Moreover , dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network . To improve the detection performance , we utilized discrete wavelet transform as a pooling method , in which the functional brain network-based feature describing the relationship between fatigue and brain network organization . In the feature selection phase , a hybrid three layered feature selection method is presented , and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method . In this study , we improve the encoder by employing densely connected convolutional networks as they can strengthen feature extraction and facilitate gradient propagation especially on a small training set . We also present a novel multi-scale attention model which is employed to deal with the recognition of math symbols in different scales and restore the fine-grained details dropped by pooling operations . Validated on the CROHME competition task , the proposed method significantly outperforms the state-of-the-art methods with an expression recognition accuracy of 52.8 % on CROHME 2014 and 50.1 % on CROHME 2016 , by only using the official training dataset . Ineffective spatial – temporal motion feature pooling has been a fundamental bottleneck for human action recognition/detection for decades . Previous pooling schemes such as global , spatial – temporal pyramid , or human and object centric pooling fail to capture discriminative motion patterns because informative movements only occur in specific regions of the human body , that depend on the type of action being performed . Global ( holistic ) motion feature pooling methods therefore often result in an action representation with limited discriminative capability . To tackle these challenges , this paper proposes an efficient multiscale representation for word images , which is learned in an unsupervised manner using the spherical k-means algorithm . A pooling function is applied in a spatial grid to obtain a fixed-length vector of features , robust to small shifts in the image . Scale variability in handwritten data is also considered by using patches of various sizes in the encoding process . In the second stage , we design a network that drastically increases the equivalent receptive field . Large receptive fields can be achieved either by pooling at the expense of precision , increasing the kernel size of the convolutional filters at the expense of increasing the number of parameters , or by increasing the number of convolutional layers at the risk of encountering vanishing gradients during training . Our network design and corresponding receptive field for the subsequent stages ( t ≥ 2 ) is shown in Figure 2d . section : Efficient Grid Size Reduction Traditionally , convolutional networks used some pooling operation to decrease the grid size of the feature maps . In order to avoid a representational bottleneck , before applying maximum or average pooling the activation dimension of the network filters is expanded . The Stanford Natural Language Inference ( SNLI ) corpus snliemnlp2015 provides approximately 550 K hypothesis / premise pairs . Our baseline , the ESIM sequence model from Chen2017EnhancedLF , uses a biLSTM to encode the premise and hypothesis , followed by a matrix attention layer , a local inference layer , another biLSTM inference composition layer , and finally a pooling operation before the output layer . Overall , adding ELMo to the ESIM model improves accuracy by an average of 0.7 % across five random seeds . In this article , we propose spatio-temporal sampling detectors , where a set of circular masks over motion scenario are utilized to detect the motion dynamics . Two alternative mechanisms , Max-pooling and Sum-pooling , are used to extracting spatio-temporal descriptors from motion energy occupied by the circular masks . To improve the classification results , centroid kinematics is added to the feature vectors , where this feature contributes substantially to characterizing the motion pattern of an action . In this paper , a novel sparsity-based framework is proposed , which adopts the max pooling operation for HSI classification . Compared with the traditional sparsity-based frameworks using residual error , sparse vectors in our proposed framework are utilized to generate the feature vectors using max pooling operation . Experimental results demonstrate that our proposed framework can achieve the state-of-the-art classification performance . Our sequence descriptor includes cell , block , and subsequence descriptors . We use maximum pooling and a logistic reg ... Firstly , the expression features are implicitly extracted by means of continuous convolution . Then the extracted implicit features are subsampled by the maximum pooling method . Finally , the Softmax classifier is used to classify the facial expressions . While in this letter , we argue that the construction of these two links can be build simultaneously . The proposed algorithm starts from the construction of a complete similarity graph based on superpixels where the t-links and n-links have been embedded and hence the t-links and n-links can be easily obtained using the max pooling function and distance matrix respectively . This strategy not only makes the segmentation more accurate but also makes the method more robust to the selection of parameters . Current CNNs are increasingly deeper , data-hungrier and this makes their success tributary of the abundance of labeled training data . CNNs also rely on max/average pooling which reduces dimensionality of output layers and hence attenuates their sensitivity to the availability of labeled data . However , this process may dilute the information of upstream convolutional layers and thereby affect the discrimination power of the trained representations , especially when the learned categories are fine-grained . In this paper we develop a novel modeling framework , Dirichlet process active region ( DPAR ) , that addresses the deficiencies of the previous GP-based approaches . In particular , with a discretized representation of the environment , we can explicitly account for planar shifts via a max pooling step , and reduce the computational complexity of the statistical inference