In this paper , we focus on the feature pooling methods for scene character recognition . We research three kinds of pooling methods : the average ( sum ) pooling , max pooling and weighted-based pooling methods . Specifically , various feature pooling methods are introduced , their merits and demerits are studied , and existing problems are discussed . In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling . We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets ; they are also easy to implement , and can be applied within various deep neural network architectures . These benefits come with only a light increase in computational overhead during training and a very modest increase in the number of model parameters . This way , a series of representations can be derived that trade-off generalization against specificity . Thereby we show a connection between NBNN classification and different pooling strategies , where , in contrast to traditional pooling schemes that perform spatial pooling of the features , pooling is performed in feature space . Moreover , rather than picking a single partitioning , we propose to combine them in a multi kernel framework . Tweet-pooling schemes were created based on mention/reply relationships between tweets and Twitter users , with several ( non-networked ) established methods also tested as a comparison . Results show that pooling tweets using network information gives better topic coherence and clustering performance than other pooling schemes , on the majority of datasets tested . Our findings contribute to an improved methodology for topic modeling with Twitter content . To automatically learn this weight , the output of the pooling function is fed into a SVM classifier and they are trained jointly to minimize a shape classification loss . Experimental results on several standard shape datasets demonstrate the effectiveness of the proposed learned pooling function , which can achieve considerable improvements compared with both BoCF and BoSCP . [ reference ] . Given a 30×30×30 TDF voxel grid of a local 3D patch around an interest point , we use eight convolutional layers ( each with a rectified linear unit activation function for nonlinearity ) and a pooling layer to compute a 512 - dimensional feature representation , which serves as the feature descriptor . Since the dimensions of the initial input voxel grid are small , we only include one layer of pooling to avoid a substantial loss of information . Convolution parameters are shown in Fig . The model and variational approximation are each a diagonal Gaussian distribution with all mean and log variance parameters given by a network composed of three dense layers with ReLU activations and units . The statistic network determining the mean and log variance parameters of posterior over context variables is composed of three dense layers before and after pooling , each with units with Rectified Linear Unit ( ReLU ) activations . Figure [ reference ] shows The non - contextual word representation for a single word is computed by using a gated combination of fixed , pre - trained word vectors with learned character - based embeddings . We compute using a single - layer convolutional neural network with convolutional filters of width followed by a - pooling operation over time Seo2017 , Weissenborn2017 . The formal definition of this combination is given in Eq . We adopt the Unet for attribute transform due to its good tradeoff between efficiency and reconstruction ability . In general , the Unet architecture involves an encoder subnetwork and a decoder subnetwork , then skip connection and pooling operation are further introduced to exploit multi - scale information . As for attribute transform , we design a 10 - layer Unet , which includes 5 convolution layers for encoding and another 5 convolution layers for decoding . paragraph : Ablation Study for PSPNet To evaluate PSPNet , we conduct experiments with several settings , including pooling types of max and average , pooling with just one global feature or four - level features , with and without dimension reduction after the pooling operation and before concatenation . As listed in Table [ reference ] , average pooling works better than max pooling in all settings . Especially , we take advantages of wavelet transformations and invariant moments to obtain the key features as well as reduce dimensions of feature data based on the cognitive rules of human brains . Furthermore , we improve usual pooling methods , e.g. max pooling and average pooling , and propose the weighted pooling method to reduce dimensions with no effect on accuracy , which let storage requirement and recognition time greatly decrease . The simulation results show that the proposed method does better than some typical and nearly-proposed algorithms in balancing the accuracy and running time . With this reformulation , ORLTM employs a stochastic optimization algorithm to learn the tensor low-rank structure alternatively for online updating . To capture the final tensors , ORLTM uses an average pooling operation on folded tensors in all modes . We also provide the analysis regarding computational complexity , memory cost , and convergence . ( 1 ) We demonstrate the advantage of using images with a properly large size as input to CNN instead of the conventionally resized one . ( 2 ) We benchmark the performance of different CNN layers improved by average/max pooling on the feature maps . Our evaluation and observation confirm that the Conv5 descriptor yields very competitive accuracy under such a pooling strategy . The integration of these approaches results in a high performance average ( max ) pooling-based video event recognition . Compared to the average ( max ) pooling combined with the state-of-the-art normalization methods and fine-tuned support vector machine classification , the proposed processing scheme improves the event recognition accuracy in terms of mean average precision over the Columbia consumer video and unstructured social activity attribute datasets , where achieves a pretty comparable result on UCF101 and ActivityNet datasets . For overcoming this defect , a weighted average pooling algorithm is proposed in this paper , the different weights are assigned to the abstract features from different local image regions in the pooling operation , so as to reflect its different roles in face recognition . The weighted average pooling algorithm is applied to the FaceNet network , and a face recognition algorithm based on the improved FaceNet model is proposed . The simulation experiments show that the proposed face recognition algorithm has higher recognition accuracy than the existing face recognition methods based on deep learning . To effectively extract the complementary information concealed within these predictions , we develop a novel pooling mechanism which accounts for competition among contestants : a stacking paradigm integrating conditional logit regression and log-likelihood-ratio-based forecast selection . Empirical results using data related to horseracing events demonstrate that : ( i ) base model strength and diversity are important when combining model-based predictions for CEs ; ( ii ) average-based pooling , commonly employed elsewhere , may not be appropriate for CEs ( because average-based pooling exclusively focuses on strength ) ; and ( iii ) the proposed stacking ensemble provides statistically and