text:
\section{Introduction}
\label{S:1}

Detection of logos in scene images and videos has a number of useful applications: commercial analysis of brands \cite{gao2014brand}, vehicle-logo detection \cite{psyllos2010vehicle} for intelligent traffic-control systems and even Augmented Reality \cite{hagbi2009shape}. A logo is a unique symbol representing any brand or organization that expresses its functionality and distinguishes the brand and its products from others. A merchant can easily assess the presence of his brand in television, social media and e-commerce sites by searching for the company's unique logo. Detection and localization of logos is a crucial step in inspecting market trends, allowing companies to meet the customers' needs by optimizing existing marketing schemes.
Automating logo detection will also allow merchants to detect copyright infringement cases by testing the originality of suspicious advertisements.

\setlength\textfloatsep{5mm}
\begin{figure}
\label{pic1}
  \includegraphics[width=1\linewidth]{PR_logo.pdf}
  \caption{Illustration of Query-based logo detection problem.}
  \label{figure:1}

\end{figure}

Logo detection problem can be seen as a special case of object detection \cite{ren2015faster} in images.
The appearance of a logo varies drastically in real-world images due to lighting effects, occlusions, rotations, shearing effects and scales. It is hard to detect different sizes of logos in a diverse contextual environment with uncontrolled illumination, low resolution, and high background clutter. In the recent years, logo detection has gained a lot of attention from the computer vision community \cite{bianco2017deep,boia2016logo,eggert2015benefit,oliveira2016automatic,su2017deep,alaei2016logo}. Earlier works of logo detection rely on the bag-of-words approach \cite{csurka2004visual} where SIFT features are quantized into a vocabulary of learned logo patterns in images. Boia \emph{et al. } \cite{boia2016logo} used a novel approach based on homographic class graphs to perform both logo localization and recognition. Recently, significant improvement has been achieved by adopting deep-learning techniques in this field \cite{bianco2015logo,eggert2015benefit,iandola2015deeplogo}.
In the meantime, several object detection algorithms have been introduced, namely, R-CNN \cite{girshick2014rich}, Fast R-CNN \cite{girshick2015fast} and Faster R-CNN \cite{ren2015faster} which have been successfully adapted for the logo detection problem \cite{oliveira2016automatic}, boosting object-recognition performances further. In addition, due to advent of deep learning, there have been significant progress in scene-text detection \cite{liu2019curved}, scene text recognition \cite{luo2019moran}, script identification \cite{bhunia2019script} tasks etc; however, there are very limited recent deep-frameworks towards logo detection and recognition in spite of its complexity.

Deep-learning based frameworks are largely data-driven, contrary to logo-datasets that have several image classes but few images. Since deployable logo detection models need to be robust to new unseen logos,  the model should be designed to satisfy the incremental demands for logo classes, contrary to existing methods which are limited to a set of seen logos and are not scalable enough for practical deployment. With the current problem setup, it is impossible for models to work with the logo of any brand. To meet the need for a scalable solution, we re-design the problem statement as shown in Figure \ref{pic1}. Given a query logo of a particular company or organization, the main objective would be to find out whether the logo of the same company or organization is present in a target image or not. If present, the model fetches its position within the given scene.

Following this problem statement, we propose a one-shot learning based technique to design a framework that adapts to new logo classes in a data-efficient way. Recently, one-shot learning \cite{fei2006one} has gained notable attention in learning new concepts from sparse data.
One-shot image classification \cite{koch2015siamese} and one-shot image segmentation \cite{caelles2017one,Dong2018,shaban2017one, Zhang2018} are some practical applications of it. Inspired by these works, we propose a one-shot query based logo detection framework that consists of two modules: the conditioning module and the segmentation module. The conditioning part takes a query logo image and obtains a latent representation which will be used as a conditional input to the segmentation branch. The segmentation network is a basic encoder-decoder architecture. In order to capture the multi-scale correlation between the query image and different regions of the target image, we concatenate the conditional latent representation with multiple layers of the encoder part(segmentation network), followed by $1\times1$ convolution.
The obtained representation is further combined with the respective layers of the decoder part in order to better guide the decoder part of segmentation network to generate binary segmentation mask, forming a skip-connection like architecture \cite{ronneberger2015u}. When the model detects high similarity between a query and some particular region of the target image, the network tries to produce high response at the corresponding portion of the segmentation map. To apply our model for a new logo-class, unlike the fine-tuning approach \cite{caelles2017one} which may require large number of training samples with corresponding ground truth data, our approach needs only one sample of the logo of a concerned company or organisation. This query logo sample can be obtained very easily from the official logo design image or just by cropping an appropriate logo portion from a scene logo image. In this paper, we make following novel contributions:

\begin{itemize}[leftmargin=*]
\item We propose a scalable solution for the logo detection problem by re-designing the traditional problem setting. We present a query-based logo search and detection system by employing a simple, fully differentiable one-shot learning framework which can be used for new logo classes without further training the whole network. To the best of our knowledge, ours is the first work to address one-shot query based deep learning framework for logo retrieval which is novel in the literature of logo-retrieval research.

\item To deal with the logos of varying sizes, we propose a novel one-shot framework through \textit{multi-scale conditioning} that is specially designed to learn the similarity between the query image and target image at multiple scales and resolutions.
\end{itemize}

\section{Related Works}
\paragraph{Traditional methods:} Logo-related research has been carried out for over two decades in the area of computer vision and pattern recognition. Earlier, logo recognition and detection were categorized as a specific problem of object detection. The primary models were developed on geometric object features \cite{lamdan1988object} which relied on properties of objects such as lines, vertices, curves and shapes. Later on, properties of pixel value (luminance or color) were introduced as photometric object \cite{schmid1997local,gevers1999color,lowe2004distinctive} features, which eventually replaced the previous ones. These features were computed locally, could solve the problem of occlusion to an extent and were able to distinguish similar objects better way \cite{schmid1997local}. Thereafter many logo-related works were carried out with the methods of content-based indexing and retrieval in trademark databases. The main goal is to assist in trademark infringement detection by checking a newly designed trademark with registered logos in archives \cite{kim1998content,yin2002content,eakins2003shape, van2007layout}. The task of trademark recognition in videos is inherently harder due to loss of quality
of original logos during processing (e.g. color sub-sampling, video interlacing, motion blur, etc.). However in this case, it is assumed that the acquired images are of good quality and moderate distortion free. Kovar \emph{et al. } \cite{kovar2002logo} applied a heuristic technique to discard sparse or small populated edge regions of the images and analysed the set of significant edges during logo detection. The work in \cite{den2003logo} deals with the logos that appear on the rigid planar surfaces having homogeneous colored background in images using Hough Transformation. Color histogram back projection is applied on candidate logo regions \cite{hall2004brand} to recognize candidate logos. Multidimensional receptive field histograms are also used to perform the task of logo recognition. Here, the most likely logo region is computed for every candidate region. Therefore, if a region in the image does not contain a logo, the identification precision gets reduced.

The traditional logo recognition models are well established on key-point based detectors and descriptors (specially SIFT). SIFT-based models \cite{lowe2004distinctive} basically take an image and transform it into a large collection of feature vectors that are invariant to affine transformations and even robust to different lighting conditions. One of its main characteristics is the ability to detect stable salient points in the image across multiple scales. On the basis of this, Lamberto \emph{et al. } \cite{bagdanov2007trademark} proposed a representation of trademarks and video frame contents using SIFT feature-points mainly targeting to detect, localize and retrieve trademarks in a robust manner, irrespective of irregularities. The classification of retrieved trademarks is analyzed by matching a set of SIFT feature descriptors for each trademark instance with the features detected in every frame of the video. Kleban \emph{et al. } \cite{kleban2008spatial} proposed a logo detection model by clustering matching spatial configurations of frequent local features and introducing spatial pyramid mining.  Alexis and Olivier presented a new content-based retrieval framework \cite{joly2009logo} using a thresholding strategy in order to improve the accuracy of query images. In \cite{meng2010interactive}, the author described the logo detection problem as a small object detection problem and solved it by interactive visual object search through mutual information maximization. Romberg and Lienhart \cite{romberg2013bundle} exploited a large scale recognition approach using feature bundling. As feature bundles carry more information about the image content than single visual words, they aggregated individual local features into bundles and then detected logos by querying the database of reference images based on features. Based on the analysis of the local features and basic structure, such as edges, curves, triangles, etc. Romberg \emph{et al. }  \cite{romberg2011scalable} presented a system by  encoding and indexing the spatial layout of local features found in logos. Revaud \emph{et al. } \cite{revaud2012correlation} introduced dedicated correlation-based burstiness model using a down-weight technique for noisy logo detections. Boia \emph{et al. } \cite{boia2015elliptical, boia2016logo} smartly exploited homographic class graphs to analyze logo localization and recognition tasks. It is noteworthy that they used inverted secondary models to control inverted colors instances. Marcal \emph{et al. } \cite{rusinol2010efficient} presents a robust queried-by-example logo retrieval system where logos are compactly described as a variant of the shape context descriptor. They perform k-NN search in the locality sensitive hashing database to retrieve logos. Jianlong \emph{et al. } \cite{fu2010effective} exploits local features to form a visual codebook and build an inverted file to accelerate the indexing process. Then several groups are proposed according to the local feature type namely "point type", "shape type" and "patch type". Finally, adaptive feature selection with weight updating mechanism is used to perform logo retrieval. Jinqiao \emph{et al. } \cite{wang2006logo} used k-means clustering to develop a visual logo dictionary, and next, latent semantic indexing and analysis is used for logo retrieval. Soysal \emph{et al. } \cite{soysal2011joint} considered spatial similarity of local patterns by utilizing a descriptor for scene logo retrieval.