procedure compared with the GP-based approaches . The proposed algorithm was applied on two real pedestrian trajectory datasets collected using a 3D Velodyne Lidar , and showed 15 % improvement in prediction accuracy and 4.2 times reduction in computational time compared with a GP-based algorithm . The shared task includes two tracks : one for NER offset and entity classification ( track 1 ) and the other one for concept indexing ( track 2 ) . We developed a pipeline system based on deep learning methods for this shared task , specifically , a subsystem based on BERT ( Bidirectional Encoder Representations from Transformers ) for NER offset and entity classification and a subsystem based on Bpool ( Bi-LSTM with max/mean pooling ) for concept indexing . Evaluation conducted on the shared task data showed that our system achieves a micro-average F1-score of 0.9105 on track 1 and a microaverage F1-score of 0.8391 on track 2 . In image classification based on Bag-of-Features(BoF ) , the Locality-constrained Linear Coding ( LLC ) is a successful implementation , which is a more effective coding scheme compared with the traditional vector quantization(VQ ) coding . Although , to achieve the best performance , max pooling scheme is chosen in the SPM layer , much of the spatial information is still lost during the pooling step , because all the coded descriptors are given the same importance to obtain the final representation . In this paper , we propose a new scheme that makes full use of spatial structure information to readjust their relative weights red and thus give some descriptors more chances to appear in the final feature vector more than others . Image classification tests on three kinds of image datasets ( CIFAR-10 , CIFAR-100 , and SVHN ) were respectively conducted . Afterward , comparisons were made on experimental accuracy and speed obtained through three relatively popular pooling methods ( i.e. , dropout-pooling , maxout-pooling , and stochastic- pooling ) . Research results indicated that pooling model based on failure probability theory featured scientific derivation without the need for empirical parameters and presented the most accurate results in experiments on three kinds of image in training data and test data . subsection : Experiment setting Due to training time constraints , some parameters have been fixed to reasonable and empirical values , such as the size of convolutions ( pixels , 32 feature maps ) and the size of subsamplings ( pixels using max pooling ) . All layers use ReLU units , except of softmax being used in the output layer . Figure [ reference ] illustrates the architecture of the proposed system , which is based on schwenk2018filtering . As it can be seen , the encoder consists of a bidirectional LSTM , and our sentence embeddings are obtained by applying a max - pooling operation over its output . The resulting sentence embeddings are fed into the decoder in two ways : first , they are used to initialize the hidden and cell state of the decoder LSTM after applying a linear transformation to each , and second , they are concatenated to the input embeddings at every time step . The sub - windows can be overlapping or not ( Figure [ reference ] shows an overlapping configuration ) . The max - pooling operation shrinks the size of the feature map . This is controlled by the pooling size and the stride hyper - parameter , which corresponds to the horizontal and vertical increments at which pooling sub - windows are positioned . Inspired by this architecture , we introduce a faster version consisting of 4 convolutional layers . At every layer , a representation is computed by a max - pooling operation over the feature maps ( see Figure [ reference ] ) . The final representation concatenates representations at different levels of the input sentence . One simple way to ensure constant effort is to reduce the strides of the first two layer in the case of lower resolution input , or by simply removing the first pooling layer of the network . For this purpose we have performed the following three experiments : receptive field with stride and maximum pooling after the first layer . receptive field with stride and maximum pooling after the first layer . Convolutional and max pooling layers are used to process features down to a very low resolution . At each max pooling step , the network branches off and applies more convolutions at the original pre - pooled resolution . After reaching the lowest resolution , the network begins the top - down sequence of upsampling and combination of features across scales . The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map ( s ) . Specifically , the decoder uses pooling indices computed in the max - pooling step of the corresponding encoder to perform non - linear upsampling . This eliminates the need for learning to upsample . The new successive higher - order window representations then are fed into LSTM which is described below . A max - over - pooling or dynamic k - max pooling is often applied to feature maps after the convolution to select the most or the k - most important features . However , LSTM is specified for sequence input , and pooling will break such sequence organization due to the discontinuous selected features . ( a ) shows 16 examples of pixel - space reconstructions for different location samples throughout the network . The reconstructions are similar to the max pooling case , but as the pooling locations change they result in small local deformations of the visualized image . Despite the stochastic nature of the model , the multinomial distributions effectively capture the regularities of the data . It is the 1 - D version of the max - pooling module used in computer vision . Given a discrete input function , the max - pooling function of is defined as where is an offset constant . This very pooling module enabled us to train ConvNets deeper than 6 layers , where all others fail . Bilinear pooling has been recently proposed as a feature encoding layer , which can be used after the convolutional layers of a deep network , to improve performance in multiple vision tasks . Different from conventional global average pooling or fully connected layer , bilinear pooling gathers 2nd order information in a translation invariant fashion . However , a serious drawback of this family of pooling layers is their dimensionality explosion . Generally , these two tasks are coupled through two steps , 1 ) fusing the supervision by considering the contexts between scene labels and local captions , and 2 ) jointly optimizing semantically symmetric LSTM models . Particularly , in order to balance bias between dense captioning and scene recognition , a scene adaptive non-maximum suppression ( NMS ) method is proposed to emphasize the scene related regions in region proposal procedure , and a region-wise and category-wise weighted pooling method is proposed to avoid over attention on particular regions in local to global pooling procedure . For the model training and evaluation , scene labels are manually annotated for Visual Genome database . However , due to the scene appearance and the shooting position changing with time , such as distance , viewpoint , illumination , weather , season , traffic flow , etc . , it is a challenge to extract more distinguishing and representative image representation by overcoming multiple variables . Recently based on convolutional neural networks , many global pooling methods have been proposed and reached the cutting edge of the state of the art , but they ignore the correlation of local regions in different channels . Therefore we propose a self-learning attention global pooling model for feature maps and performs end-to-end fine-tuning training for contrastive loss through Siamese network . ( 1 ) We use image-level tags labeled on the objects in images to improve the saliency detection results . ( 2 ) We extend the global average pooling ( GAP ) to predict the salient object in complex images and use it as a layer in the deep convolutional network . ( 3 ) We conduct extensive experiments and the results show that the proposed method achieves state-of-the-art performance on mean absolutely error ( MAE ) , area under the ROC curve ( AUC ) , and F -measure index on the TBS dataset . Feature recalibration is a very effective strategy of further improving performance in deep networks . The commonly used global pooling operation will lose the information of distinguishing features , which requires additional fully connected layers to adjust the relationship between feature maps . In this paper , we propose a novel architectural unit that aims to recalibrate feature maps based on the discriminative feature information contained in each feature map . Finally , experimental results on UC-Merced and WHU-RS19 datasets demonstrate that fusing global and local features in a CNN using the proposed sparse representation framework can certainly improve the performance of classification using single kind of features . Moreover , the proposed global average pooling strategy is very effective to fuse local features select most representative features from convolutional layers of a CNN . Methods based on convolutional neural networks ( CNNs ) have proved to be state-of-the-art on the few datasets used to benchmark this task . However , such CNNs typically collapse spatial information via a Global Average Pooling operation . They do not perform any explicit treatment of spatial relationships of the higher-level features which emerge in the later stages of the network and which may correspond to facial parts or blemishes that are characteristic of age . Since precise annotations are time-consuming to produce , we first rely on bigger datasets built with cheaper image-level labels . We design a global pooling function to work with them and to recover latent information about spatial localization of objects . We then deal with usual object-level annotations and introduce several new modules to learn part-based representations . A special characteristic of our model is the bottleneck layers which enable us to retain its expressiveness while reducing the number of multiply-accumulate operations and the required storage . We introduce a modification of global weighted average pooling (GWAP)—global weighted output average pooling ( GWOAP ) . This paper demonstrates how they allow us to calculate class activation maps ( CAMs ) in order to indicate the most relevant input character image regions used by our CNN to identify a certain class . We first employ a Siamese network to generate high-resolution feature maps . Inspired by the Seed , Expand and Constrain principles [ 1 ] , we introduce the weighted global average pooling ( WGAP ) and a one-class cross entropy loss , to produce localization maps from image-level supervision . Then we construct a fully-connected conditional random field ( CRF ) model to constrain the boundaries of segmentations . Rather than inputing the generated words directly , the hidden state vector of the caption - LSTM after it has generated the last word in each caption is used to represent its content . Average - pooling is applied over the 5 hidden - state vectors , to obtain a vector representation for the image . The third input source is the textual knowledge which is mined from a large - scale knowledge base , the DBpedia . , we pay special attention to the first and the last Residual Units of the entire network . For the first Residual Unit ( that follows a stand - alone convolutional layer , conv ) , we adopt the first activation right after conv and before splitting into two paths ; for the last Residual Unit ( followed by average pooling and a fully - connected classifier ) , we adopt an extra activation right after its element - wise addition . These two special cases are the natural outcome when we obtain the pre - activation network via the modification procedure as shown in Fig . These RoIs further are fed into two sibling position sensitive RoI pooling layer in R - FCN module to produce class score maps and bounding box prediction maps . In the end of R - FCN , two global average pooling are applied on both class score maps and bounding box prediction maps respectively for aggregating the class scores and bounding box predictions . There are two major advantages that we adopt R - FCN over R - CNN . It is noted that after PSROIPooling , the