economically accurate forecasts . These results have important implications for regulators of betting markets associated with CEs and in particular for the accurate assessment of market efficiency . At present , the vast majority of GCNs use a neighborhood aggregation framework to learn a continuous and compact vector , then performing a pooling operation to generalize graph embedding for the classification task . These approaches have two disadvantages in the graph classification task : (1)when only the largest sub-graph structure ( k-hop neighbor ) is used for neighborhood aggregation , a large amount of early-stage information is lost during the graph convolution step ; ( 2 ) simple average/sum pooling or max pooling utilized , which loses the characteristics of each node and the topology between nodes . In this paper , we propose a novel framework called , dual attention graph convolutional networks ( DAGCN ) to address these problems . In this paper , we focus on the feature pooling methods for scene character recognition . We research three kinds of pooling methods : the average ( sum ) pooling , max pooling and weighted-based pooling methods . Specifically , various feature pooling methods are introduced , their merits and demerits are studied , and existing problems are discussed . We evaluate our method on various benchmarks for generic action , fine-grained action and gesture recognition . Results show that rank pooling brings an absolute improvement of 7 - 10 average pooling baseline . At the same time , rank pooling is compatible with and complementary to several appearance and local motion based methods and features , such as improved trajectories and deep learning features . This paper extends the algorithm to the vocoder-free TTS and propose a GAN-based training algorithm using low-frequency-resolution amplitude spectra to overcome the difficulty in modeling complicated distribution of the high-dimensional spectra . In the proposed algorithm , amplitude spectra are transformed into low-frequency-resolution amplitude spectra by applying an average pooling function along with a frequency axis ; then the GAN-based distribution compensation is performed in the low-frequency-resolution domain . Because the low-frequency-resolution amplitude spectra approximately emulate filter banks , the proposed algorithm is expected to improve synthetic speech quality by reducing differences in spectral envelopes of natural and synthetic speech . This paper describes a model that we submitted to W-NUT 2016 Shared task # 1 : Geolocation Prediction in Twitter . Our model classifies a tweet or a user to a city using a simple neural networks structure with fully-connected layers and average pooling processes . From the findings of previous geolocation prediction approaches , we integrated various user metadata along with message texts and trained the model with them . This method densely extracted 4 , 096 dimensional ConvNet features at the penultimate layer of VGG - Nets pre - trained on ImageNet . These features from different scales ( smallest image side ) were aggregated by average pooling . Then , these averaged features from two networks ( “ Net - D ” containing 16 layers and “ Net - E ” containing 19 layers ) were further fused by stacking . For example , [ reference ] aggregates local descriptors ( RootSIFT [ reference ] ) extracted from face crops using Fisher Vector [ reference ] ( FV ) encoding to obtain a single descriptor per face track . Since the success of deep learning in image - based face recognition [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] , simple strategies for face descriptor aggregation prevailed , such as average - and max - pooling [ reference ][ reference ] . It is based on the NetVLAD [ reference ] layer which implements an encoding similar to VLAD encoding [ reference ] , while being differentiable and thus fully - trainable . NetVLAD has been shown to outperform average and max pooling for the same vector dimensionality , which makes it perfectly suited for our task . Here we provide a brief overview of NetVLAD ( for full details please refer to [ reference ] ) , followed by our improvement , GhostVLAD . Learnt GhostVLAD aggregation . Using the GhostVLAD aggregation layer ( with G = 0 i.e. equivalent to NetVLAD ) together with our set - based training framework strongly outperforms the standard average - pooling approach , regardless of whether training is done with non - degraded images ( SE - GV - 2 , row 8 vs SE , rows 3 and 4 ) , degraded images ( SE - GV - 2 , row 9 vs SE - 2 , row 5 ) , or if a different feature extractor architecture ( ResNet - 50 ) is used ( Res - GV - 2 , row 6 vs Res , row 2 ) . Using 256 - D vs 128 - D face descriptors as inputs to GhostVLAD , while keeping the same dimensionality of the final template representation ( 128 - D ) , achieves better results ( rows 9 vs 7 ) , so we use 256 - D in all latter experiments . Our inference model converts the output hidden vectors of BiLSTMs to the fixed - length vector with pooling operations and puts it into the final classifier to determine the overall inference class . Particularly , in addition to using mean pooling and max pooling similarly to ESIM DBLP : conf / acl / ChenZLWJI17 , we propose to use weighted pooling based on external knowledge to obtain a fixed - length vector as in Equation ( [ reference ] ) ( [ reference ] ) . In our experiments , we regard the function as a 1 - layer feed - forward neural network with ReLU activation function . After the enhancement of local inference , another Bi - LSTM layer is used to capture local inference information and their context for inference composition . Instead of summation adopted by Parikh et al . , ESIM proposes to compute both max and average pooling and feeds the concatenate fixed length vector to the final classifier : a fully connected multi - layer perceptron . Figure [ reference ] shows a high - level view of the ESIM architecture , where the bottom LSTM1 layer of Figure [ reference ] is the input encoding layer , CNNs possess several advantages over recurrent models , including increased parallelism and better scaling to long sequences such as those often seen with character - level language data . Convolutional models for sequence processing have been more successful when combined with RNN layers in a hybrid architecture Lee2016 , because traditional max - and average - pooling approaches to combining convolutional features across timesteps assume time invariance and hence can not make full use of large - scale sequence order information . We present quasi - recurrent neural networks for neural sequence modeling . The proposed encoding mechanism yields a richer representation for both premise and hypothesis by taking the history of each other into account . Using a max or average pooling over the independent and dependent readings does not further improve our model . This was expected since dependent reading produces more promising and relevant encodings . Finally , we convert and to fixed - length vectors with pooling , and . As shown in Equations [ reference ] and [ reference ] , we employ both max and average pooling and describe the overall inference relationship with concatenation of their outputs . subsection : Classification It down - weights face images with blur , occlusion , improper exposure and extreme poses . Table 1 shows that the network achieves higher accuracy than the average pooling baseline in the verification and identification tasks . Cascaded two attention blocks - Content - aware aggregation . 