußballabteilung hatte es die Auswirkung, dass die Fußballabteilung nach dem elften Spieltag die BSG verließ und sich dem neugegründeten SC Rotation Leipzig anschloss. Im späteren Verlauf, der immer wieder auch Veränderungen im Spieleraufgebot nach sich zog, entstand aus dem SC Rotation und dann der Fußballsektion des SC Leipzig der im Bruno-Plache-Stadion (auch Heimstätte der BSG Einheit Ost) antretende 1. FC Lokomotive Leipzig.

Abteilung Hockey
In den Anfangsjahren der Betriebssportgemeinschaft verfügte die BSG Einheit Ost Leipzig über eine erfolgreiche Hockeyabteilung. Im Jahr 1951 konnte der Verein auf sich aufmerksam machen, weil er von vier möglichen DDR-Meistertiteln im Hockey drei gewann. Im darauffolgenden Jahr konnten die Herren im Hallenhockey ihren Meistertitel verteidigen.

Was mit der Hockeyabteilung danach passierte, ist nicht bekannt. Weil aber der ATV Leipzig 1845, der nach der Wende aus der BSG Einheit Zentrum Leipzig entstanden ist, sowohl die Meistertitel von Einheit Zentrum Leipzig als auch von Einheit Ost für sich beanspruchte, besteht die Möglichkeit, dass im Jahr 1953 die Hockeyabteilund die BSG Einheit Ost verlassen und sich der BSG Einheit Zentrum angeschlossen hat.

Literatur
 Hanns Leske: Enzyklopädie des DDR-Fußballs. Verlag Die Werkstatt, Göttingen 2007, ISBN 978-3-89533-556-3, S. 543.
 Andreas Baingo, Michael Horn: Die Geschichte der DDR-Oberliga. 2. Auflage. Verlag Die Werkstatt, Göttingen 2004, ISBN 3-89533-428-6, S. 328.
 D.F.S.F (Hrsg.): DDR-Chronik – DDR-Fußball 1949–1991 (Band 1). Berlin 2011.

Einzelnachweise

BSG Einheit Ost
Leipzig, BSG Ost
Leipzig, Einheit Ost

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Our goal is to leave our community better because we exist, not despite it.
"Sustainable" is a confusing term these days. To us, being "sustainable" means educating our guests, contributing to the community, offsetting our impact, and leaving the environment a bit better. Sustainability also begins with education. Our trips are soaked in learning opportunities! Participants on our excursions receive at least one field guide (Sydney city trips excepted) that teaches them about local flora or fauna. For domestic school groups, we have designed lesson plans to sync in with the national curriculum so our trip is an extension of what students are learning in the classroom.
Our office is completely solar powered! By using renewable energy, we are doing our part to reduce our reliance of fossil fuels. We are proud to say that we are also the first tour operator in Queensland to earn an EcoBiz star partnership with Chamber of Commerce and Industry Queensland (CCIQ).
Small World Journeys pays to offset the carbon emissions that result from all of our our trips, and have done so since the inception of the company. Through Sustainable Travel International (STI), our emissions are calculated and offset by our financial support of sustainability projects around the world. Some of those projects include an amazing project in Cambodia to help communities get clean drinking water without burning wood to boil it.
Additionally, we also give our groups the opportunity to offset their air travel and other emissions with our online Carbon Calculator. Offsetting your personal carbon emissions costs less than you might think!
In 2017, we offset 76.72 metric tons of carbon dioxide (CO2) emissions!
Essential to our journeys is the perspective of Australia's Indigenous people. Our policy is to offer at least one experience with Aboriginal people on every set tour. Students learn about the traditions of the Kuku Yalanji, Tjapukai, Yidinji, Ngadjon jii and Jirrbal people through local Aboriginal guides. On our educational excursions, students can learn basics of Jirrbal language, hear about traditional protected area management from a Yirrganydji ranger, create art and dance with Tjapukai people, learn how to use a spear and hunt for food with Kuku Yalanji guides, and learn from a Yirrganydji elder about early life in Cairns. We also encourage our guests to purchase locally-made Indigenous crafts and art as souvenirs as well. Click on the link for information about our trips that involve Aboriginal culture.