resolution of feature maps becomes . Therefore , we use global average pooling for classification ( after pixel - wise max ) and box regression branches , and pixel - wise softmax on mask branch . subsection : Ground Truth and Loss Function [ reference ] or SENet - 50 [ reference ] . Both networks are cropped after the global average pooling layer , and an extra FC layer is added to reduce the output dimension to D F . We typically pick D F to be low - dimensional ( e.g. 128 or 256 ) , and do not see a significant drop in face recognition performance compared to using the original 2048 - D descriptors . [ reference ] ( a ) ) to predict the possible scales of faces given an input image of fixed size . The network is a half - channel version of ResNet - 18 with a global pooling at the end . The output of this network is a probability vector of dimensions , where is the predefined number of scales . However , our experiments show that these global descriptors are not representative enough for the challenging ADE20 K data . Therefore , different from global pooling in , we exploit the capability of global context information by different - region - based context aggregation via our pyramid scene parsing network . section : Pyramid Scene Parsing Network The proposed convolutional sentence model takes simple architectures such as ( essentially the same convolutional architecture as SENNA ) , which consists of a convolution layer and a max - pooling over the entire sentence for each feature map . This type of models , with local convolutions and a global pooling , essentially do a “ soft ” local template matching and is able to detect local features useful for a certain task . Since the sentence - level sequential order is inevitably lost in the global pooling , the model is incapable of modeling more complicated structures . Max - Avg Pooling ) , i.e. 0.5 * ( global average pooling + global max pooling ) , just before the final softmax layer . Comparisons between the models with and without This is in fact a “ global pooling ” operation , which is also investigated in several concurrent works . In a global average pooling is used to reduce the model size and also reduce overfitting ; in , a global average pooling is used on the testing stage after all fc layers to improve accuracy ; in , a global max pooling is used for weakly supervised object recognition . The global pooling operation corresponds to the traditional Bag - of - Words method . subsection : Training the Network We first propose to modify the convolutional blocks of VDCNN model by the usage of Temporal Depthwise Separable Convolutions ( TDSCs ) . Next , we reduce the number of fully connected layers using the Global Average Pooling ( GAP ) technique . The resulting proposed architecture is called Squeezed Very Deep Convolutional Neural Networks ( SVDCNN ) . Our system is built on top of widely used Caffe infrastructure . For the network architecture setup , we adopted the mlpconv layer and global averaged pooling scheme introduced in . DSN can be equipped with different types of loss functions , such as Softmax and SVM . Combining position information , our approach matches corner points more accurately than the conventional embedding approaches do . Corner pooling extracts information inside the bounding boxes onto the border . To make this information more aware at the corners , we design a cross-star deformable convolution network to conduct feature adaption . By detecting objects as paired keypoints , we eliminate the need for designing a set of anchor boxes commonly used in prior single - stage detectors . In addition to our novel formulation , we introduce corner pooling , a new type of pooling layer that helps the network better localize corners . Experiments show that CornerNet achieves a 42.2 % AP on MS COCO , outperforming all existing one - stage detectors . Similar to the offset loss , we only apply the losses at the ground - truth corner location . section : Corner Pooling As shown in Fig . 2 , there is often no local visual evidence for the presence of corners .
Moreover , dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network . To improve the detection performance , we utilized discrete wavelet transform as a pooling method , in which the functional brain network-based feature describing the relationship between fatigue and brain network organization . In the feature selection phase , a hybrid three layered feature selection method is presented , and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method .
In this study , we improve the encoder by employing densely connected convolutional networks as they can strengthen feature extraction and facilitate gradient propagation especially on a small training set . We also present a novel multi-scale attention model which is employed to deal with the recognition of math symbols in different scales and restore the fine-grained details dropped by pooling operations . Validated on the CROHME competition task , the proposed method significantly outperforms the state-of-the-art methods with an expression recognition accuracy of 52.8 % on CROHME 2014 and 50.1 % on CROHME 2016 , by only using the official training dataset .
Ineffective spatial – temporal motion feature pooling has been a fundamental bottleneck for human action recognition/detection for decades . Previous pooling schemes such as global , spatial – temporal pyramid , or human and object centric pooling fail to capture discriminative motion patterns because informative movements only occur in specific regions of the human body , that depend on the type of action being performed . Global ( holistic ) motion feature pooling methods therefore often result in an action representation with limited discriminative capability .
To tackle these challenges , this paper proposes an efficient multiscale representation for word images , which is learned in an unsupervised manner using the spherical k-means algorithm . A pooling function is applied in a spatial grid to obtain a fixed-length vector of features , robust to small shifts in the image . Scale variability in handwritten data is also considered by using patches of various sizes in the encoding process .