7 ( b ) show that our NAN significantly reduces the error of the baseline CNN + AvePool . This again suggests that in the presence of large face variances , the widely used strategies such as average - pooling aggregation and the pairwise distance computation are far from optimal . In such cases , our learned NAN model is clearly more powerful , and the aggregated feature representation by it is more favorable for the video face recognition task . On IJB - A dataset , QAN significantly outperforms the state - of - the - art algorithm NAN by 10.81 % at 0.001 FPR , 4.5 % at 0.01 FPR and 2.12 % at FPR=0.1 , as shown in Table [ reference ] . Compared with average pooling baseline , QAN reduces false negative rate at above three FPRs by 29.32 % , 6.45 % and 7.91 % . Our experiments on the two tasks show that QAN is robust for set - to - set recognition . Possible applications of automatic facial expression recognition include better transcription of videos , movie or advertisement recommendations , detection of pain in telemedicine etc . Traditional convolutional neural networks ( CNNs ) that use convolutional layers , max or average pooling and fully connected layers are considered to capture only first - order statistics . Second - order statistics such as covariance are considered to be better regional descriptors than first - order statistics such as mean or maximum . ( see Figure [ reference ] ) . We can use two parallel stride 2 blocks : and . is a pooling layer ( either average or maximum pooling ) the activation , both of them are stride the filter banks of which are concatenated as in figure [ reference ] . section : Inception - v2 However in applications with rich user behaviors , features are often contained with variable - length list of ids , e.g. , searched terms or watched videos in YouTube recommender system . These models often transform corresponding list of embedding vectors into a fixed - length vector via sum / average pooling , which causes loss of information . The proposed DIN tackles it by adaptively learning the representation vector w.r.t . The pooling operation is the key to image quality assessment ( IQA ) , but the traditional pooling treats local quality map equally . In this paper , we propose a novel method named adaptive pooling for image quality assessment , which explicitly considers importance of different local regions . The adaptive pooling operation assigns different weights to local quality map according to the variance of local regions . The proposed method is verified on two challenging IQA databases ( CSIQ and TID 2008 databases ) , and the results demonstrate that it achieves better results than the state-of-the-art methods . For video sequences , the feature maps are extracted by a set of Spatial Temporal Convolutional Layers ( STCLs ) . The feature maps are then split into sub-regions by an Adaptive Region Pooling Layer ( ARPL ) . The feature vectors of each sub-region are labelled as one class . AICN enhances the adaptiveness of model . By using the adaptive region pooling layer and the intra-frame classifier , AICN is adaptive to frames with different resolutions and is easier to be applied on other scenes . AICN evaluates the abnormality of frames based on the intra-frame classification results . Specifically , we enhance the entire feature hierarchy with accurate localization signals in lower layers by bottom-up path augmentation , which shortens the information path between lower layers and topmost feature . We present adaptive feature pooling , which links feature grid and all feature levels to make useful information in each level propagate directly to following proposal subnetworks . A complementary branch capturing different views for each proposal is created to further improve mask prediction . It is achieved by learning an attribute-to-word co-occurrence prior to impose a label inconsistency distortion over the ℓ1 based coding regularizer , such that the descriptor codes can maximally preserve the image semantic similarity . Second , we propose an adaptive feature pooling scheme over “ superpixels ” rather than over fixed spatial pyramids , named Geometric Consistency Pooling ( GCP ) . As an effect , local descriptors enjoying good geometric consistency are pooled together to ensure a more precise spatial layouts embedding in BoF. Both of our phases are unsupervised , which differ from the existing works in supervised dictionary learning , sparse coding and feature pooling . Most loss layers compute pixel-wise cost between feature maps and ground truths , ignoring spatial layouts and interactions between neighboring pixels with same object category , and thus networks can not be effectively sensitive to intra-class connections . Stride by stride , our method firstly conducts adaptive pooling filter operating over predicted feature maps , aiming to merge predicted distributions over a small group of neighboring pixels with same category , and then computes cost between the merged distribution vector and their category label . Such design can make groups of neighboring predictions from same category involved into estimations on predicting correctness with respect to their category , and hence train networks to be more sensitive to regional connections between adjacent pixels based on their categories . We solve overfitting by using the gradient norm clipping and the cosine annealing learning rate schedule with restarts , which have a significant impact on the performance of our deep neural network . More importantly , adaptive pooling layer is used in our models . With this modification , models are able to adapt to images of any size . We develop a graph convolutional network ( GCN ) framework to integrate heterogeneous brain networks . Particularly , an adaptive pooling scheme is designed , catering to the modal structural diversity and sharing the advantages of locality , loyalty and likely as in standard convolutional networks . The experimental results demonstrate that our method achieves state-of-the-art prediction results , and validates the advantages of the utilization of multi-modal brain networks in that , more modals are always at least as good as the best modal , if not better . Considering the fact that logo only occupies a small portion of an image , we apply Faster R-CNN to detect logo proposals first , and then use a two-step pooling strategy with adaptive weight to obtain an accurate global signature . The adaptive weighted pooling method can effectively balance the recall and precision of proposals by incorporating the probability of each proposal being a logo . Experimental results show that the proposed method interprets the similarity between query and database image more accurately and achieves state of the art performance . It is a requirement to find well-photographed and attractive photos from a large number of images , but image aesthetic quality assessment ( IAQA ) is challenging because of the subjectivity of aesthetic criteria . In this paper , we propose an adaptive pooling model for IAQA based on convolutional neural network ( CAP ) which consists of three parts : feature extraction , feature adaptive pooling , and obtaining quality scores . Through experimental comparison , we choose ResNet50 network as the image feature extraction network . The OverFeat paper computes convolutional features from an image pyramid for classification , localization , and detection . Adaptively - sized pooling ( SPP ) on shared convolutional feature maps is developed for efficient region - based object detection and semantic segmentation . Fast R - CNN enables end - to - end detector training on shared convolutional features and shows compelling accuracy and speed . We have recently seen a revival of attention given to convolutional neural networks ( CNNs ) due to their high performance for large - scale visual recognition tasks . The architecture of CNNs is relatively simple and consists of successive layers organized in a hierarchical fashion ; each layer involves convolutions with learned filters followed by a pointwise non - linearity and a downsampling operation called “ feature pooling ” . The resulting image representation has been empirically observed to be invariant to image perturbations and to encode complex visual patterns , which are useful properties for visual recognition . In this paper , we propose a top-down computational model for goal-driven saliency detection based on a coding-based classification framework . It consists of four successive steps : feature extraction , descriptor coding , local pooling and saliency prediction . In the step of local pooling , we investigate the effect of multi-scale contextual information for saliency detection and find that there exists an optimal contextual scale to achieve the patch-level feature presentation . The local sketches are captured by the local maxima of Gabor responses , where the local maximum pooling accounts for shape deformations in objects . The local textures are captured by the local averages of Gabor filter responses , where the local average pooling extracts texture information for appearances . The selection of local sketch variables and local texture variables can be accomplished by a projection pursuit type of learning process , where both types of variables can be compared and merged within a common framework . For SDR images , perceptual pooling strategies have improved correlation of image quality assessment ( IQA ) algorithms with subjective scores . The primary contributions of this paper are : ( 1 ) integrating local information-based pooling strategies in the TMQI IQA algorithm , ( 2 ) measuring image naturalness by using mean-subtracted contrast-normalized pixels , and ( 3 ) testing the proposed methods on JPEG compressed tone-mapped images and tone-mapped images for SDR displays using subjective scores . In deep convolutional neural networks ( CNNs ) , local pooling operation is a key building block to effectively downsize feature maps for reducing computation cost as well as increasing robustness against input variation . There are several types of pooling operation , such as average/max-pooling , from which one has to be manually selected for building CNNs . We propose a new predictive learning framework , Heterogeneous Convolutional Neural Network ( HCNN ) , that represents EHRs as graphs with heterogeneous attributes ( e.g. diagnoses , procedures , and medication ) , and then develop a novel deep learning methodology for risk prediction of multiple comorbid diseases . The main innovation of the framework is that it defines the distance between the heterogeneous attributes of the graph representation extracted from the EHR and develops an appropriate learning infrastructure that is a composition of sparse convolutional layers and local pooling steps that match with the local structure of the space of the heterogeneous attributes . As a result , the new method is capable of capturing features about the relationships between heterogeneous attributes of the graphs . In particular , the increased complexity in characterizing the visual patterns of diverse human actions with both the spatial and temporal variations imposes more challenges to the conventional sparse coding scheme . In this paper , we propose an enhanced sparse coding scheme through learning discriminative dictionary and optimizing the local pooling strategy . Localizing when and where a specific action happens in realistic videos is another challenging task . However , BIM has the following three problems : a very heavy computational cost due to dense input , a disputable pooling operation in modeling relations of the visual cortex , and blind feature selection in a feed-forward framework . To solve these problems , we develop an enhanced BIM ( EBIM ) , which removes uninformative input by imposing sparsity constraints , utilizes a novel local weighted pooling operation with stronger physiological motivations , and applies a feedback procedure that selects effective features for combination . Empirical studies on the CalTech5 database and CalTech101 database show that EBIM is more effective and efficient than BIM . For example , Jarrett et al . [ 11 ] claim that the nonlinearity f ( x ) = |tanh ( x ) | works particularly well with their type of contrast normalization followed by local average pooling on the Caltech - 101 dataset . However , on this dataset the primary concern is preventing overfitting , so the effect they are observing is different from the accelerated ability to fit the training set which we report when using ReLUs . If we set s = z , we obtain traditional local pooling as commonly employed in CNNs . If we set s < z , we obtain overlapping pooling .
In this paper , we focus on the feature pooling methods for scene character recognition . We research three kinds of pooling methods : the average ( sum ) pooling , max pooling and weighted-based pooling methods . Specifically , various feature pooling methods are introduced , their merits and demerits are studied , and existing problems are discussed .
In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling . We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets ; they are also easy to implement , and can be applied within various deep neural network architectures . These benefits come with only a light increase in computational overhead during training and a very modest increase in the number of model parameters .
This way , a series of representations can be derived that trade-off generalization against specificity . Thereby we show a connection between NBNN classification and different pooling strategies , where , in contrast to traditional pooling schemes that perform spatial pooling of the features , pooling is performed in feature space . Moreover , rather than picking a single partitioning , we propose to combine them in a multi kernel framework .
Tweet-pooling schemes were created based on mention/reply relationships between tweets and Twitter users , with several ( non-networked ) established methods also tested as a comparison . Results show that pooling tweets using network information gives better topic coherence and clustering performance than other pooling schemes , on the majority of datasets tested . Our findings contribute to an improved methodology for topic modeling with Twitter content .
To automatically learn this weight , the output of the pooling function is fed into a SVM classifier and they are trained jointly to minimize a shape classification loss . Experimental results on several standard shape datasets demonstrate the effectiveness of the proposed learned pooling function , which can achieve considerable improvements compared with both BoCF and BoSCP .