The tree is part of a program that involves scientists and researchers finding resilient corals, attaching them to giant structures called "trees" and growing them bigger. The corals are then re-planted on the reef. This is a one-of-a-kind unprecedented project at the Great Barrier Reef and we are thrilled to be a part of it. Students on our tours can also learn from this teaching tool at the reef, which we feel is a symbol of hope for the future of the Great Barrier Reef.
For more information about Reef Restoration Foundation and our sponsorship and involvement, see Philanthropy and Partnerships.
Thanks to our partner, Rainforest Rescue, a 5 square metre plot of Daintree Rainforest is adopted in our guests' names. This helps to preserve areas of the rainforest that have been damaged. Since 2008 we have have protected more than 500m2 of rainforest! Each group receives a certificate to take home, which explains why it is such a special gift to the rainforest. On educational excursions, students even have the opportunity to do plant native trees in riparian zones.
For more information about Rainforest Rescue and our donations, see Philanthropy and Partnerships.
A Small World Journey is about getting to know a place intimately. This is why we don't spend hours in a bus. We have time to dig deep into a destination, and spend our time outdoors enjoying ourselves. This also means minimising our need for fuel and contributing to a healthier planet.
Our guides aren't just "bus drivers": they have a strong background in the natural sciences, and stay up-to-date on current environmental and cultural issues, helping our guests to more deeply know their destination. They teach not only about local flora, fauna and natural history, but about the subtleties of culture, etiquette, and language in Australia.
For more about our expert guides, see The SWJ Team.
We use locally-owned accommodation whenever possible along with restaurants, suppliers and local guides to keep income in the community. We also buy our produce from local farmers. This support is crucial to keeping the tourist dollar circulating in locally, which benefits the community as a whole. We also give you a list of where to buy locally-made souvenirs on your trip.
Ask us about adding a talk by Small World Journeys' owner about Sustainability & You OR Impacts of Tourism – two different talks that highlight the importance of keeping income in the community and reducing "leakage".
We have committed at least 5% of our net profits each year to support non-profit environmental and community organisations. We supply our guests with detailed information about the non-profit organisations to whom we give, so guests may also offer their support if they choose. Every year we make financial donations to organisations like Rainforest Rescue to help save the Daintree Rainforest, Kuranda Envirocare to help protect a riparian zone that hosts 2 endangered species, to The Fitzroy Island Turtle Rehabilitation Centre and to Rosies Friends on the Street to help Cairns' homeless and needy people. In addition, we volunteer for and give to Oz Harvest, a charity helping reduce food waste and re-allocate potentially wasted food to folks who need it.
We do our best to spread the message of reducing consumption. In addition, we look for innovative ways to reuse and recycle – like sending away all of our groups' used snack wrappers to a factory that breaks down the plastic and makes them into other things! We even wipe our bums with toilet paper from Who Gives a Crap (they give 50% of profits to build toilets in developing countries!).
As a student on one of our educational tours, you receive a cloth shopping bag and a reusable water bottle. This gives you the opportunity to decline a plastic bag at the souvenir shop and use your own – which helps keep plastic bags from ending up in landfills, our oceans, and in the tummies of turtles. Similarly, your own water bottle avoids having to purchase plastic bottles that crowd landfills each year. Because of this practice, we estimate that since 2010 we have saved over 10,000 plastic water bottles and 5,000 plastic bags from going in landfills.
For more information on our responsible tourism policies, see Responsible Travel.
Volunteering rocks. As a business we value tremendously efforts made to support the community. So we give our employees time each week to volunteer for the local charity organisation of their choice during work hours. This has the natural outcome of involving our team in making a difference in Cairns, and allowing them to feel good about contributing. And we believe happy staff equals happy customers!