In the second stage , we design a network that drastically increases the equivalent receptive field . Large receptive fields can be achieved either by pooling at the expense of precision , increasing the kernel size of the convolutional filters at the expense of increasing the number of parameters , or by increasing the number of convolutional layers at the risk of encountering vanishing gradients during training . Our network design and corresponding receptive field for the subsequent stages ( t ≥ 2 ) is shown in Figure 2d .
section : Efficient Grid Size Reduction Traditionally , convolutional networks used some pooling operation to decrease the grid size of the feature maps . In order to avoid a representational bottleneck , before applying maximum or average pooling the activation dimension of the network filters is expanded .
The Stanford Natural Language Inference ( SNLI ) corpus snliemnlp2015 provides approximately 550 K hypothesis / premise pairs . Our baseline , the ESIM sequence model from Chen2017EnhancedLF , uses a biLSTM to encode the premise and hypothesis , followed by a matrix attention layer , a local inference layer , another biLSTM inference composition layer , and finally a pooling operation before the output layer . Overall , adding ELMo to the ESIM model improves accuracy by an average of 0.7 % across five random seeds .
In this article , we propose spatio-temporal sampling detectors , where a set of circular masks over motion scenario are utilized to detect the motion dynamics . Two alternative mechanisms , Max-pooling and Sum-pooling , are used to extracting spatio-temporal descriptors from motion energy occupied by the circular masks . To improve the classification results , centroid kinematics is added to the feature vectors , where this feature contributes substantially to characterizing the motion pattern of an action .
In this paper , a novel sparsity-based framework is proposed , which adopts the max pooling operation for HSI classification . Compared with the traditional sparsity-based frameworks using residual error , sparse vectors in our proposed framework are utilized to generate the feature vectors using max pooling operation . Experimental results demonstrate that our proposed framework can achieve the state-of-the-art classification performance .
Our sequence descriptor includes cell , block , and subsequence descriptors . We use maximum pooling and a logistic reg ...
Firstly , the expression features are implicitly extracted by means of continuous convolution . Then the extracted implicit features are subsampled by the maximum pooling method . Finally , the Softmax classifier is used to classify the facial expressions .
While in this letter , we argue that the construction of these two links can be build simultaneously . The proposed algorithm starts from the construction of a complete similarity graph based on superpixels where the t-links and n-links have been embedded and hence the t-links and n-links can be easily obtained using the max pooling function and distance matrix respectively . This strategy not only makes the segmentation more accurate but also makes the method more robust to the selection of parameters .
Current CNNs are increasingly deeper , data-hungrier and this makes their success tributary of the abundance of labeled training data . CNNs also rely on max/average pooling which reduces dimensionality of output layers and hence attenuates their sensitivity to the availability of labeled data . However , this process may dilute the information of upstream convolutional layers and thereby affect the discrimination power of the trained representations , especially when the learned categories are fine-grained .
In this paper we develop a novel modeling framework , Dirichlet process active region ( DPAR ) , that addresses the deficiencies of the previous GP-based approaches . In particular , with a discretized representation of the environment , we can explicitly account for planar shifts via a max pooling step , and reduce the computational complexity of the statistical inference procedure compared with the GP-based approaches . The proposed algorithm was applied on two real pedestrian trajectory datasets collected using a 3D Velodyne Lidar , and showed 15 % improvement in prediction accuracy and 4.2 times reduction in computational time compared with a GP-based algorithm .
The shared task includes two tracks : one for NER offset and entity classification ( track 1 ) and the other one for concept indexing ( track 2 ) . We developed a pipeline system based on deep learning methods for this shared task , specifically , a subsystem based on BERT ( Bidirectional Encoder Representations from Transformers ) for NER offset and entity classification and a subsystem based on Bpool ( Bi-LSTM with max/mean pooling ) for concept indexing . Evaluation conducted on the shared task data showed that our system achieves a micro-average F1-score of 0.9105 on track 1 and a microaverage F1-score of 0.8391 on track 2 .
In image classification based on Bag-of-Features(BoF ) , the Locality-constrained Linear Coding ( LLC ) is a successful implementation , which is a more effective coding scheme compared with the traditional vector quantization(VQ ) coding . Although , to achieve the best performance , max pooling scheme is chosen in the SPM layer , much of the spatial information is still lost during the pooling step , because all the coded descriptors are given the same importance to obtain the final representation . In this paper , we propose a new scheme that makes full use of spatial structure information to readjust their relative weights red and thus give some descriptors more chances to appear in the final feature vector more than others .
Image classification tests on three kinds of image datasets ( CIFAR-10 , CIFAR-100 , and SVHN ) were respectively conducted . Afterward , comparisons were made on experimental accuracy and speed obtained through three relatively popular pooling methods ( i.e. , dropout-pooling , maxout-pooling , and stochastic- pooling ) . Research results indicated that pooling model based on failure probability theory featured scientific derivation without the need for empirical parameters and presented the most accurate results in experiments on three kinds of image in training data and test data .
subsection : Experiment setting Due to training time constraints , some parameters have been fixed to reasonable and empirical values , such as the size of convolutions ( pixels , 32 feature maps ) and the size of subsamplings ( pixels using max pooling ) . All layers use ReLU units , except of softmax being used in the output layer .