\paragraph{Deep learning-based methods:}
Bianco \emph{et al. } \cite{bianco2015logo} applied an unsupervised segmentation algorithm to produce a number of object proposals that are more likely to contain a logo object. Thereafter these object proposals are processed through a query expansion step in order to deal with the variation of logo instances. Finally, pre-trained CNN model with SVM classifier was used for logo recognition. Based on the previous Deep-CNN based pipeline, Eggert \emph{et al. } \cite{eggert2015benefit} trained an SVM classifier on synthetic training examples to compare with a trained classifier on real images. The main objective of this approach was to demonstrate the benefit of using synthetic images during training. Iandola \emph{et al. } \cite{iandola2015deeplogo} investigated several variations of GoogLeNet architecture including GoogLeNet with global average pooling and auxiliary classifier after each inception layers. Oliveira \emph{et al. } \cite{oliveira2016automatic} exploited Fast R-CNN to detect graphic logos. To tackle the limitation of large-scale graphic logo datasets, transfer learning was used to leverage image representation.

Very recently, Bianco \emph{et al. } \cite{bianco2017deep} proposed a deep learning pipeline investigating the benefits of different training choices such as class-balancing, data augmentation, contrast normalization, sample-weighting and explicit modelling of background class. Su \emph{et al. } \cite{su2017deep} has introduced a  framework to generate new training data for logo detection by synthesizing context and thus it intends to increase the robustness against unseen cluttered background. Based on the similar hypothesis, a more advanced framework \cite{su2018open} using Generative Adversarial Networks has been designed to generate context consistent logo images for training.  \cite{su2018scalable} proposed a incremental learning approach which discovers informative training images from noisy web data in order to improve the performance. In contrast to these recent works, we here intend to explore the existing benchmark logo-datasets and design a new one-shot deep framework for query-based logo retrieval.

\paragraph{One shot learning:} Creditably, one-shot learning requires only a single annotated image to learn a new class. For few-shot learning, only a few examples of a class are needed to generalize knowledge for recognition. In recent years, one-shot learning is applied to various fields of computer vision such as image classification and visual question answering.
The Siamese network architecture by Koch \emph{et al. } \cite{koch2015siamese} has shown that few-shot image classification can outperform several classification baselines for a binary verification task. The siamese network makes use of two
shared network to extract features from two input images and a similarity score between the two feature representations decides the correspondence between the input images. Annother important recent work in few-shot classification is Matching networks \cite{vinyals2016matching} that learns to determine the correct class label for a given query image from unseen catagories. Discriminative methods described in \cite{bertinetto2016learning,hariharan2016low} have the ability to update parameters of a base classifier that has learnt from training classes, while adapting to new classes for the specific task. But the complication in adapting classifiers in this manner is that they are prone to overfitting. Bertinetto \emph{et al. } \cite{bertinetto2016learning} trained two-branch networks, where one branch receives an example and generates a set of dynamic parameters and second branch classifies the query image based on those parameters and a set of learnt static parameters. Noh \emph{et al. } \cite{noh2016image} used a similar approach for question answering. Most of the existing works on one-shot learning focus on classification, not structured output. In \cite{caelles2017one}, a simple approach is proposed by authors to perform one-shot semantic segmentation by fine-tuning a pre-trained segmentation network on a labelled image. But, this method is prone to overfitting. Later, Shaban \emph{et al. } \cite{shaban2017one} introduced a two-branched network to support dense semantic image segmentation in the one-shot setting. A N-way (classes) few shot segmentation framework based on metric learning and prototype learning has been introduced by Dong \emph{et al. } \cite{dong2018few}. Very recently, Zhang \emph{et al. } \cite{Zhang2018} proposed a one-shot semantic semantic framework with a new mask average pooling operation\cite{Zhang2018}. These earlier frameworks do not consider multi-scale information, and the performance is limited in real-world logo datasets.

In contrast, instead of overly complicated network design, we use off-the-shelf neural network components in our architecture design that is trainable end-to-end. The contribution of the work lies in following aspects in terms of network design choices: $(1)$ We use a parameterized $1\times1$ convolution layer in order to measure the similarity in high-dimensional space. $(2)$ A multi-scale conditioning operation is used in order to handle the logos of different scale. $(3)$ Skip-connection between encoder-decoder parts of the network is found to be helpful for better information passing.

\begin{figure*}[t]
\label{pic2}
\includegraphics[width=1.0\linewidth]{Architecture.pdf}
\caption{Query-based logo detection framework: At-first, the conditional network $G$ takes the query logo image $I_{q}^{i}$ and outputs a latent representation $z_{i}\in \mathbb{R}^{{1}\times {1}\times 512}$. In the main segmentation branch, the target image $I_{t}^{i}$ is taken as input. After 5 stages of down-sampling, the encoder part reduces the input to $8\times8\times256$. The earlier obtained $z_{i}$ is concatenated with last layer of each stage via a tile operation. Suppose, at stage-1, i.e. s=1, the feature map $f^{i,s}$ is combined with the $z_{i}$ after the necessary tile operation. Then, after applying a $1\times1$ convolution on the combined representation, it is further concatenated with its respective layer at the decoder side as shown in the figure.
   }

\label{fig:long}
\label{fig:onecol}
\end{figure*}

\section{Problem Setup}
The main objective of the logo detection problem is to find out whether a query logo of a particular company or organization is present in a target image or not. If it is present, we obtain a segmentation map containing information about the spatial location of the query in that target image. More formally, given a query image $I_{q}$ and a target image $I_{t}$ our job is to find out a segmentation map $I_{m}$ which is a binary 2D-matrix. The value $1$ in the segmentation map represents the region containing the query logo whereas $0$ represents background. During training, we have access to a large number of query target-mask triplets $\left \{{I_q^i,I_t^i,I_m^{i}}\right \}_{i=1}^N $ where $I_m^{i}$ is the semantic segmentation map of $I_t^i$ given the conditional query image $I_q^i$ of various logo classes. In the experiments, we have seen that our model is also able to generalize to unknown logos.

\section{Proposed Framework}
\subsection{Overview}
Our proposed end-to-end network can be divided into two steps: (i) conditioning step and (ii) segmentation step. In the conditioning part, we extract a conditional latent representation of the query image through a Convolutional Neural Network or CNN. The segmentation part of our model is a modified version of the U-Net \cite{ronneberger2015u} architecture. The encoder intends to extract more robust feature information from the target image and subsequently, the decoder tries to obtain the corresponding segmentation map conditioned on the latent representation of the query logo-image.
By the word \textit{conditioning}, we here intend to segment regions of the target image conditioned on the query latent vector; in other words, the regions in target image which have a close similarity with the conditional query logo image. In order to overcome the possibility of drastic mismatch in scale and resolution between the query logo and the target image containing that logo, we encourage the model to learn \textit{multi-scale conditioning} by combining the logo representation at different scales to the encoder part of the U-Net architecture. The proposed one-shot learning framework is fully differentiable and the two-branch architecture can be trained in an end-to-end manner.