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The Hydro Rush features a spray cannon that continuously squirts water keeping the kids cool. The large slide has an easy climbing system where the kids can pull themselves up and foot holds to climb up as well. My 4 and 5 year old had a blast we had 5 boys together and they were all able to enjoy the Hydro Rush it is very large but takes a minimal amount of water. That was a favorite feature of mine the Hydro Rush will not put a cramp on your water bill .
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Anthony Rissesco is a contemporary violinist who performs folk, fiddle, rock, classical and more. He was a student of Jan Wicha and Philippe Djokic. He has toured for many years with Lennie Gallant and appeared on Lennie Lennie's album "live Acoustic at the Carleton" which received a Canadian Folk Music award. Anthony has been on many festival stages across the U.S with roots rock band "Pogey" He has played and toured with many artists from the Maritimes such as Bruce Guthro and Anne Murray. He has been a guest performer with Symphony Nova Scotia several times either performing with Lennie Gallant or playing fiddle medleys and singing folk songs. He is part of the trio "Papilio" which has released its second CD and whose first was nominated for an ECMA and Music Nova Scotia awards. He grew up playing in the "down east old time tradition"and competed in fiddling competitions across the country. He is a first place prize winner in five Canadian provinces and has represented Nova Scotia at the Canadian Grand Masters several times. Anthony is 3rd prize winner at the Canadian open. He is also a past Maritime fiddle champion and recently a judge at that oldest of Canadian fiddle competitions.
Tickets: $5 (4 and under free). Available at the door.


text:
Nasdaq: CFRX
Navigate ContraFect Corporation
Lysins
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Publications / Posters
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ContraFect Corporation Prices Approximately $10 Million Public Offering of Common Stock and Concurrent $3 Million Private Placement of Common Stock to Pfizer
YONKERS, N.Y., Dec. 10, 2019 (GLOBE NEWSWIRE) -- ContraFect Corporation (Nasdaq: CFRX), a clinical-stage biotechnology company focused on discovering and developing direct lytic agents for the treatment of life-threatening infectious diseases, today announced the pricing of its underwritten public offering of 37,150,000 shares of its common stock at a public offering price of $0.27 per share. The gross proceeds from the public offering will be approximately $10 million, before deducting underwriting discounts and commissions and estimated offering expenses payable by ContraFect. In addition, ContraFect also announced that Pfizer Inc. has agreed to purchase 11,111,111 shares of ContraFect's common stock pursuant to a stock purchase agreement with ContraFect at the price per share in the public offering, in a separate private placement transaction that is expected to close concurrently with the public offering. The gross proceeds from the private placement will be $3 million. All shares in the public offering and private placement are to be sold by ContraFect.
ContraFect intends to use the net proceeds of the public offering and private placement to fund initiation of its Phase 3 DISRUPT trial of exebacase (CF-301) in Staph aureus bacteremia, including right-sided endocarditis, to fund advancement of its portfolio, including IND-enabling activities for an engineered gram-negative lysin directly targeting highly-resistant Pseudomonas aeruginosa, and for working capital and other general corporate purposes.
The public offering and private placement are expected to close concurrently on or about December 12, 2019, subject to customary closing conditions.
Maxim Group LLC is acting as sole book-running manager for the public offering.
The public offering is being made pursuant to an effective shelf registration statement on Form S-3 that was filed with the U.S. Securities and Exchange Commission (SEC) on November 30, 2018 and declared effective on December 13, 2018. A preliminary prospectus supplement describing the terms of the public offering has been filed with the SEC. A final prospectus supplement will be filed with the SEC and will form a part of the effective registration statement. Copies of the final prospectus supplement and accompanying prospectus relating to the public offering may be obtained, when available, by contacting Maxim Group LLC, 405 Lexington Avenue, 2nd Floor, New York, NY 10174, or by telephone at (212) 895-3745.
The shares being sold in the private placement have not been and will not be registered under the Securities Act of 1933, as amended, or any state securities laws and may not be offered or sold in the United States absent registration with the SEC or an applicable exemption from such registration requirements.