Figure [ reference ] illustrates the architecture of the proposed system , which is based on schwenk2018filtering . As it can be seen , the encoder consists of a bidirectional LSTM , and our sentence embeddings are obtained by applying a max - pooling operation over its output . The resulting sentence embeddings are fed into the decoder in two ways : first , they are used to initialize the hidden and cell state of the decoder LSTM after applying a linear transformation to each , and second , they are concatenated to the input embeddings at every time step .
The sub - windows can be overlapping or not ( Figure [ reference ] shows an overlapping configuration ) . The max - pooling operation shrinks the size of the feature map . This is controlled by the pooling size and the stride hyper - parameter , which corresponds to the horizontal and vertical increments at which pooling sub - windows are positioned .
Inspired by this architecture , we introduce a faster version consisting of 4 convolutional layers . At every layer , a representation is computed by a max - pooling operation over the feature maps ( see Figure [ reference ] ) . The final representation concatenates representations at different levels of the input sentence .
One simple way to ensure constant effort is to reduce the strides of the first two layer in the case of lower resolution input , or by simply removing the first pooling layer of the network . For this purpose we have performed the following three experiments : receptive field with stride and maximum pooling after the first layer . receptive field with stride and maximum pooling after the first layer .
Convolutional and max pooling layers are used to process features down to a very low resolution . At each max pooling step , the network branches off and applies more convolutions at the original pre - pooled resolution . After reaching the lowest resolution , the network begins the top - down sequence of upsampling and combination of features across scales .
The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map ( s ) . Specifically , the decoder uses pooling indices computed in the max - pooling step of the corresponding encoder to perform non - linear upsampling . This eliminates the need for learning to upsample .
The new successive higher - order window representations then are fed into LSTM which is described below . A max - over - pooling or dynamic k - max pooling is often applied to feature maps after the convolution to select the most or the k - most important features . However , LSTM is specified for sequence input , and pooling will break such sequence organization due to the discontinuous selected features .
( a ) shows 16 examples of pixel - space reconstructions for different location samples throughout the network . The reconstructions are similar to the max pooling case , but as the pooling locations change they result in small local deformations of the visualized image . Despite the stochastic nature of the model , the multinomial distributions effectively capture the regularities of the data .
It is the 1 - D version of the max - pooling module used in computer vision . Given a discrete input function , the max - pooling function of is defined as where is an offset constant . This very pooling module enabled us to train ConvNets deeper than 6 layers , where all others fail .
Bilinear pooling has been recently proposed as a feature encoding layer , which can be used after the convolutional layers of a deep network , to improve performance in multiple vision tasks . Different from conventional global average pooling or fully connected layer , bilinear pooling gathers 2nd order information in a translation invariant fashion . However , a serious drawback of this family of pooling layers is their dimensionality explosion .
Generally , these two tasks are coupled through two steps , 1 ) fusing the supervision by considering the contexts between scene labels and local captions , and 2 ) jointly optimizing semantically symmetric LSTM models . Particularly , in order to balance bias between dense captioning and scene recognition , a scene adaptive non-maximum suppression ( NMS ) method is proposed to emphasize the scene related regions in region proposal procedure , and a region-wise and category-wise weighted pooling method is proposed to avoid over attention on particular regions in local to global pooling procedure . For the model training and evaluation , scene labels are manually annotated for Visual Genome database .
However , due to the scene appearance and the shooting position changing with time , such as distance , viewpoint , illumination , weather , season , traffic flow , etc . , it is a challenge to extract more distinguishing and representative image representation by overcoming multiple variables . Recently based on convolutional neural networks , many global pooling methods have been proposed and reached the cutting edge of the state of the art , but they ignore the correlation of local regions in different channels . Therefore we propose a self-learning attention global pooling model for feature maps and performs end-to-end fine-tuning training for contrastive loss through Siamese network .
( 1 ) We use image-level tags labeled on the objects in images to improve the saliency detection results . ( 2 ) We extend the global average pooling ( GAP ) to predict the salient object in complex images and use it as a layer in the deep convolutional network . ( 3 ) We conduct extensive experiments and the results show that the proposed method achieves state-of-the-art performance on mean absolutely error ( MAE ) , area under the ROC curve ( AUC ) , and F -measure index on the TBS dataset .