\subsection{Conditioning Module}
Logo-to-dense feature map conversion is a primary step in our architecture.
Examining the power of extracting robust task-specific feature representation of convolutional networks, we use a CNN as the feature extractor. For this purpose, at first, we resize each query logo image to a fixed size of $64 \times 64$. Next, we feed the query image $I_{q}^{i}$ to the network $G(.)$ which encodes the image to a latent representation $z^{i}$. Specially, the logo image is converted into a multichannel feature vector of unit spatial dimension (i.e. $1\times1\times512$) for further use. Thus,

\begin{equation}
z^{i} = G(I_{q}^{i};\theta_{G})
\end{equation}

where $z^{i} \in  \mathbb{R}^{1 \times 1 \times 512}$ and $\theta_{G}$ is the parameter of the network. We adapt VGG-16 like architecture consisting of 13 convolution layers with rectified non-linearity (ReLU) activation after each layer.
After 6 max-pooling layers, the input image gets converted to $\frac{1}{2^6}$ shape of feature map giving a final feature representation of dimension $1\times1\times512$.

\subsection{Multi-scale Segmentation module}
\label{multi_seg}

In this section, we describe our multi-scale segmentation module. Given a target image $I_{t}^{i}$ and the obtained latent representation $z^{i}$ of query logo, the segmentation network $F(.)$ will try to obtain a segmentation map $\overline{I_{m}^{i}}$. Mathematically,
\begin{equation}
\overline{I_{m}^{i}} = F(I_{t}^{i}, z^{i};\theta_{F})
\end{equation}
where $\theta_{F}$ is the parameters of the network. Following the architecture of U-Net, we have introduced two networks: Encoder network and Decoder network.  Encoder network encodes the target image to a latent representation. The encoder follows the typical structure of a CNN. It consists of a set of two convolution layers of $3\times3$ filter size and followed by a $2\times2$ max-pooling layer with stride 2. This set of operations are repeated five times to progressively down-sample the input image until we get a bottleneck layer where we get the final latent feature representation. In this five-stage down-sampling process we have used increasing number of filters: 64, 128, 256, 512 and 512 respectively in each stage. After, each stage of down-sampling the spatial dimension of the feature maps are reduced $\frac{1}{2}$ times. Thus, the final latent feature map is $\frac{1}{2^{5}}$ of the input image. For example, if $I_{t}$ has the shape of $256\times256\times3$, then the latent feature map is of $8\times8\times512$. We have seen that further down-sampling the obtained spatial representation eventually degrade the results to some extent.

In order to learn the correlation between the query image and the target image, a naive approach would be to just concatenate the latent representations of the two networks.
However, the said method has several disadvantages: 1. The scale difference between the query and the target image makes it difficult to achieve robust segmentation with a single scale conditioning approach. Thus, multi-scale aggregation is required to obtain detailed parsing maps. 2. Even state-of-the-art logo detection frameworks find it hard to detect smaller logos and ones present in very large scenes. Recent works on object detection \cite{bell2016inside,hariharan2015hypercolumns,kong2016hypernet, wang2018multi} reveal that the feature map of the shallower convolution layers have higher resolution and are helpful to detect small objects whereas the deeper layers contain richer task-specific semantic information.

\paragraph{Multi-scale Conditioning:} Taking cues from the aforesaid observations, we make use of the in-network hierarchical feature produced from feature maps of encoder part of the segmentation network having different spatial resolution. Let, given a target image $I_{t}^{i} \in \mathbb{R}^{H\times W\times C}$, the feature map extracted from the $s^{th}$ stage of the five-stage convolution network be $f^{i,s} \in \mathbb{R}^{H_{s}\times W_{s}\times C_{s}}$ , where, $H_{s}= \frac{H}{2^{s}}$ and ${W_{s}}= \frac{W}{2^{s}}$. Earlier, we got $z^{i} \in \mathbb{R}^{{1}\times {1}\times 512}$ as the conditional query logo representation. In order to impose condition on the segmentation network using latent query vector $z^{i}$, we use a technique similar to sliding window based template matching. To measure the similarity between latent query vector $z^{i}$ and each discrete feature $f_{mn}^{i, s}$ in $f^{i, s}$, we use a simple concatenation operation followed by a $1\times1$ convolution.

\begin{equation}\label{conv_eqn}
\overline{f}_{mn}^{i,s} = Conv_{1\times1}^{s}([f_{mn}^{i,s}; z^{i}])
\end{equation}
where$f_{mn}^{i, s}$ represents the feature representation at position $(m,n)$,
and which, upon concatenation with $z^{i}$ gives a vector of size $1\times 1\times (C_{s}+512$). The number of $1\times1$ convolutional filters in $s^{th}$ layer is set to $C_{s}$, and thus it gives $\overline{f}_{mn}^{i,s}$ of dimension $1\times 1\times C_{s}$. By this simple operation(eqn. \ref{conv_eqn}), it tries to capture the similarity between the latent query vector and feature embedding at $(m,n)$ position of the feature map $f^{i,s}$ from segmentation network. High activation values in $\overline{f}^{i,s}$ signifies high extent of matching that the query logo is likely to be present at that location and vice-versa. Mathematically, let's denote conditional query representation as $Z$ and one discrete feature $f_{mn}^{i, s}$ as $F$. Using $1\times1$ convolution operation with parameters $\theta_{1\times1}$, we intend to model $P(Y| F, Z; \theta_{1\times1})$ where $Y$ denotes the similarity between $F$ and $Z$.  To illustrate,  $P(Y| F_{1}, Z_{1}; \theta_{1\times1}) > P(Y| F_{2}, Z_{2}; \theta_{1\times1})$ when the similarity between $F_{1}$ and $Z_{1}$ is higher than $F_{2}$ and $Z_{2}$. These parameters of $1\times1$ convolutions are learnt implicitly from the traditional cross-entropy loss in an end-to-end manner. In other words, this $1\times1$ convolution is a function that takes $Z$ and $F$ as input, and predicts their similarity at its output in terms of activation values. Unlike other works \cite{Zhang2018}, instead of using cosine-similarity computation for feature matching, we make such choice mainly because of two reasons: : \textit{First}, using a parameterized layer to capture the similarity is expected to be robust to various kind of deformations, illuminations present in real word logo retrieval scenarios. \textit{Second}, $1\times1$ convolution handles the intra-class variance well, in other words, sometimes it is found that despite same logo-class it has some difference in terms of the color of the logo, font used, presence of text inside a logo (for example in \textit{adidas} logos, sometimes the text remains missing). Also, it is to be noted that we use the final latent query representation from conditioning module for multi-scale conditioning at every layer in encoder part (segmentation network) since deeper latent representation is supposed to contain better semantic information of the query logo, instead of using pooled feature representation from earlier layers of conditioning module. More comparison with alternative choices are given in ablation study (Section \ref{ablation}).

For faster and efficient implementation, a tile operation is performed on $z^{i}$ to convert it to a specific spatial dimension such that is compatible for concatenation with $f^{i,s}$: $z_{T}^{i,s}=Tile(z^{i};s_{c})$, where $s_{c}$ is the scale required to tile $z^{i}\in \mathbb{R}^{{1}\times {1}\times 512}$ to a new dimension $z_{T}^{i,s} \in \mathbb{R}^{H_{s}\times W_{s}\times 512}$ and concatented with $f^{i,s}$ giving a dimension of  $\mathbb{R}^{H_{s}\times W_{s}\times 512 + C_{s}}$. This is followed by a $1\times1$ convolution with $C_{s}$ filters:
\begin{equation}\label{conv_eqn2}
\overline{F}^{i,s} = Conv_{1\times1}^{s}([f^{i,s}; z_{T}^{i,s}])
\end{equation}
where $\overline{F}^{i,s} \in \mathbb{R}^{H_{s}\times W_{s}\times C_{s}}$. Alternatively, it can be interpreted as applying a sliding window based template matching over the target image with stride $2^{s}$ (since every max-pooling layers steps down the spatial resolution of feature-map by a factor of $2$) for $s^{th}$ stage in encoder part of segmentation network.