This press release shall not constitute an offer to sell or a solicitation of an offer to buy the securities in the public offering or the private placement, nor shall there be any sale of these securities, in any state or other jurisdiction in which such offer, solicitation or sale would be unlawful prior to the registration or qualification under the securities laws of any such state or other jurisdiction.
ContraFect is a biotechnology company focused on discovering and developing differentiated biologic therapies for life-threatening, drug-resistant infectious diseases, particularly those treated in hospital settings. An estimated 700,000 deaths worldwide each year are attributed to antimicrobial-resistant infections. We intend to address life threatening infections using our therapeutic product candidates from our platform of DLAs, which include lysins and amurin peptides. Lysins are a new class of DLAs which are recombinantly produced antimicrobial proteins with a novel mechanism of action associated with the rapid killing of target bacteria, eradication of biofilms and synergy with conventional antibiotics. Amurin peptides are a new class of DLAs, which exhibit broad-spectrum activity against a wide range of antibiotic-resistant Gram-negative pathogens, including Pseudomonas aeruginosa (P. aeruginosa), Acinetobacter baumannii, and Enterobacter species. We believe that the properties of our lysins and amurin peptides will make them suitable for targeting antibiotic-resistant organisms, such as methicillin-resistant Staph aureus (MRSA) and P. aeruginosa, which can cause serious infections such as bacteremia, pneumonia and osteomyelitis. We have completed a Phase 2 clinical trial for the treatment of Staph aureus bacteremia, including endocarditis, with our lead lysin candidate, exebacase, which is the first lysin to enter clinical studies in the U.S.
This press release contains, and ContraFect's officers and representatives may make from time to time, "forward-looking statements" within the meaning of the U.S. federal securities laws. Forward-looking statements can be identified by words such as "projects," "may," "will," "could," "would," "should," "believes," "expects," "anticipates," "estimates," "intends," "plans," "potential," "promise" or similar references to future periods. Examples of forward-looking statements in this release include, without limitation, statements regarding the proposed concurrent public offering and private placement of ContraFect's common stock. Forward-looking statements are statements that are not historical facts, nor assurances of future performance. Instead, they are based on ContraFect's current beliefs, expectations and assumptions regarding the future of its business, future plans, strategies, projections, anticipated events and trends, the economy and other future conditions. Because forward-looking statements relate to the future, they are subject to inherent risks, uncertainties and changes in circumstances that are difficult to predict and many of which are beyond ContraFect's control, including those detailed in ContraFect's filings with the SEC. Actual results may differ from those set forth in the forward-looking statements. Important factors that could cause actual results to differ include, among others, the uncertainties related to market conditions and the completion of the concurrent public offering and private placement on the anticipated terms or at all, and ContraFect's ability to develop treatments for drug-resistant infectious diseases. Any forward-looking statement made by ContraFect in this press release is based only on information currently available and speaks only as of the date on which it is made. Except as required by applicable law, ContraFect expressly disclaims any obligations to publicly update any forward-looking statements, whether written or oral, that may be made from time to time, whether as a result of new information, future developments or otherwise.
Lauren Stival
Stern Investor Relations
Email: lauren.stival@sternir.com
ContraFect has filed a registration statement (including a base prospectus dated December 13, 2018 and a Preliminary Prospectus Supplement dated December 9, 2019) with the Securities and Exchange Commission, or the SEC, for the public offering. Before you invest, you should read the Preliminary Prospectus Supplement, the accompanying prospectus and the other documents ContraFect has filed with the SEC for more complete information about ContraFect and the public offering. You may get these documents for free by visiting EDGAR on the SEC web site at www.sec.gov. Alternatively, ContraFect, Maxim Group LLC or any dealer participating in the public offering will arrange to send you the Preliminary Prospectus Supplement and the accompanying prospectus if you request it by contacting Maxim Group LLC, 405 Lexington Avenue, 2nd Floor, New York, NY 10174, or by telephone at (212) 895-3745.
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1. Does the Real ID Act Unfairly Target Immigrants?


DSB's VR component of the Combined State Plan describes DSB's goals, priorities, partnerships, and activities that will be carried out to serve customers between July 1, 2016 and June 30, 2020.