[ reference ] . Given a 30×30×30 TDF voxel grid of a local 3D patch around an interest point , we use eight convolutional layers ( each with a rectified linear unit activation function for nonlinearity ) and a pooling layer to compute a 512 - dimensional feature representation , which serves as the feature descriptor . Since the dimensions of the initial input voxel grid are small , we only include one layer of pooling to avoid a substantial loss of information . Convolution parameters are shown in Fig .
The model and variational approximation are each a diagonal Gaussian distribution with all mean and log variance parameters given by a network composed of three dense layers with ReLU activations and units . The statistic network determining the mean and log variance parameters of posterior over context variables is composed of three dense layers before and after pooling , each with units with Rectified Linear Unit ( ReLU ) activations . Figure [ reference ] shows
The non - contextual word representation for a single word is computed by using a gated combination of fixed , pre - trained word vectors with learned character - based embeddings . We compute using a single - layer convolutional neural network with convolutional filters of width followed by a - pooling operation over time Seo2017 , Weissenborn2017 . The formal definition of this combination is given in Eq .
We adopt the Unet for attribute transform due to its good tradeoff between efficiency and reconstruction ability . In general , the Unet architecture involves an encoder subnetwork and a decoder subnetwork , then skip connection and pooling operation are further introduced to exploit multi - scale information . As for attribute transform , we design a 10 - layer Unet , which includes 5 convolution layers for encoding and another 5 convolution layers for decoding .
paragraph : Ablation Study for PSPNet To evaluate PSPNet , we conduct experiments with several settings , including pooling types of max and average , pooling with just one global feature or four - level features , with and without dimension reduction after the pooling operation and before concatenation . As listed in Table [ reference ] , average pooling works better than max pooling in all settings .
Especially , we take advantages of wavelet transformations and invariant moments to obtain the key features as well as reduce dimensions of feature data based on the cognitive rules of human brains . Furthermore , we improve usual pooling methods , e.g. max pooling and average pooling , and propose the weighted pooling method to reduce dimensions with no effect on accuracy , which let storage requirement and recognition time greatly decrease . The simulation results show that the proposed method does better than some typical and nearly-proposed algorithms in balancing the accuracy and running time .
With this reformulation , ORLTM employs a stochastic optimization algorithm to learn the tensor low-rank structure alternatively for online updating . To capture the final tensors , ORLTM uses an average pooling operation on folded tensors in all modes . We also provide the analysis regarding computational complexity , memory cost , and convergence .
( 1 ) We demonstrate the advantage of using images with a properly large size as input to CNN instead of the conventionally resized one . ( 2 ) We benchmark the performance of different CNN layers improved by average/max pooling on the feature maps . Our evaluation and observation confirm that the Conv5 descriptor yields very competitive accuracy under such a pooling strategy .
The integration of these approaches results in a high performance average ( max ) pooling-based video event recognition . Compared to the average ( max ) pooling combined with the state-of-the-art normalization methods and fine-tuned support vector machine classification , the proposed processing scheme improves the event recognition accuracy in terms of mean average precision over the Columbia consumer video and unstructured social activity attribute datasets , where achieves a pretty comparable result on UCF101 and ActivityNet datasets .
For overcoming this defect , a weighted average pooling algorithm is proposed in this paper , the different weights are assigned to the abstract features from different local image regions in the pooling operation , so as to reflect its different roles in face recognition . The weighted average pooling algorithm is applied to the FaceNet network , and a face recognition algorithm based on the improved FaceNet model is proposed . The simulation experiments show that the proposed face recognition algorithm has higher recognition accuracy than the existing face recognition methods based on deep learning .
To effectively extract the complementary information concealed within these predictions , we develop a novel pooling mechanism which accounts for competition among contestants : a stacking paradigm integrating conditional logit regression and log-likelihood-ratio-based forecast selection . Empirical results using data related to horseracing events demonstrate that : ( i ) base model strength and diversity are important when combining model-based predictions for CEs ; ( ii ) average-based pooling , commonly employed elsewhere , may not be appropriate for CEs ( because average-based pooling exclusively focuses on strength ) ; and ( iii ) the proposed stacking ensemble provides statistically and economically accurate forecasts . These results have important implications for regulators of betting markets associated with CEs and in particular for the accurate assessment of market efficiency .
At present , the vast majority of GCNs use a neighborhood aggregation framework to learn a continuous and compact vector , then performing a pooling operation to generalize graph embedding for the classification task . These approaches have two disadvantages in the graph classification task : (1)when only the largest sub-graph structure ( k-hop neighbor ) is used for neighborhood aggregation , a large amount of early-stage information is lost during the graph convolution step ; ( 2 ) simple average/sum pooling or max pooling utilized , which loses the characteristics of each node and the topology between nodes . In this paper , we propose a novel framework called , dual attention graph convolutional networks ( DAGCN ) to address these problems .
In this paper , we focus on the feature pooling methods for scene character recognition . We research three kinds of pooling methods : the average ( sum ) pooling , max pooling and weighted-based pooling methods . Specifically , various feature pooling methods are introduced , their merits and demerits are studied , and existing problems are discussed .
We evaluate our method on various benchmarks for generic action , fine-grained action and gesture recognition . Results show that rank pooling brings an absolute improvement of 7 - 10 average pooling baseline . At the same time , rank pooling is compatible with and complementary to several appearance and local motion based methods and features , such as improved trajectories and deep learning features .
This paper extends the algorithm to the vocoder-free TTS and propose a GAN-based training algorithm using low-frequency-resolution amplitude spectra to overcome the difficulty in modeling complicated distribution of the high-dimensional spectra . In the proposed algorithm , amplitude spectra are transformed into low-frequency-resolution amplitude spectra by applying an average pooling function along with a frequency axis ; then the GAN-based distribution compensation is performed in the low-frequency-resolution domain . Because the low-frequency-resolution amplitude spectra approximately emulate filter banks , the proposed algorithm is expected to improve synthetic speech quality by reducing differences in spectral envelopes of natural and synthetic speech .