The decoder network of the segmentation model is similar to the encoder part, the only difference being that up-convolution has been used. Every stage in the  decoder  path  consists  of  an  up-sampling layer followed by a $2\times2$ up-convolution layer. After that, we combine this with the previously obtained fused representation of the corresponding stage. At last, two $3 \times 3$ convolutions, followed by a ReLU completes the set of operations for a particular stage. These set of operations are repeated for all the stages until we get the predicted segmentation map $\overline{I_{m}^{i}}$ through a SoftMax layer.
At the end, we define a pixel-wise binary cross entropy loss $L$ which is used to train the complete model.
\begin{equation}
L= \frac{1}{HW}\sum_{i=0}^{W} \sum_{j=0}^{H} (-I_{m}^{i}log(\overline{I_{m}^{i}}))
 \end{equation}
The aim of the model is to minimize this loss by updating the parameter of the both the conditioning ($\theta_{G}$) and segmentation ($\theta_{F}$) modules of the network through back-propagation technique in an end-to-end manner.

Some earlier works like Mask-RCNN \cite{he2017mask} first predicts the bounding box using Region Proposal Network(RPN) and then performs the instance segmentation as a two steps process. This requires two separate loss functions for region proposal and final instance segmentation, respectively. In contrast, we avoid using a two steps process and predict the segmentation map in a single step directly, and thus it makes the process faster as well as easy for implementation. That becomes feasible because of \textit{multiscale conditioning} and \textit{parameterized similarity matching layer} with \textit{skip connections} that helps in better information passing between encoder and decoder part of the framework.

\section{Experiment}

We have evaluated the performance of our framework in both one-shot and traditional settings. In the one-shot setting, given an unseen conditional query logo image (from the test set), the model trained on a completely disjoint set of logo classes returns a reference binary mask for localization. In the traditional setting, on top of our query-based strategy, we experiment with the traditional train-test split strategy used by previous works \cite{su2017deep} where some images for each logo-class are utilized for training while the rest is used for evaluation. Despite our one-shot architecture, we evaluated the framework in the traditional setup in order to directly measure our model's performance against the traditional setting that has been adopted by every existing work. Although different classification and recognition problems have been extended to query-based retrieval setups (e.g. image classification to image retrieval \cite{Gordo2016}, handwriting recognition to query-based word spotting \cite{Manmatha1996}), not many prior works using deep learning frameworks exist that addresses logo retrieval in an open-vocabulary scenario and is robust to challenges like low resolution, cluttered background etc. While existing methods are designed for more complicated tasks (e.g. detection and localization of all the logos in the training set), the boost in performance even in sparse training data plus the ability to generalize to unseen logos in the wild justifies our proposal to extend the problem-statement from a traditional to query-based setup.

\subsection{Datasets}

Our model makes use of binary masks as ground truths. FlickrLogos-32 \cite{RombergICMR2011} is a very popular logo dataset consisting of boundary box annotations as well as binary masks. Therefore, we train our model on this dataset. To evaluate the robustness of the one-shot architecture we explored another dataset Toplogos-10 \cite{su2017deep}.  The main reasons to select FlickrsLogos for training and TopLogos for testing are as follows: (a) To the best of our knowledge, FlickrsLogos is the only logo dataset which has binary segmentation mask as ground truth along with bounding box labels. Rest of the available datasets have bounding box labels only. (b) During testing, we preferred to use TopLogos because it has instances of logos of varying sizes and different cluttered background scenario which provides many challenges for logo detection. Brief discussions of these datasets are given below.

\textbf{Training Set} - \textit{FlickrsLogos-32:}
This dataset comprises 8,240 images from 32 different logo classes, each class representing a particular brand. Each class has 70 images with ground truth annotations in the form of bounding boxes and binary masks.
To make the dataset congenial for our approach, we ignore 6,000 images with no logo class. Thus, we have total $32\times70$ i.e. 2240 images available for our experiments.

\textbf{Evaluation set} - \textit{TopLogos-10:}
It contains 700 images of 10 different clothing brand logos with various degree of composition complexity in the logos. Basically, there are ten logo classes: "Adidas", "Chanel", "Gucci", "Helly Hansen", "Lacoste", "Michael Kars", "Nike", "Prade", "Puma", "Supreme". For every class, there are 70 images with fully manually labelled logo bounding boxes. But, unlike the FlickrsLogos-32 it does not have binary mask annotations.
This dataset contains natural images where logo instances in a variety of context, e.g. Hats, wallets, shoes, shown gels, lipsticks, spray, phone covers, eye glasses, jackets, peaked caps, T-shirts, and sign boards. In short, Toplogos-10 represents logo instances with varying sizes in natural real-world scenarios that provide real challenges for logo detection. The main reason behind using TopLogos-10 dataset is to check the generalization ability of our framework in a completely unseen scenario with more complexity, which is a pressing need of one-shot framework. Even if TopLogos has only 10 logo classes, every query-target pair (note that, this number is large) is unknown to the trained model during testing and it challenges the model to deal with real one-shot scenarios like varying logo sizes, different cluttered background etc.

\subsection{Implementation Details}
We will discuss some salient details of our model: starting from data preparation to training and inference details.

\textbf{Data preparation: } \textit{(a) One-shot setting: }As discussed earlier, we have used FlickrsLogos-32 dataset for training our model in one-shot setting. It contains 2240 images with ground truth annotations. We denote this set as $D^{t}$ and their corresponding masks as $D^{m}$. The images of these two sets are resized to a fixed dimension $256\times256$. Now, we obtain the conditional query logo objects by cropping the main images with respect to their bounding boxes followed by resizing to a dimension of $64\times64$. This forms the conditional set of query objects $D^{q}$. Then, we generate a large number of triplets from the obtained image sets $\lbrace D^{q}, D^{t}, D^{m}\rbrace$. If $n$ is the number of images per logo class then $n\times(n-1)$  triplet combination is possible per class. In total, for 32 logo classes we get $32\times(70\times69)$ i.e. 154560 triplets. We use 90\% of these triplets as our training set and 10\% triplets as validation set.
\textit{(b) Traditional setting: }In this set-up, our main objective of query-based logo search remains unchanged. But, now we generate our training triplets only from a small set (40 images per class) of the available data in the FlickrsLogos-32 dataset. These triplets will be used for training the model. And for testing we will use the rest of the images for each class.

\textbf{Training: } We train our model in an end-to-end fashion from scratch using these large number of generated triplets $\left \{{I_q^i,I_t^i,I_m^{i}}\right \}_{i=1}^N $ where $N$ is the total number of triplets. The conditioning branch takes $I_q^i \in \mathbb{R}^{64\times64\times3}$ as an input and obtains a conditional representation of the logo object. This representation will be concatenated with the segmentation network at different scale. The segmentation branch takes $I_t^i \in \mathbb{R}^{256\times256\times3}$ as input and tries to predict a binary segmentation mask $I_m^i$. We use Gaussian initialization with 0.01 standard deviation. We train our model using stochastic gradient descent (SGD) optimizer with initial learning rate 0.0004 and momentum and weight decay are set to 0.9 and 0.0005 respectively. The model is trained for 100K iterations with batch size of 32 for optimizing loss up to a satisfactory level. We have implemented the whole model in Tensorflow and run on a server with Nvidia Titan X GPU with 12 GB of memory.

\textbf{Testing: } In one-shot setting, the main advantage of our model is that once it's trained it can be used for any logo class. To test the scalability and robustness of our model we have used a different dataset. For traditional setting, we test our model on the same FlickrsLogos-32 dataset but with testing images available for each class.

The final binary segmentation mask is obtained by using a threshold of 0.5 on the predicted mask.  To obtain quantitative results on these datasets, we generate a minimum bounding box for each logo instances covering the mask from the predicted binary mask. To attain bounding boxes from the output map, first, we compute the topmost spatial position $T$ ($x_{t}$ , $y_{t}$), bottom most binary pixel position $B$ ($x_{b}$ , $y_{b}$), leftmost binary pixel position $L$ ($x_{l}$, $y_{l}$) and topmost binary pixel position  $R$ ($x_{r}$ , $y_{r}$). Then, we evaluate bounding box value ${X}'=x_{l} $, ${Y}'=y_{t} $, ${H}'=y_{b}-y_{t}$ and ${W}'=x_{r}-x_{l}$.
At last, these bounding boxes are compared with the ground-truth bounding boxes to obtain the Intersection over Union (IoU).
For the performance evaluation, we have used mean Average Precision (mAP) metric for all classes. For the mAP calculation, we choose the  IoU threshold as 0.5. It means that a detection will be considered as positive if the IoU between the predicted and ground-truth exceeds 50\%. As the proposed logo detection technique is based on segmentation of the logo, we have also included the mean pixel IoU or mPixIoU \cite{liang2015semantic} as another metric for the evaluation purpose. However, unlike FlickrsLogos-32 dataset, other dataset does not have any pixel level binary annotation. Thus, the mPixIoU metric is shown only for FlickrsLogos-32 dataset. The code is available here \footnote{https://github.com/AyanKumarBhunia/Deep-One-Shot-Logo-Retrieval.}.