The DSB VR attachment describes how DSB will provide high quality, effective vocational rehabilitation services to our customers. The State Plan is a part of the Workforce Development Combined State Plan that is required by the Workforce Innovation and Opportunity Act.
The Workforce Innovation and Opportunity Act (WIOA) of 2014 reauthorized the Rehabilitation Act of 1973, and maintained funding for the vocational rehabilitation activities through the Department of Services for the Blind. The law also consolidated job training programs under the Workforce Investment Act of 1998 (WIA), amended the Wagner-Peyser Act, and reauthorized adult-education programs. The emphasis within the law is for all workforce partners to deeply collaborate to streamline services and improve outcomes for a shared customer base. Within WIOA, there is an added emphasis on youth services, universal accessibility, serving communities with barriers, and effective engagement with business for all partner programs.
All workforce partners under WIOA in Washington State will collaborate on a combined state plan as an agreement with the federal partners on how the law will be implemented in the state. The combined state plan, entitled "Talent and Prosperity for All", has a number of components: a strategic component, a shared operational component, and specific attachments for each of the individual partner agencies. The vocational rehabilitation attachment requires DSB to provide responses to specific area questions.
The draft of the Department of Services for the Blind's vocational rehabilitation program attachment to the combined plan is availalable for your review. Comments can be sent to comments@dsb.wa.gov.
The Department of Services for the Blind has also submitted a draft response to the combined operational component of the state plan, which will later be merged with the responses of the other core partner groups. Comments can be sent to comments@dsb.wa.gov.
PDF documents can be viewed with Adobe Reader.
The Division of Vocational Rehabilitation - https://www.dshs.wa.gov/ra/division-vocational-rehabilitation/2016-2020-draft-dvr-state-plan.
For more information contact us at 800-552-7103 or comments@dsb.wa.gov.


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Back to Features > Winds of change
In the UK, the past few years have seen an increase in an array of extreme weather events. Hotter, drier summers and wet, stormy winters are placing more strain on our energy infrastructure and our vital industries
EMBRACING THE OPPORTUNITY OF THE GROWING OFFSHORE SECTOR IN THE UK. BY ALEXANDRE GOLISANO
In the UK, the past few years have seen an increase in an array of extreme weather events. Hotter, drier summers and wet, stormy winters are placing more strain on our energy infrastructure and our vital industries. Back in 2018, The Environment Agency estimated that the economic damage of the 2015-16 winter floods (the most extreme flooding on record at the time) was around £1.6 billion. Heavy deluges hitting the entire country at the beginning of this year in the form of storms Ciara, Dennis, Ellen and Francis, illustrated the direct effect the climate crisis is having on our environment, effectively forced into ever-sharper definition.
Growing pressure is being applied by consumers and governments alike to avert the climate crisis by moving away from our traditional 'dirty' fossil fuel power sources towards low carbon solutions.
Climate change is (still) an energy problem
Although the UK and its like-minded international allies have been working towards targets to limit global warming for many years, the Intergovernmental Panel on Climate Change has agreed that our definition of a 'safe' limit of climate change needs to shift. For example, the 2oC global temperature increase cap as set out by the Paris Agreement, publicised for many years as the most progressive global climate change target, is now being revised. Instead, 1.5oC is the absolute maximum that can be mitigated in order to prevent numerous low-lying countries (such as the Netherlands) from being inundated as sea levels rise.
But seeing the climate crisis without also recognising the economic opportunities of building a more sustainable world would be a mistake. While we face significant challenges to limiting our greenhouse gas emissions and our contribution towards global warming, the UK is ideally placed to taking a leading role in a new emerging 'green economy' approach. This is where the windy weather of the British Isles comes into play…
The UK energy sector in particular has already made significant strides towards decarbonising. Energy supply was the single largest generator of greenhouse gas emissions producing 242.1 million tonnes of CO2 in 1990, but by 2018 had reduced this by an impressive 59 per cent. However, the UK is now the first country which has committed to achieve net zero emissions by 2050 – meaning that the government will need to rely on offshore power in their strategy to reach this goal.