Feature recalibration is a very effective strategy of further improving performance in deep networks . The commonly used global pooling operation will lose the information of distinguishing features , which requires additional fully connected layers to adjust the relationship between feature maps . In this paper , we propose a novel architectural unit that aims to recalibrate feature maps based on the discriminative feature information contained in each feature map .
Finally , experimental results on UC-Merced and WHU-RS19 datasets demonstrate that fusing global and local features in a CNN using the proposed sparse representation framework can certainly improve the performance of classification using single kind of features . Moreover , the proposed global average pooling strategy is very effective to fuse local features select most representative features from convolutional layers of a CNN .
Methods based on convolutional neural networks ( CNNs ) have proved to be state-of-the-art on the few datasets used to benchmark this task . However , such CNNs typically collapse spatial information via a Global Average Pooling operation . They do not perform any explicit treatment of spatial relationships of the higher-level features which emerge in the later stages of the network and which may correspond to facial parts or blemishes that are characteristic of age .
Since precise annotations are time-consuming to produce , we first rely on bigger datasets built with cheaper image-level labels . We design a global pooling function to work with them and to recover latent information about spatial localization of objects . We then deal with usual object-level annotations and introduce several new modules to learn part-based representations .
A special characteristic of our model is the bottleneck layers which enable us to retain its expressiveness while reducing the number of multiply-accumulate operations and the required storage . We introduce a modification of global weighted average pooling (GWAP)—global weighted output average pooling ( GWOAP ) . This paper demonstrates how they allow us to calculate class activation maps ( CAMs ) in order to indicate the most relevant input character image regions used by our CNN to identify a certain class .
We first employ a Siamese network to generate high-resolution feature maps . Inspired by the Seed , Expand and Constrain principles [ 1 ] , we introduce the weighted global average pooling ( WGAP ) and a one-class cross entropy loss , to produce localization maps from image-level supervision . Then we construct a fully-connected conditional random field ( CRF ) model to constrain the boundaries of segmentations .
Rather than inputing the generated words directly , the hidden state vector of the caption - LSTM after it has generated the last word in each caption is used to represent its content . Average - pooling is applied over the 5 hidden - state vectors , to obtain a vector representation for the image . The third input source is the textual knowledge which is mined from a large - scale knowledge base , the DBpedia .
, we pay special attention to the first and the last Residual Units of the entire network . For the first Residual Unit ( that follows a stand - alone convolutional layer , conv ) , we adopt the first activation right after conv and before splitting into two paths ; for the last Residual Unit ( followed by average pooling and a fully - connected classifier ) , we adopt an extra activation right after its element - wise addition . These two special cases are the natural outcome when we obtain the pre - activation network via the modification procedure as shown in Fig .
These RoIs further are fed into two sibling position sensitive RoI pooling layer in R - FCN module to produce class score maps and bounding box prediction maps . In the end of R - FCN , two global average pooling are applied on both class score maps and bounding box prediction maps respectively for aggregating the class scores and bounding box predictions . There are two major advantages that we adopt R - FCN over R - CNN .
It is noted that after PSROIPooling , the resolution of feature maps becomes . Therefore , we use global average pooling for classification ( after pixel - wise max ) and box regression branches , and pixel - wise softmax on mask branch . subsection : Ground Truth and Loss Function
[ reference ] or SENet - 50 [ reference ] . Both networks are cropped after the global average pooling layer , and an extra FC layer is added to reduce the output dimension to D F . We typically pick D F to be low - dimensional ( e.g. 128 or 256 ) , and do not see a significant drop in face recognition performance compared to using the original 2048 - D descriptors .
[ reference ] ( a ) ) to predict the possible scales of faces given an input image of fixed size . The network is a half - channel version of ResNet - 18 with a global pooling at the end . The output of this network is a probability vector of dimensions , where is the predefined number of scales .
However , our experiments show that these global descriptors are not representative enough for the challenging ADE20 K data . Therefore , different from global pooling in , we exploit the capability of global context information by different - region - based context aggregation via our pyramid scene parsing network . section : Pyramid Scene Parsing Network
The proposed convolutional sentence model takes simple architectures such as ( essentially the same convolutional architecture as SENNA ) , which consists of a convolution layer and a max - pooling over the entire sentence for each feature map . This type of models , with local convolutions and a global pooling , essentially do a “ soft ” local template matching and is able to detect local features useful for a certain task . Since the sentence - level sequential order is inevitably lost in the global pooling , the model is incapable of modeling more complicated structures .