\subsection{Baselines}
To exploit the robustness of our approach, we compared the following baselines with our proposed approach.

\textbf {Fine-tuning: }As suggested by \cite{caelles2017one}, we fine-tune a pre-trained FCN network (only the fully connected layers) with full supervision on the available paired data of a particular logo class and test it on the target images.

\textbf{U-Net:} Here, we focus on the naive way for generating segmentation maps by considering each logo class as a separate semantic class. We use U-Net \cite{ronneberger2015u} encoder-decoder network that takes the target image as input and outputs segmentation map without any conditional reference. Note that this set-up cannot be extended to new classes.

\textbf{SiameseFCN:} Siamese networks are extensively used for one-shot classification tasks \cite{koch2015siamese}. In this method, we use two pre-trained FCNs to extract dense features from query images and target images. L1 similarity metric is used to learn the coherence between the feature for every pixel in the query image and the target image in order to produce a pixel-level binary mask.

\textbf{CoFCN:}
Here, we explore a conditional segmentation network based on FCN \cite{shaban2017one}. At first, we feed query images in the conditional branch to generate a set of parameters $\theta$. We use $\theta$ in the parameterized part of the learned segmentation model, which takes target images as input and produces a segmentation map.

\textbf{SG-One:} This one-shot segmentation framework \cite{Zhang2018} uses a new masked average pooling operation to extract the latent representation of the query image. However, since our framework uses cropped query image (i.e. support set), we use simple global average pooling instead, and use cosine-similarity followed by a tanh layer for similarity matching with rest setup similar to \cite{Zhang2018}. A quite similar one-shot framework has also been addressed for visual tracking problem \cite{Zhang2018}.

\begin{table}[]
\centering
\caption{Comparison of Logo detection performance on FlickrsLogos-32 dataset following traditional setting.``\#'' denotes query-based frameworks.}
\begin{tabular}{l|ccc}
\hline
\textbf{Method}                        & \textbf{mPixIoU} & \textbf{mIoU} & \textbf{mAP} \\ \hline
Bag of Words (BoW) \cite{romberg2013bundle}                     & -                & -             & 54.5         \\ \hline
Deep Logo \cite{iandola2015deeplogo}                               & -                & -             & 74.4         \\ \hline
BD-FRCN-M \cite{oliveira2016automatic} & -                & -             & 73.5         \\ \hline
RealImg \cite{su2017deep}                               & -                & -             & 81.1         \\ \hline
Faster-RCNN \cite{ren2015faster}                               & -                & -             & 70.2         \\ \hline
SSD \cite{liu2016ssd}                               & -                & -             & 67.5         \\ \hline
YOLO \cite{redmon2016you}                               & -                & -             & 68.7         \\ \hline
U-Net \cite{ronneberger2015u}                                  & 20.3             & 30.1          & 40.5         \\ \hline
\textsuperscript{\#}SiameseFCN \cite{koch2015siamese}          & 70.8             & 77.6          & 79.4         \\ \hline
\textsuperscript{\#}CoFCN \cite{shaban2017one}                 & 71.4             & 81.9          & 84.1         \\ \hline
\textsuperscript{\#}SG-One \cite{Zhang2018}                 & 76.5             & 84.5          & 86.9         \\ \hline
\textsuperscript{\#}Ours              & \textbf{78.2}             & \textbf{86.7}          & \textbf{89.2}         \\ \hline
\end{tabular}
\label{tab1}
\end{table}

\begin{table}
\centering
\caption{Logo detection performance on Evaluation set following one-shot setting}
\label{tab2}
\begin{tabular}{c|c|l|c|c|c}
\hline
 \textbf{Training set}                                                                         & \textbf{Testing set}                                                                           & \textbf{Method} & \textbf{mPixIoU} & \textbf{mIoU} & \multicolumn{1}{l}{\textbf{mAP } }  \\
\hline
\multirow{5}{*}{\rotcell{\begin{tabular}[c]{@{}c@{}}FlickrsLogos\\(20 classes) \end{tabular}}} & \multirow{5}{*}{\rotcell{\begin{tabular}[c]{@{}c@{}}FlickrsLogos\\(12 classes) \end{tabular}}} & Fine-tuning     & 15.8             & 24.1          & 27.4                                \\
\cline{3-6}
                                                                                &                                                                                & SiameseFCN      & 36.9             & 46.5          & 51.1                                \\
\cline{3-6}
                                                                                &                                                                                & CoFCN           & 44.6             & 51.3          & 59.7                                \\
\cline{3-6}
                                                                                &                                                                                & SG-One          & 52.3             & 59.7          & 64.4                                \\
\cline{3-6}
                                                                                &                                                                                & \textbf{Ours}   & \textbf{59.1}    & \textbf{66.3} & \textbf{66.8}                       \\
\hline
\multirow{5}{*}{\rotcell{\begin{tabular}[c]{@{}c@{}} FlickrsLogos\\(20 classes)\end{tabular}}} & \multirow{5}{*}{\rotcell{\begin{tabular}[c]{@{}c@{}} TopLogos\\(10 classes)\end{tabular}}}     & Fine-tuning     & -                & -             & 14.7                                \\
\cline{3-6}
                                                                                &                                                                                & SiameseFCN      & -                & -             & 24.4                                \\
\cline{3-6}
                                                                                &                                                                                & CoFCN           & -                & -             & 29.2                                \\
\cline{3-6}
                                                                                &                                                                                & SG-One          & -                & -             & 36.9                                \\
\cline{3-6}
                                                                                &                                                                                & \textbf{Ours}   & -                & -             & \textbf{40.1}                       \\
\hline
\end{tabular}
\end{table}

\begin{figure*}
\label{pic3}
\begin{center}
  \includegraphics[width=1\linewidth]{results_.png}
  \caption{Some qualitative results of our method: In the first two rows we have shown results on FlickrLogo-32 using traditional setting and for the next two rows we have shown one-shot results on TopLogos-10 dataset. I Here, images are in the following order (left to right): query logo, target image, predicted mask, and final detection result (shown by boxes on the images),respectively.}
\end{center}
\end{figure*}

\subsection{Evaluations}

\textbf{Traditional setting:}
We have compared our results with the baselines and some state-of-the-art logo detection methods \cite{iandola2015deeplogo, oliveira2016automatic,romberg2013bundle,su2017deep} as well as evaluated the performance using popular bounding box detectors like Faster-RCNN\footnote[1]{https://goo.gl/zQsSka (Faster-RCNN)} \cite{ren2015faster}, YOLO\footnote[2]{https://goo.gl/vUFEuq (YOLO)}\cite{redmon2016you} and SSD\footnote[3]{https://goo.gl/vxR9c7 (SSD)} \cite{liu2016ssd} while opting for VGG as the baseline architecture. Due to limited data, we initialize this framework with weights trained on Pascal-VOC dataset (see Table \ref{tab1}). Among popular traditional logo detectors, RealImg \cite{su2017deep} gives highest mAP score of 81.1\% and uses a deep model to synthetically generate training data in order to improve its performance. Though state-of-the-art object detectors like Faster-RCNN, YOLO, SSD achieve impressive performance on benchmark object detection datasets, their performance is limited in FlickrLogos-32 dataset because of limited training data. For the same reason, the performance of U-Net is poor.
SiameseFCN, which is a query-based framework, performed moderately on FlickrLogo-32 dataset. CoFCN performed well with a high mAP of 84.1\% which is 3.0\% and 9.4\% greater than RealImg \cite{su2017deep} and Deep Logo \cite{iandola2015deeplogo}  respectively. The proposed multi-scale query based detection technique achieved a better mAP than the previous state-of-the-art method \cite{su2017deep} with 8.1\% increase in absolute mAP; our rival query-based method, SG-One \cite{Zhang2018} trails by 2.3\% mAP value.
\\
\textbf{One-shot setting:}
For the one-shot setup, the results shown in Table \ref{tab2} indicate the capability of different methods to generalize to new classes. These results are comparatively lower than the results observed in the traditional setting. We realize that this is because we are trying to evaluate the performance of the model on a more open space setting by allowing the system to detect logo in a target scene from a single reference logo sample; contrarily, in the traditional setup, the total number of logo classes is fixed and 40 images from each logo class were used to train the model. The fine-tuned baseline produces relatively low mAP since it quickly overfits to the fine-tuning data in the support set. For FlickrLogo-32, our proposed method outperformed the general fine-tuning based approach by 39.4\% mAP value. On TopLogos-10, which is a clothing-logo dataset with dense background clutter, observed results are relatively low. We demonstrate some qualitative results of our framework in Figure 3. We have also evaluated the performance by varying IoU threshold from $0.5$ to $0.8$ and the performance of our framework drops to a limited extent compared to other competitive methods (see Figure \ref{figure4}), and thus it illustrates the superiority of our design choices.