This paper describes a model that we submitted to W-NUT 2016 Shared task # 1 : Geolocation Prediction in Twitter . Our model classifies a tweet or a user to a city using a simple neural networks structure with fully-connected layers and average pooling processes . From the findings of previous geolocation prediction approaches , we integrated various user metadata along with message texts and trained the model with them .
This method densely extracted 4 , 096 dimensional ConvNet features at the penultimate layer of VGG - Nets pre - trained on ImageNet . These features from different scales ( smallest image side ) were aggregated by average pooling . Then , these averaged features from two networks ( “ Net - D ” containing 16 layers and “ Net - E ” containing 19 layers ) were further fused by stacking .
For example , [ reference ] aggregates local descriptors ( RootSIFT [ reference ] ) extracted from face crops using Fisher Vector [ reference ] ( FV ) encoding to obtain a single descriptor per face track . Since the success of deep learning in image - based face recognition [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] , simple strategies for face descriptor aggregation prevailed , such as average - and max - pooling [ reference ][ reference ] .
It is based on the NetVLAD [ reference ] layer which implements an encoding similar to VLAD encoding [ reference ] , while being differentiable and thus fully - trainable . NetVLAD has been shown to outperform average and max pooling for the same vector dimensionality , which makes it perfectly suited for our task . Here we provide a brief overview of NetVLAD ( for full details please refer to [ reference ] ) , followed by our improvement , GhostVLAD .
Learnt GhostVLAD aggregation . Using the GhostVLAD aggregation layer ( with G = 0 i.e. equivalent to NetVLAD ) together with our set - based training framework strongly outperforms the standard average - pooling approach , regardless of whether training is done with non - degraded images ( SE - GV - 2 , row 8 vs SE , rows 3 and 4 ) , degraded images ( SE - GV - 2 , row 9 vs SE - 2 , row 5 ) , or if a different feature extractor architecture ( ResNet - 50 ) is used ( Res - GV - 2 , row 6 vs Res , row 2 ) . Using 256 - D vs 128 - D face descriptors as inputs to GhostVLAD , while keeping the same dimensionality of the final template representation ( 128 - D ) , achieves better results ( rows 9 vs 7 ) , so we use 256 - D in all latter experiments .
Our inference model converts the output hidden vectors of BiLSTMs to the fixed - length vector with pooling operations and puts it into the final classifier to determine the overall inference class . Particularly , in addition to using mean pooling and max pooling similarly to ESIM DBLP : conf / acl / ChenZLWJI17 , we propose to use weighted pooling based on external knowledge to obtain a fixed - length vector as in Equation ( [ reference ] ) ( [ reference ] ) . In our experiments , we regard the function as a 1 - layer feed - forward neural network with ReLU activation function .
After the enhancement of local inference , another Bi - LSTM layer is used to capture local inference information and their context for inference composition . Instead of summation adopted by Parikh et al . , ESIM proposes to compute both max and average pooling and feeds the concatenate fixed length vector to the final classifier : a fully connected multi - layer perceptron . Figure [ reference ] shows a high - level view of the ESIM architecture , where the bottom LSTM1 layer of Figure [ reference ] is the input encoding layer ,
CNNs possess several advantages over recurrent models , including increased parallelism and better scaling to long sequences such as those often seen with character - level language data . Convolutional models for sequence processing have been more successful when combined with RNN layers in a hybrid architecture Lee2016 , because traditional max - and average - pooling approaches to combining convolutional features across timesteps assume time invariance and hence can not make full use of large - scale sequence order information . We present quasi - recurrent neural networks for neural sequence modeling .
The proposed encoding mechanism yields a richer representation for both premise and hypothesis by taking the history of each other into account . Using a max or average pooling over the independent and dependent readings does not further improve our model . This was expected since dependent reading produces more promising and relevant encodings .
Finally , we convert and to fixed - length vectors with pooling , and . As shown in Equations [ reference ] and [ reference ] , we employ both max and average pooling and describe the overall inference relationship with concatenation of their outputs . subsection : Classification
It down - weights face images with blur , occlusion , improper exposure and extreme poses . Table 1 shows that the network achieves higher accuracy than the average pooling baseline in the verification and identification tasks . Cascaded two attention blocks - Content - aware aggregation .
7 ( b ) show that our NAN significantly reduces the error of the baseline CNN + AvePool . This again suggests that in the presence of large face variances , the widely used strategies such as average - pooling aggregation and the pairwise distance computation are far from optimal . In such cases , our learned NAN model is clearly more powerful , and the aggregated feature representation by it is more favorable for the video face recognition task .
On IJB - A dataset , QAN significantly outperforms the state - of - the - art algorithm NAN by 10.81 % at 0.001 FPR , 4.5 % at 0.01 FPR and 2.12 % at FPR=0.1 , as shown in Table [ reference ] . Compared with average pooling baseline , QAN reduces false negative rate at above three FPRs by 29.32 % , 6.45 % and 7.91 % . Our experiments on the two tasks show that QAN is robust for set - to - set recognition .
Possible applications of automatic facial expression recognition include better transcription of videos , movie or advertisement recommendations , detection of pain in telemedicine etc . Traditional convolutional neural networks ( CNNs ) that use convolutional layers , max or average pooling and fully connected layers are considered to capture only first - order statistics . Second - order statistics such as covariance are considered to be better regional descriptors than first - order statistics such as mean or maximum .