To achieve this, the renewables sector in the UK is growing fast, supported by both private investment and the government's introduction of Contracts for Difference in 2014-15, which have been successful in incentivising renewables projects by protecting businesses from volatile wholesale process and guaranteeing a reliable return on investment. According to Renewable UK, the UK's current operational capacity from onshore and offshore wind is over 22 gigawatts. This is enough energy to power almost 16 million homes. One of the low carbon sources most suited to our current climate and environment, offshore wind already contributes a significant part of our renewable energy with eight GW installed, a figure which is forecast to increase to 30GW in 2030 and 75GW in 2050, with several new project auctions in the works.
While this target sounds ambitious, innovations both at home and in the global markets are supporting this boom in offshore wind power. The size of turbines is increasing, meaning that more power can be generated from each unit installed in a highly efficient way. With the number of ongoing projects in the UK also scaling rapidly, the cost of investment in the sector has dropped by approximately 40 per cent in just the past two years. As a result, the cost of the electricity generated becomes more affordable, projected to drop below electricity wholesale prices in 2024. Demand on the consumer side for green and affordable energy is strong, however the unpredictable nature of wind energy supply still poses an obstacle to weaning our grid off 'switch on' energy sources. Built for a traditional one-way power flow in a passive network, our grid is working on building up the digital capacity required to manage the new reality of distributed renewable energy generation. The UK has adapted significantly since the days of coal-fired power, and improved infrastructure must be implemented to suit our new approach to energy.
What's next for offshore in the UK?
Currently, the flow of data in our power grid is inconsistent across the country. Forty per cent of the power generated through renewables in the UK goes directly to Distribution Network Operators (rather than the Transmission Network Operator). This creates a major obstacle for National Grid to embrace active energy management and balancing the grid's load effectively, something which is essential to incorporating the variable output of renewable sources into our energy mix to deliver a reliable supply for both consumers and industry. Data is fundamental to the future of our power sector's economy to unlock system and consumer benefits, flexibility, resilience and costs in the most efficient way.
Alexandre Golisano is Strategy Director Power Systems at Schneider Electric. At Schneider, we believe access to energy and digital is a basic human right. We empower all to make the most of their energy and resources, ensuring Life Is On everywhere, for everyone, at every moment. We provide energy and automation digital solutions for efficiency and sustainability. We combine world-leading energy technologies, real-time automation, software and services into integrated solutions for Homes, Buildings, Data Centers, Infrastructure and Industries.
For further information please visit: www.se.com


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University girl defends Sh102million gift from her Belgium boyfriend
Felista Njoroge, a student at Nairobi Technical Training Institute (NTTI), has moved to court to defend the Sh102 million gift from her Belgium boyfriend.
The 21-year-old was aggrieved by the Asset Recovery Agency's decision to freeze her accounts.
In an affidavit to the High Court, Njoroge says the agency misled the court about her sources of funds, which according to her, she could have explained had they contacted her.
"The agency's decision to seek the freezing orders was an infringement to her economic rights. It was actuated by malice since no explanation was sought regarding the sources of her funds," Njoroge said through her lawyer, Thomas Maosa.
She has accused the agency of invading her privacy stating that she has the capacity to engage in business and develop relationships to make money.
"I am lawfully entitled to my funds held at my bank account since the agency obtained the sweeping orders freezing the account without asking me where I received the funds from," she said.
Additionally, she maintained that she is not engaged in any financial misconduct or money laundering, and that she can explain how she acquired the Sh102.5 million.
Njoroge now wants the court to set aside an order stopping her from withdrawing the money from her account held at Co-operative Bank of Kenya.
The agency claimed that the money was suspiciously wired and alleged that they were proceeds of unlawful activities. ARA thus sought court orders to deny her access to the funds.
"The monies were for investing in land projects and safaris," her lawyers told ARA, two days after the agency moved to freeze her accounts.
Njoroge, through her lawyers, however, argued that the aforementioned amount was set to be channelled towards her investment projects.