Max - Avg Pooling ) , i.e. 0.5 * ( global average pooling + global max pooling ) , just before the final softmax layer . Comparisons between the models with and without
This is in fact a “ global pooling ” operation , which is also investigated in several concurrent works . In a global average pooling is used to reduce the model size and also reduce overfitting ; in , a global average pooling is used on the testing stage after all fc layers to improve accuracy ; in , a global max pooling is used for weakly supervised object recognition . The global pooling operation corresponds to the traditional Bag - of - Words method . subsection : Training the Network
We first propose to modify the convolutional blocks of VDCNN model by the usage of Temporal Depthwise Separable Convolutions ( TDSCs ) . Next , we reduce the number of fully connected layers using the Global Average Pooling ( GAP ) technique . The resulting proposed architecture is called Squeezed Very Deep Convolutional Neural Networks ( SVDCNN ) .
Our system is built on top of widely used Caffe infrastructure . For the network architecture setup , we adopted the mlpconv layer and global averaged pooling scheme introduced in . DSN can be equipped with different types of loss functions , such as Softmax and SVM .
Combining position information , our approach matches corner points more accurately than the conventional embedding approaches do . Corner pooling extracts information inside the bounding boxes onto the border . To make this information more aware at the corners , we design a cross-star deformable convolution network to conduct feature adaption .
By detecting objects as paired keypoints , we eliminate the need for designing a set of anchor boxes commonly used in prior single - stage detectors . In addition to our novel formulation , we introduce corner pooling , a new type of pooling layer that helps the network better localize corners . Experiments show that CornerNet achieves a 42.2 % AP on MS COCO , outperforming all existing one - stage detectors .
Similar to the offset loss , we only apply the losses at the ground - truth corner location . section : Corner Pooling As shown in Fig . 2 , there is often no local visual evidence for the presence of corners .
A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition Pose Adaptive Motion Feature Pooling for Human Action Analysis Query-by-example word spotting using multiscale features and classification in the space of representation differences Convolutional Pose Machines Rethinking the Inception Architecture for Computer Vision Deep contextualized word representations Computational architecture of a visual model for biological motions segregation A Novel Sparsity-Based Framework Using Max Pooling Operation for Hyperspectral Image Classification Discriminative human action classification using locality-constrained linear coding Design and Experiment of Facial Expression Recognition Method Based on LBP and CNN Arbitrary Body Segmentation With a Novel Graph Cuts-Based Algorithm Human Action Recognition with Deep Temporal Pyramids Predictive Modeling of Pedestrian Motion Patterns with Bayesian Nonparametrics A Deep Learning-Based System for PharmaCoNER Image Classification Based on Weight Adjustment before Feature Pooling Improving pooling method for regularization of convolutional networks based on the failure probability density Deep Learning For Smile Recognition Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings Brain tumor segmentation with Deep Neural Networks Supervised Learning of Universal Sentence Representations from Natural Language Inference Data Rethinking the Inception Architecture for Computer Vision Stacked Hourglass Networks for Human Pose Estimation SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation A C-LSTM Neural Network for Text Classification Stochastic Pooling for Regularization of Deep Convolutional Neural Networks Character-level Convolutional Networks for Text Classification MoNet: Moments Embedding Network MUCH: Mutual Coupling Enhancement of Scene Recognition and Dense Captioning Self-learning Attention Global Pooling Based Image Representation for Visual Place Recognition Salient object detection with side information Feature Recalibration in Deep Learning via Depthwise Squeeze and Refinement Operations Fusing Deep Local and Global Features for Remote Sensing Image Scene Classification Apparent Age Estimation with Relational Networks Designing Deep Architectures for Visual Understanding A high-performance CNN method for offline handwritten Chinese character recognition and visualization Dense High-Resolution Siamese Network for Weakly-Supervised Change Detection Image Captioning and Visual Question Answering Based on Attributes and External Knowledge Identity Mappings in Deep Residual Networks Detecting Faces Using Region-based Fully Convolutional Networks Fused Text Segmentation Networks for Multi-oriented Scene Text Detection GhostVLAD for set-based face recognition Recurrent Scale Approximation for Object Detection in CNN Pyramid Scene Parsing Network Convolutional Neural Network Architectures for Matching Natural Language Sentences Dual Path Networks Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition Squeezed Very Deep Convolutional Neural Networks for Text Classification Deeply-Supervised Nets CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection CornerNet: Detecting Objects as Paired Keypoints
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ICIP 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NAACL-HLT BioNLP-OST@EMNLP-IJNCLP ICONIP (3) ArXiv ACL Medical Image Anal. EMNLP 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV IEEE Transactions on Pattern Analysis and Machine Intelligence ArXiv ICLR NIPS ACM Multimedia IGARSS CRV ICSAI IEEE Transactions on Pattern Analysis and Machine Intelligence ECCV ArXiv 2018 24th International Conference on Pattern Recognition (ICPR) ACCV 2017 IEEE International Conference on Computer Vision (ICCV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS NIPS IEEE Transactions on Pattern Analysis and Machine Intelligence ICANN AISTATS ECCV
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