\begin{figure}
  \includegraphics[width=1\linewidth]{Result_iou_grapg.png}
  \caption{Performance with varying IoU thresold values.}
  \label{figure4}

\end{figure}

These improvements over other existing one-shot frameworks \cite{koch2015siamese, shaban2017one, Zhang2018} are mainly due to following design choices in our framework. (1) Multi-scale conditioning (2) Parameterized similarity matching operation through
$1\times1$ convolutional layer at every scale (3) Skip connection between encoder and decoder part of the network. Overall, our framework is undoubtedly simple, easy to implement and understand.

\begin{table}[]
\label{tab3}
\centering
\caption{mAP (\%) of different variants of our configuration on FlickrsLogos-32 dataset using traditional setting}
\begin{tabular}{c|c|c|c|c}
\hline
\multirow{2}{*}{\textbf{Method}} & \multicolumn{3}{c|}{\textbf{k-shot setting}} & \multirow{2}{*}{\textbf{\begin{tabular}[c]{@{}c@{}}Traditional\\ Setting\end{tabular}}} \\ \cline{2-4}
 & k=1 & k=3 & k=5 &  \\ \hline
Variant 1 & - & - & - & 36.5 \\ \hline
Variant 2 & 59.1 & 64.5 & 65.6 & 82.7 \\ \hline
\textbf{Proposed} & \textbf{66.8} & \textbf{71.2} & \textbf{71.6} & \textbf{89.2} \\ \hline
\end{tabular}
\label{tab3}
\end{table}

\subsection{Ablation Study}\label{ablation}
In this section, we have given a comprehenssive analysis of each sub-variant of our framework along with quantitative analysis for different alternative network design choices. Here, we have also shown results for $k$-shot cases, using $k$ different samples of the query image as our conditional input serially, and final binary mask is obtained by logical OR operation between the $k$ predicted binary maps. For the traditional set-up we have used  FlickrsLogos-32 dataset as before. For $k$-shot setting, we have trained our model using images of 20 classes of FlickrsLogos-32 dataset and the remaining part is used for testing (Table \ref{tab3}).

\begin{itemize}[leftmargin=*]

\item {{Variant 1}: } Here, we use a general U-Net architecture without any conditioning branch(not a query-based framework) and notice limited performance.

\item {{Variant 2}: }In this variant, we use conditional query representation, which is concatenated with the latent space of segmentation module, in order to fetch of the location of query logo in a target image. However, it fails to detect small logos in images, specifically when the size of the query logo image and the actual logo present in the target differs considerably.

\item {{Proposed Method}: }We overcome the above problems by employing a \textit{multi-scale conditioning} operation. This makes our model capable of detecting small transformed logos, which is the one of the major challenges for logo detection.
\end{itemize}

\noindent\textbf{Alternative Designs:} To localize the query logo within the target scene image, instead of cosine-similarity measure, we focus on simple concatenation operation followed by $1\times1$ convolution to learn the correlation. Using a parameterized layer for similarity matching at multiple-scales offers a better performance in the context of logo detection (see section \ref{multi_seg}). Keeping rest of the architecture same, we calculate cosine similarity at each spatial position of feature-map from encoder (segmentation) network with conditional query latent vector (converted to the same depth as $f^{i,s}$ using $1\times1$ convolution), followed by a $tanh$ layer and finally combine to the corresponding decoder stage. This alternative network design achieves mAP values of 64.1\% and 87.8\%, trailing ours by 2.7\% and 1.4\% for one-shot and traditional setting on the FlickrsLogos-32 dataset (same setting as Table \ref{tab3})
Also, we tried using pooled (Global Average Pooling \cite{lin2013network}) feature representation of feature maps from multiple layers of the conditional network, with an intuition to get a multi-scale representation of the query logo and evaluate similarity (\textit{ours}) with feature maps of same stage from segmentation network; the results, however, drop by 5.6\%. This signifies that the final latent representation from deep layers of the conditional network contains higher semantic information of the logo that can be used as a conditional input to segmentation network to fetch the logo at multiple-scales of the target image.

\vspace{0.5cm}

\begin{figure}
\centering
  \includegraphics[width=0.55\linewidth]{error.png}
  \caption{Some failure cases of our frameworks. Yellow box is the Ground-Truth. Green box is True Positive. Red box is False Positive. First row illustrates a false-positive case where our model wrongly predicts logo instances(marked in red). This happens due to certain extent of similarity between the query logo and irrelevant patch in the target image. In the second row, it fails to detect a logo instances due to different orientation. Third row shows a failure case due to tiny size of the logo and irregular illumination.
}
  \label{figure5}
\end{figure}

\noindent\textbf{Error Analysis:} Figure \ref{figure5} shows some of the failure cases of our framework. Here, we have used simple pixel-wise cross-entropy loss to train the network; however,  rotation invariant network design could be explored on the top of our proposed multi-scale conditioning framework, so that rotation related error (see second row of \ref{figure5}) could be avoided. Adversarial feature deformation \cite{kumar2019handwriting} could be used to generalize the model from limited data. In addition, it is to be noted that in spite of multiscale-conditioning it fails in few cases for tiny size of the logo. To alleviate such errors, a feature magnification module like \cite{li2017perceptual} or attention mechanism \cite{bhunia2019texture} could be helpful. In addition, the imbalance between  foreground-background could be better handled using newly proposed focal-loss \cite{lin2017focal} which adds a multiplying factor to the normal cross-entropy loss in order to emphasize on hard, misclassified examples.

\section{Conclusion}
In this paper, we have re-designed the traditional logo detection problem setting by proposing a query based logo search system that uses a one-shot architecture. The driving idea of our architecture is the use of \textit{multi-scale conditioning} with a skip-connection based architecture that predicts a logo segmentation mask. The proposed framework is simple and easy to implement. It is capable of detecting new logo classes without additional training data.

We demonstrate the effectiveness of our system by doing experiment on unseen logos in the wild. From the experiments on publicly available logo detection datasets, we noted that our proposed system outperformed the benchmark results \cite{iandola2015deeplogo, oliveira2016automatic,romberg2013bundle,su2017deep} without even extending existing logo datasets.

Though our system is scale invariant, it may fail for logos which are tiny in size. A feature magnification module \cite{li2017perceptual} could be useful to improve the performance in such cases. Also, the imbalance between fore-ground and background information could be handled properly to boost the performance further. In future we plan to  work on these to improve the accuracy.

\bibliographystyle{elsarticle-num}

redpajama_set_name: RedPajamaArXiv


The human ear is a miniature receiver, amplifier and signal processor all in one. The ear is split into three main sections the outer ear, middle ear and the inner ear. The ear also plays an important part in how we maintain our balance and how we perceive things like distance, room structures and atmosphere. Hearing is one of the five human sense's and is essential for how we communicate and live our modern lives. The ears pick up sound waves which travel through the air and through different materials and this is how sound is felt rather than seen.
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text:
Q: Dynamic settings.py I have used django-constance as a library.
Although one thing I notice is that when I tried using ADMIN and MANAGER
CONSTANCE_CONFIG = {
'ADMINS': ([('Errors', 'admin@gmail.com')], 'Admin Emails'),
}

sending of emails is not working.
In MANAGER I have tried this:
MANAGER = CONSTANCE_CONFIG['ADMINS'][0]

still sending emails is not working. Am I missing a wrong implementation?
Or can you suggest any other library which can override ADMIN and MANAGER in settings.py. I am using Django 1.8.5 and Python 3.
also when trying to import inside settings.py it produces error as well.