Her boyfriend, Merc De Mesel was said to have made the first deposit of Sh25,803,756 on August 4, 2021.
A second deposit of a similar amount was made on August 5 with the third and fourth deposits following on August 6 but as separate transactions of Sh25,242,756 and Sh25,579,356.
Messel, a bitcoin billionaire, lamented that he followed the stipulated money transaction processes and also filed the requisite papers.
Shinyalu MP Justus Kizito in custody over malicious damage of property
Police launch manhunt for two armed men who shot siblings at Gatundu courts


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Second Place
$100 Award
The Ola Dress
by Sandra Worsham
I chose Kathy to give The Ola Dress to. I thought she was the most sentimental of all the second cousins. I remembered her as a child, her sweet smile, her loving ways. Everyone always said she was the grandchild most like Aunt Alice, my mother's oldest sister. Kathy was the one who sent birthday cards to Mama and pictures of her children in Christmas cards every year.
It was the night before she died from ovarian cancer that Mama told me about The Ola Dress. It was the night she wanted to talk, and she wanted me to take notes about everything she said. I sat in a chair at the foot of her bed with a notepad and pen.
"The family pictures are in that second drawer to my desk in the living room," she said. "Separate those by family and give them to whichever family they belong. That gold Depression glass bowl with the roses in the bottom is something Alice brought me from the World's Fair. You can keep it."
She waved her hand in the air over her face as if shooing away a bug. Then she continued. "There is an old dress in the bottom drawer of my dresser. We always called it The Ola Dress because it belonged to a little cousin of ours who died young. We got her dress, and Alice tatted a neckline to run a ribbon through. You might want to put that in a shadow box."
Then she got philosophical. "Life is just a book, and this is the grand finale," she said. And, "It is just as important to prepare for death as it is to prepare for life." And, "Don't hold anything against anybody, whatever they did or didn't do during this time of my sickness." And, finally, "Is everything all right? Between us?"
Ironic that she would ask that last question. It was the question I always asked her, the question I asked to make that gnawing feeling of guilt, or something not being right, go away. A feeling that the radio station was not quite on the mark, a little sound of static inside my heart. The question was always brought on by that look on my mother's face, the sad look, the look that made me think I had done something wrong, made her mad, hurt her feelings.
So often I had. If I talked back or touched something in the store after being told not to so many times, there was that look, the sad look. And the waiting began. Waiting for forgiveness. It seemed like hours, the back turned, the silence, the look, that look.
"Mama, smile when I smile," I once said, looking up at her, the tips of my fingers touching her elbow as she stood staring out the window over the kitchen sink.
"I don't feel like smiling."
"Is everything all right yet?" I asked, after waiting longer.
If everything wasn't all right yet, the answer was silence.
"Mama, I'm sorry," I pleaded.
"There are some things that saying 'I'm sorry' won't make all right." When she said that, I wondered what those things might be. What could you do that could not be undone? You could kill a person. Saying 'I'm sorry' would not bring a dead person back to life. I wondered if my mother thought I would ever commit a murder. When she said those words, life seemed like a long tunnel of waiting with something beautiful only at the very end, something like blue light, like pink quartz, like forgiveness.
What Mama wanted to know that night was if that never-again-mentioned subject was still standing between us, or if it had been finally put to rest. It was a subject I would not discuss again, not on that night, or the next day, or ever again with my mother.
It was the day that, once it happened, was put to rest, buried between us as real as if it had been given a bonafide funeral and had been buried in a coffin under layers of dirt. It was the day, followed by the next day, the day of declaration followed by the day of about-face, of turn-around, of the renunciation of the declaration.
"Mama, I want to go live with Gale," I said in the letter I handed her at age twenty-eight, on the day I had designated to tell her in her living room. "She makes me happy, and I love her," I wrote in my leaning-backward handwriting.
The letter that day slipped from my mother's fingers. It fluttered to the floor like a wounded bird. My mother's face went into her hands. She shook her head. The tears came. "I can't live here in this town any more," she said. "I'm going to have to move."