( see Figure [ reference ] ) . We can use two parallel stride 2 blocks : and . is a pooling layer ( either average or maximum pooling ) the activation , both of them are stride the filter banks of which are concatenated as in figure [ reference ] . section : Inception - v2
However in applications with rich user behaviors , features are often contained with variable - length list of ids , e.g. , searched terms or watched videos in YouTube recommender system . These models often transform corresponding list of embedding vectors into a fixed - length vector via sum / average pooling , which causes loss of information . The proposed DIN tackles it by adaptively learning the representation vector w.r.t .
The pooling operation is the key to image quality assessment ( IQA ) , but the traditional pooling treats local quality map equally . In this paper , we propose a novel method named adaptive pooling for image quality assessment , which explicitly considers importance of different local regions . The adaptive pooling operation assigns different weights to local quality map according to the variance of local regions . The proposed method is verified on two challenging IQA databases ( CSIQ and TID 2008 databases ) , and the results demonstrate that it achieves better results than the state-of-the-art methods .
For video sequences , the feature maps are extracted by a set of Spatial Temporal Convolutional Layers ( STCLs ) . The feature maps are then split into sub-regions by an Adaptive Region Pooling Layer ( ARPL ) . The feature vectors of each sub-region are labelled as one class .
AICN enhances the adaptiveness of model . By using the adaptive region pooling layer and the intra-frame classifier , AICN is adaptive to frames with different resolutions and is easier to be applied on other scenes . AICN evaluates the abnormality of frames based on the intra-frame classification results .
Specifically , we enhance the entire feature hierarchy with accurate localization signals in lower layers by bottom-up path augmentation , which shortens the information path between lower layers and topmost feature . We present adaptive feature pooling , which links feature grid and all feature levels to make useful information in each level propagate directly to following proposal subnetworks . A complementary branch capturing different views for each proposal is created to further improve mask prediction .
It is achieved by learning an attribute-to-word co-occurrence prior to impose a label inconsistency distortion over the ℓ1 based coding regularizer , such that the descriptor codes can maximally preserve the image semantic similarity . Second , we propose an adaptive feature pooling scheme over “ superpixels ” rather than over fixed spatial pyramids , named Geometric Consistency Pooling ( GCP ) . As an effect , local descriptors enjoying good geometric consistency are pooled together to ensure a more precise spatial layouts embedding in BoF. Both of our phases are unsupervised , which differ from the existing works in supervised dictionary learning , sparse coding and feature pooling .
Most loss layers compute pixel-wise cost between feature maps and ground truths , ignoring spatial layouts and interactions between neighboring pixels with same object category , and thus networks can not be effectively sensitive to intra-class connections . Stride by stride , our method firstly conducts adaptive pooling filter operating over predicted feature maps , aiming to merge predicted distributions over a small group of neighboring pixels with same category , and then computes cost between the merged distribution vector and their category label . Such design can make groups of neighboring predictions from same category involved into estimations on predicting correctness with respect to their category , and hence train networks to be more sensitive to regional connections between adjacent pixels based on their categories .
We solve overfitting by using the gradient norm clipping and the cosine annealing learning rate schedule with restarts , which have a significant impact on the performance of our deep neural network . More importantly , adaptive pooling layer is used in our models . With this modification , models are able to adapt to images of any size .
We develop a graph convolutional network ( GCN ) framework to integrate heterogeneous brain networks . Particularly , an adaptive pooling scheme is designed , catering to the modal structural diversity and sharing the advantages of locality , loyalty and likely as in standard convolutional networks . The experimental results demonstrate that our method achieves state-of-the-art prediction results , and validates the advantages of the utilization of multi-modal brain networks in that , more modals are always at least as good as the best modal , if not better .
Considering the fact that logo only occupies a small portion of an image , we apply Faster R-CNN to detect logo proposals first , and then use a two-step pooling strategy with adaptive weight to obtain an accurate global signature . The adaptive weighted pooling method can effectively balance the recall and precision of proposals by incorporating the probability of each proposal being a logo . Experimental results show that the proposed method interprets the similarity between query and database image more accurately and achieves state of the art performance .
It is a requirement to find well-photographed and attractive photos from a large number of images , but image aesthetic quality assessment ( IAQA ) is challenging because of the subjectivity of aesthetic criteria . In this paper , we propose an adaptive pooling model for IAQA based on convolutional neural network ( CAP ) which consists of three parts : feature extraction , feature adaptive pooling , and obtaining quality scores . Through experimental comparison , we choose ResNet50 network as the image feature extraction network .
The OverFeat paper computes convolutional features from an image pyramid for classification , localization , and detection . Adaptively - sized pooling ( SPP ) on shared convolutional feature maps is developed for efficient region - based object detection and semantic segmentation . Fast R - CNN enables end - to - end detector training on shared convolutional features and shows compelling accuracy and speed .
We have recently seen a revival of attention given to convolutional neural networks ( CNNs ) due to their high performance for large - scale visual recognition tasks . The architecture of CNNs is relatively simple and consists of successive layers organized in a hierarchical fashion ; each layer involves convolutions with learned filters followed by a pointwise non - linearity and a downsampling operation called “ feature pooling ” . The resulting image representation has been empirically observed to be invariant to image perturbations and to encode complex visual patterns , which are useful properties for visual recognition .
In this paper , we propose a top-down computational model for goal-driven saliency detection based on a coding-based classification framework . It consists of four successive steps : feature extraction , descriptor coding , local pooling and saliency prediction . In the step of local pooling , we investigate the effect of multi-scale contextual information for saliency detection and find that there exists an optimal contextual scale to achieve the patch-level feature presentation .
The local sketches are captured by the local maxima of Gabor responses , where the local maximum pooling accounts for shape deformations in objects . The local textures are captured by the local averages of Gabor filter responses , where the local average pooling extracts texture information for appearances . The selection of local sketch variables and local texture variables can be accomplished by a projection pursuit type of learning process , where both types of variables can be compared and merged within a common framework .