A: 1#  Probably you already know, django-constance does not support tuple. Basically it is really hard to detect a widget for tuple specially
in your case. ADMINS can be added/deleted so how possibly you can make it dynamic through a single widget..!!(think about all django widgets). So here
 CONSTANCE_ADDITIONAL_FIELDS will also not work.
2# I think you are misunderstanding the working of django constance.
It does not refresh your django server. So MANAGER = CONSTANCE_CONFIG['ADMINS'][0] is totally wrong(even using CONSTANCE_ADDITIONAL_FIELDS). You accessing constant value here(not dynamic).
 You need to access it like
from constance import config
print(config.ADMINS)

3# Default logging config uses AdminEmailHandler class for mail_admins, which uses ADMINS value from django settings, not constance config.
So one possible solution might be to create your own handler class which will use ADMINS value from constance config. So change your setting.py to
CONSTANCE_CONFIG = {
    'ADMIN1': ('admin@gmail.com', 'This one will receive error on 500'),
} # you can add as many admins as you want with ADMIN1, ADMIN2 etc(no tuple)

then create your own handler class which will use CONSTANCE_CONFIG.
from django.utils.log import AdminEmailHandler
from constance import config
from django.conf import settings
from django.core.mail.message import EmailMultiAlternatives

class ConstanceEmailHandler(AdminEmailHandler):
    def send_mail(self, subject, message, html_message=None, fail_silently=False, *args, **kwargs):
        # create a list of ADMIN emails here, if you have more then one ADMIN
        mail = EmailMultiAlternatives('%s%s' % (settings.EMAIL_SUBJECT_PREFIX, subject),
                                  message, settings.SERVER_EMAIL, [config.ADMIN1],
                                  connection=self.connection())
        if html_message:
            mail.attach_alternative(html_message, 'text/html')
        mail.send(fail_silently=fail_silently)

And then change your LOGGER config. I would recommend you to copy default logger config from django.utils.log(DEFAULT_LOGGING) if you  do not have your custom LOGGING setup. And change mail_admins to
'mail_admins': {
    'level': 'ERROR',
    'filters': ['require_debug_false'], # change it to require_debug_true if you want to test it locally.
    'class': '<yourproject>.<yourfile>.ConstanceEmailHandler', # path to newly created handler class
    'include_html': True
    },

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We are located in the "Red Barn" directly across the street from the Montgomery County Criminal Justice Facility Jail.
We are NOT in downtown Conroe but at 97 Criminal Justice Dr. in front of the jail.
If you need directions please call us and we will help you to our front door.
Our family has been helping families for over 30 years so we must be doing something right. Posting bail bonds is our only business and we are pretty darn good at getting things handled fast and cheap.
View information about documents that need to be completed for the bail process. Please do not hesitate to call our office if you have questions about ANY of these documents.
Right here where we've always been - across from the Montgomery County Jail.
To bail someone out of jail NOW call 936-788-2245 (936-788-BAIL).
We try to keep the bail process as simple as possible. So the first thing you need to do is call 936-788-2245 now. We have low cost financing, and accept credit cards, checks, and cash.
You are really never prepared for those unexpected emergencies--i.e. the late-night phone call by a hysterical family member or friend telling you they have been arrested and taken to jail. The first thing that you think about is "How can I help get them released from jail as quickly as possible?" We understand that mindset, so make that call now!
Copyright A1 Bail Bonds and Bail Bonds For Less. All rights reserved.


In the administration panel, go to Administration → Backup/Restore.
The backup files displayed on the Backup/Restore page are located in the var/backups directory of your CS-Cart installation.
You can also select the appropriate dump from the list, if it is located in the default directory var/backups. Click the gear button of the required file and choose Restore.
When you restore the database from an SQL dump, the existing database tables get overwritten.
If you want to restore the whole store from the backup, make sure that files have the writable permissions.


Laser radiation in the X-ray region can be generated with different means, for example using laser-generated plasmas as X-ray gain media or free electron lasers. Frequently, one uses amplified spontaneous emission, since it is difficult to make effective laser resonators for X-rays.
E See also our encyclopedia article on X-ray lasers!
1 supplier for X-ray lasers is listed in the RP Photonics Buyer's Guide. Both manufacturers and distributors can be registered.
No products of suppliers with ad package are registered yet for X-ray lasers. These would have a company logo, a detailed product description, a product image, and also appear in the corresponding encyclopedia article.
Read our encyclopedia article on X-ray lasers!


This smoothie can actually provide another 10 grams of protein, for a total of 30+ grams of protein, if you use the same Silk Protein Nut Milk that I so enjoy. It's such a simple way to boost the amount of protein in my vegan diet. It also provides more calcium than dairy milk, and tastes great. I love non dairy milk, I never was a big fan of cow's milk, it always tasted kind of funny to me. But if you love it, feel free to use it in this recipe! This recipe is highly customizable, you can easily swap ingredients in and out and alter the amounts to make it just the way you like. More recipes need this room for creativity and personal preference, don't you think?
This recipe also has a special secret ingredient to further boost nutrition, and it's completely undetectable to taste. Can you guess what it is?
Note: If you're nervous about the cauliflower and don't trust that it doesn't have a funny taste, you can reverse the amounts and start with a 1/2 cup frozen cauliflower and the full cup of berries instead. Don't be afraid to try it! It's nice to have banana alternatives, even if just sometimes.
- Add all ingredients to blender and blend until smooth, adding water as needed to get things moving.
- Pour into bowl, top with toppings of choice if desired, and enjoy!


School library media centers should focus on motivating students, expanding access to technologies, and working with students with disabilities, recommends a report by Ruth V. Smalls, a professor of school information studies at Syracuse University.
The report , the first in a two-year study that will examine the impact of certified librarians on student achievement, collected more than 1,000 electronic surveys from elementary school librarians in New York state. Many librarians surveyed for the study said that much of the frustration they experienced in their jobs was due to the poor quality of facilities, a lack of technology, and from being limited to part-time employment.


Bakers allow an even distribution of heat while the small handles allow easy maneuvering into an oven or grill. These bakers have greater wall heights which increases the volume compared to the skillets. perfectly suited corn bread, skillet lasagna, and many other one pan dishes! This will easily fit into the oven and grill to enhance your culinary experience.
Carbon steel Baker made in 14 inches. Fit for a feast with a large family, it cooks enough everyone. These are faster to season than cast iron and a pleasure to use.
Due to the size, these bakers are made with thicker steel than the smaller sizes to increase heat distribution over the entire cooking surface. Wall height is ~2.25 inches, and the distance across the handles is ~17 inches. Note: due to the imperfect nature of handmade construction, this measurement can vary. If you need specific measurements to be met, feel free to contact me to discuss the possibilities!
Bakers may also be personalized to make the perfect gift.


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Sun 29 May, 2016, 9:00 PM (EDT)
John Isner vs. Andy Murray 2016 French Open Pick, Odds, Prediction
Murray vs. Isner
Sunday May 29, 2016, 9:00 PM (EDT)
Unknown Location Unknown Address
the Line: --- Over/Under:
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John Isner and Andy Murray square off early in the fourth round of the 2016 French Open.
John Isner is coming off a five set victory over Teymuraz Gabashvili that took 45 games to decide. In the victory, Isner won 75 percent of his first serve points and 49 percent of his second serve points. Isner continues to do what he does, as he's racked up 92 aces and 194 winners in his first three matches. Isner always gives himself a chance to win with his service game, but the fact he's had 66 or more unforced errors in two of his three matches is alarming. Still, Isner has won no less than 75 percent of his first serve points, putting the ultimate pressure on his opponent. Isner hopes to make his second grand slam quarterfinal appearance of his career and first since the 2011 US Open. This will be Isner's third straight grand slam fourth round appearance.
Andy Murray is coming off a straight sets win over Ivo Karlovic that took 30 games to decide. In the victory, Murray won 88 percent of his first serve points and 57 percent of his second serve points. Murray finally had his quick and easy match after playing 10 sets in his first two matches. It starts with the unforced errors, as Murray had just four in his third round victory. If Murray can play that clean tennis while still hitting his winners, he'll have a chance to win his first grand slam title since the 2013 Wimbledon. Murray looked extremely sharp against a top-30 player, much better than what we saw in the first two rounds. Murray has failed to make the quarterfinals of a grand slam just once since 2011.
These two have met five times in the past, and Isner has won all five meetings. The most recent match was in the 2015 Shanghai Masters. Isner has been able to win four sets against Murray. This will be the first time these two meet on clay.
Isner always has a shot to beat anybody due to his service game and power shots. He's played Murray tough in the past. However, Isner's power isn't as effective on clay, while Murray's athleticism and shotmaking translates well. You also have to take the head to head in consideration and the overall experience and success in grand slams.
This is going to be a long match, but I expect Murray to prevail like he has all tournament.