I can't explain what happened to me in my bed that night in my apartment alone. All I know is that the next morning, nothing in my life was the same. I could not do the deed. I could not move ahead, my mother's life ruined. The feeling had to go away. That was all. I promised God that I would never kiss another woman.
The next morning meant one phone call to my mother. "Never mind," I said. "It's not going to happen. I take it back."
All I said to Gale was, "I'm sorry, but I can't."
From that day on, I became the person that I could live with. My mother and I never referred to that day again. Until that night and the question she asked. "Is everything all right? Between us?"
"Absolutely," I said.
I met Lauren on match.com, and we were married in Vermont. The day I went to meet Kathy at the Mexican restaurant in Eatonton, I took our wedding pictures. I had put them in an album, and I was filled with the zeal and confidence of a new convert. Kathy was young and hip. She would be cool with the news.
I left The Ola Dress in the car when I went into the restaurant. It was big and was in a shadow box with a glass front and a large, cumbersome wooden frame. In my arm I held the album containing our wedding pictures.
Kathy looked like a hippy. She wore a soft cotton peasant shirt in colors of purple and green. Her long brownish-graying braid hung like a rope over her left shoulder.
"I'm so glad you told me," she said, turning the pages of the album. "Now I can relax," she said. "Now I can really hug you." A strange statement, but I thought I understood. It meant, "Now I really know you."
When we went to our cars after lunch, we moved The Ola Dress from my trunk to hers. She seemed pleased to have it.
It wasn't until just before the family reunion that I learned that Kathy had told everyone. All my first cousins, her parents, who then told their brothers and sisters. Soon all my first cousins in Atlanta—all of Aunt Alice's children—had been told that I had married a woman. All these cousins, Fundamentalist Baptists who had surely voted against Obama. "I didn't even know two women could get married," Kathy told me her mother, my cousin Patsy, had said. Kathy had many excuses about why she had told them: it was an accident; she had let something slip about how happy she was for me, and then Patsy wouldn't let it drop until she told her. Patsy told her husband Buster. Buster called up Larry and Frankie. Larry called up Bertha and Bob. In less than a day, all of Aunt Alice's children and their husbands and wives and offspring knew.
But I went to the family reunion anyway. I was determined to finally live my own life.
At the reunion Kathy had an album of her own. She was proud to show me the pictures from her and her husband Dale's hunting trip to Africa. I sat down and turned the pages. There on every page, one after the other, were pictures of Kathy with her rifle by her side, genuflecting beside the corpse of a dead animal, its feet folded as if in prayer. There she was on every page, posed the same way, smiling the same smile, her long gray braid hanging over her shoulder, proud that she was a real woman who knew how to shoot and kill beautiful animals. She was not proud of the casserole she brought to the family get-together, but she was proud of her album. Zebra, antelope, caribou, wildebeest. Page after page of carnage. Why would you kill a zebra? I was thinking as I turned the pages. I was not thinking, Now I can really hug you.
Before I left, Kathy said, "You and Lauren must come and see our house. All the animals have come in, and we have them on the wall. I don't know how you feel about early dead animal," she laughed.
"Well, I'm thinking that The Ola Dress probably doesn't fit in with the décor," I said.
"Not really. I gave it to Daddy, and he has it on the wall in the basement. It looks good down there."
I pictured it. The Ola Dress encased in glass, protected, waiting, ready to be passed down through the generations. The leftover from a dead girl, frozen in place, something that probably wouldn't fit in anywhere.
Sandra Worsham lives in Milledgeville, Georgia, where she attends "Wings" every Tuesday afternoon with friends at a local restaurant—no literary symbolism: the wings are cheap on Tuesday. Her stories have been published in Carolina Quarterly, Chattahoochee Review, Memphis Magazine, Ascent, and Los Angeles Review, among others. In 2006, she received her MFA in fiction from Bennington College. Her story "Pinnacle" won the 2006 Red Hen Press Short Fiction Award. She is currently working on a memoir entitled Going to Wings: a Memoir of a Late-Life Coming Out. "The Ola Dress" is based on stories from this memoir.