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Q: Ember set Component property in controller I have a Component, whose property I want to set in a Controller. I seem to be having problems using the computed alias to set it. Please see code below.
My Component is as follows:
 TM.MyModalComponent = Ember.Component.extend({
    totalWidth: '200px',
    widthStyle: function() {
        console.log(this.get('totalWidth'));
        return 'width:'+this.get('totalWidth');
    }.property('totalWidth'),
 });

I am trying to set the property totalWidth in a parent controller as follows:
modalWidth:Ember.computed.alias("myModalComponent.totalWidth"),
this.set("modalWidth",'100px');

It returns a undefined value when I try to console log it and errors with
Property set failed: object in path "myModalComponent" could not be found or was destroyed.
Any help will be much appreciated.
Thanks

A: Refer this working example on fiddle, http://jsfiddle.net/gXcfL/28/. Bind controller property to component's property in template.


text:
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Arts Bereavement Events Filming Leisure & Sport Parks Public Halls Putney School of Art & Design
Public Halls
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Contact Enable
Active Wandsworth
Active Wandsworth Strategy
Find a Sports Facility
London Youth Games
Sports Club and Coach Development
The Active Wandsworth Stratgey 2017-2022 was approved by the February 2018 Overview and Scrutiny Committee at Wandsworth Borough Council, and the public document is currently being designed and will appear on these pages once completed.
The main themes which the strategy covers are:
Motivate (Workforce) – This involves inspiring and upskilling individuals and organisations in the recruitment, training and deployment of staff. Including; volunteers, coaches, officials, administrators, walk leaders and instructors. Supporting these people to ensure more sport and physical opportunities are offered for everyone to participate in active travel, physical activity and sport.
Facilitate (Places) – This involves both investing in existing sport and physical activity facilities, maintaining existing one's and thinking differently about how we use non-traditional sporting venues. Protecting and improving the number and quality of places for active travel, active recreation and sport across the borough including the River Thames, highways, parks, playing fields, sports facilities, leisure centres, schools and other community buildings where physical activity and sport take place.
Participate (Participation) – This involves changing people's behaviour towards sport and physical activity. Changing the behaviour of those where physical activity is not part of their daily routine and supporting those that are already active to maintain a regular habit. It involves ensuring children and young people have an active and healthy start to life, understanding the barriers that inactive people face and helping to nourish, foster and grow those with a sporting talent to succeed
Advocate (Support) – In order for any strategy to be successful there is a need to be collaborative and innovative. There is no one single cure for inactivity, and this theme encourages partnership working so that activity can be provided by appropriately trained staff across a range of venues to meet the needs of the public. This theme also recognises the need to embrace technology, encouraging the use of apps and harnessing the power of elite sport to inspire. Research, insight and evaluation are also key to demonstrate and evidence the positive impact that sport and physical activity has.
"To enable Wandsworth to be the most active borough in London by 2022."
That's the vision of the current Active Wandsworth Strategy 2017-2022.
Download a copy of the Active Wandsworth Strategy 2017-2022.
Providing the opportunity for everyone to Get Active!
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There are few recipes that are common in almost all housholds and Lemon rice is one. Lemon flavored rice is a classic that is loved by kids and elders alike. For me the best tasting Lemon rice was served as Prasadam during the 10 day Ganesh Chaturthi celebrations in our locality. I am yet to recreate the magic of the tangy rice served as part of festival feast.
The tangy rice is infused with aromatic roasted spices, lentils and curry leaves. There are numerous ways to spice the Lemon rice but this particular dish tastes best when kept simple and left with the Lemon to take care of the rest.
Heat a teaspoon of oil and roast the peanuts, set aside.
Heat oil in a vessel, add the mustard seeds and as they begin to splutter, add the dal and allow them to turn slightly brown.
Add curry leaves, green chillies, red chillies, asafoetida and turmeric powder all one by one. (make sure not to burn the red chillies). Add roasted peanuts, combine well and add to the cooked rice.
Add salt and lemon juice and mix well. Serve with potato fry.
TIPS: I have used Sona Masuri. Ponni rice can also be used. The rice has to be completely cooled. I have used Idhayam (gingely) oil. I prefer Lemon rice to be tangy/sour. Adjust the juice to your family preference (taste and if needed add some more).
Serve with any curry of your choice. My husband believes that Lemon Rice and Fish Curry is the best combination after he had it at a restaurant in Florida.
easy dish, tastes great with chips or appalam.
Lemon rice looks delicious..happy to follow u..
My favourite dish!! Well made!!
That's a lovely rice you've got there! I love the color and the texture. Great pictures on the log as well!
I love Lemon rice and had it as a Prasad for our society Ganesh Chaturti celebration.. I was waiting for a authentic recipe.. Thanks for yours.. will try it soon.


Monday, April 24, 2017 5:30 PM Arbor Prep AWAY 2 Whitmore Lake High School HOME 15 Game Recap Trojans dominate at home beating the Gators of Arbor Prep 15-2. The Trojans head to Grosse Ile on Thursday and Ypsi Lincoln on Friday.
Wednesday, April 27, 2016 6:00 PM Whitmore Lake High School AWAY 18 Lake Fenton High School HOME 4 Game Recap Whitmore Lake LAX picked up a big win on Wednesday with a 18-4 victory on the road vs the CO-OP of Lake Fenton and Goodrich HS.


Relatives are by blood. Family is who you choose to call your own. For Mother's Day, Michaela, Lauren, and Cole surprised their mom, Nancie, and Troy with one of our family photo sessions. This was a fun and emotional moment, as they also took the time to remember their son and brother, Shane, who passed away less than a year ago. We were honored and humbled to be a part of this day.


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Cadett, Dōggie, Fran Zabelin - "Temple" [Vex Release] out now!
Cadett is the name chosen to give life to the artistic alter ego of Gabriel Nunes, a 24-year-old Minas Gerais native born and raised in the city of Governador Valadares. As a result of the sum of his love for electronic music and the fact that he is extremely charismatic and extroverted, he has been winning Brazilian hearts for three years, when through his studies he refined his essence reaching a harmoniously floating project between the strands of Deep House, Tech House, Melodic House & Techno.
The Dōggie project, started in 2017 by DJ and producer Douglas Borges, born in 1994 in the city of Ribeirão Preto – SP, since he was a child he has always had a taste for electronic music, but in 2016 he began studying and graduated from the course in musical production of Make Music Now (MkMN), in 2017 he started playing at various parties in Franca – SP where he currently resides.
Fran Zabelin is a Brazilian singer-songwriter who started her career 11 years ago. Born in a municipality in Minas Gerais, she has been gaining a relevant space in music due to her work with projects of various genres. Francyane has already done several musical social projects in her region with children and today she has projects with indie and rock bands and has been dedicating herself and looking for an emphasis on vocals for electronic production and intends to reach major labels and national and international DJs.
Chemical Disco & Gui and Me - "Howling" [Vex Release] out now!
HÜVE - "Love Got You" [Vex Release] out now!


text:
SC appoints ex-judge as panel chief to oversee payment of compensation to Maradu residents
The Supreme Court directed the state to pay ` 25 lakh to each of the factors who are being evicted.
Published: 29th September 2019 04:03 AM | Last Updated: 29th September 2019 04:03 AM | A+A A-
Justice K Balakrishnan Nair
KOCHI: The Supreme Court has appointed Justice K Balakrishnan Nair, a former judge of Kerala High Court, as the head of the committee to oversee the payment of compensation to Maradu flat owners and to work out the actual amount paid to each of them.
A division bench comprising Justice Arun Mishra and Justice S Ravindra Bhat had held that the builder, promoter, persons or officials responsible for the construction might also be associated with the exercise and the actual amount paid by owners may be ascertained by the committee. The court issued the order without prejudice to other proceedings which may be taken by the flat owners against those responsible for the construction for realising the compensation.
The Supreme Court also issued a notice to Alfa Ventures, Holy Faith Builders and Developers Pvt Ltd, Jain Housing and Construction Ltd and KP Varkey and Builders. The court made it clear that "we attach the properties held by the persons and their bank accounts."
The Supreme Court directed the state to pay Rs 25 lakh to each of the factors who are being evicted. The court also said the chief secretary need not be present for the next hearing to be held on October 25, if the assurance given to the court to demolish the buildings is complied with.
Justice K Balakrishnan Nair Supreme Court Maradu flats compensation


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