For SDR images , perceptual pooling strategies have improved correlation of image quality assessment ( IQA ) algorithms with subjective scores . The primary contributions of this paper are : ( 1 ) integrating local information-based pooling strategies in the TMQI IQA algorithm , ( 2 ) measuring image naturalness by using mean-subtracted contrast-normalized pixels , and ( 3 ) testing the proposed methods on JPEG compressed tone-mapped images and tone-mapped images for SDR displays using subjective scores .
In deep convolutional neural networks ( CNNs ) , local pooling operation is a key building block to effectively downsize feature maps for reducing computation cost as well as increasing robustness against input variation . There are several types of pooling operation , such as average/max-pooling , from which one has to be manually selected for building CNNs .
We propose a new predictive learning framework , Heterogeneous Convolutional Neural Network ( HCNN ) , that represents EHRs as graphs with heterogeneous attributes ( e.g. diagnoses , procedures , and medication ) , and then develop a novel deep learning methodology for risk prediction of multiple comorbid diseases . The main innovation of the framework is that it defines the distance between the heterogeneous attributes of the graph representation extracted from the EHR and develops an appropriate learning infrastructure that is a composition of sparse convolutional layers and local pooling steps that match with the local structure of the space of the heterogeneous attributes . As a result , the new method is capable of capturing features about the relationships between heterogeneous attributes of the graphs .
In particular , the increased complexity in characterizing the visual patterns of diverse human actions with both the spatial and temporal variations imposes more challenges to the conventional sparse coding scheme . In this paper , we propose an enhanced sparse coding scheme through learning discriminative dictionary and optimizing the local pooling strategy . Localizing when and where a specific action happens in realistic videos is another challenging task .
However , BIM has the following three problems : a very heavy computational cost due to dense input , a disputable pooling operation in modeling relations of the visual cortex , and blind feature selection in a feed-forward framework . To solve these problems , we develop an enhanced BIM ( EBIM ) , which removes uninformative input by imposing sparsity constraints , utilizes a novel local weighted pooling operation with stronger physiological motivations , and applies a feedback procedure that selects effective features for combination . Empirical studies on the CalTech5 database and CalTech101 database show that EBIM is more effective and efficient than BIM .
For example , Jarrett et al . [ 11 ] claim that the nonlinearity f ( x ) = |tanh ( x ) | works particularly well with their type of contrast normalization followed by local average pooling on the Caltech - 101 dataset . However , on this dataset the primary concern is preventing overfitting , so the effect they are observing is different from the accelerated ability to fit the training set which we report when using ReLUs .
If we set s = z , we obtain traditional local pooling as commonly employed in CNNs . If we set s < z , we obtain overlapping pooling .
Feature Pooling in Scene Character Recognition: A Comprehensive Study Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree The Pooled NBNN Kernel: Beyond Image-to-Class and Image-to-Image Network-Based Pooling for Topic Modeling on Microblog Content Bag of Shape Features with a learned pooling function for shape recognition 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions Towards a Neural Statistician Dynamic Integration of Background Knowledge in Neural NLU Systems Deep Identity-aware Transfer of Facial Attributes Pyramid Scene Parsing Network Face Recognition Using Invariance with a Single Training Sample Online Robust Low-Rank Tensor Modeling for Streaming Data Analysis On the Large-Scale Transferability of Convolutional Neural Networks Hierarchical Concept Score Postprocessing and Concept-Wise Normalization in CNN-Based Video Event Recognition Face Recognition Based on Improved FaceNet Model A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction DAGCN: Dual Attention Graph Convolutional Networks Feature Pooling in Scene Character Recognition: A Comprehensive Study Rank Pooling for Action Recognition Vocoder-free text-to-speech synthesis incorporating generative adversarial networks using low-/multi-frequency STFT amplitude spectra A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter Deep Label Distribution Learning With Label Ambiguity GhostVLAD for set-based face recognition Neural Natural Language Inference Models Enhanced with External Knowledge Attention Boosted Sequential Inference Model Quasi-Recurrent Neural Networks DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference Neural Aggregation Network for Video Face Recognition Quality Aware Network for Set to Set Recognition Covariance Pooling for Facial Expression Recognition Rethinking the Inception Architecture for Computer Vision Deep Interest Network for Click-Through Rate Prediction Image Quality Assessment via Adaptive Pooling An Intra-Frame Classification Network for Video Anomaly Detection and Localization Video Anomaly Detection and Localization Based on an Adaptive Intra-Frame Classification Network Path Aggregation Network for Instance Segmentation Weakly supervised sparse coding with geometric consistency pooling Locally Adaptive Learning Loss for Semantic Image Segmentation Classification of blood cancer images using a convolutional neural networks ensemble Integrating Heterogeneous Brain Networks for Predicting Brain Disease Conditions Logo Retrieval Using Logo Proposals and Adaptive Weighted Pooling An Adaptive Pooling Model for Aesthetic Quality Assessment Based on Convolutional Neural Network Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Convolutional Kernel Networks Top-Down Saliency by Multi-scale Contextual Pooling Learning mixed templates for object recognition Visual attention guided quality assessment of Tone-Mapped images using scene statistics Global Feature Guided Local Pooling HCNN: Heterogeneous Convolutional Neural Networks for Comorbid Risk Prediction with Electronic Health Records Fast human action classification and VOI localization with enhanced sparse coding Enhanced biologically inspired model ImageNet classification with deep convolutional neural networks
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CSPS SPIRE 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) PAKDD (Workshops) CSPS NUT@COLING IEEE Transactions on Image Processing ACCV ACL ArXiv ICLR NAACL-HLT 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) KDD CISP-BMEI CVPR MICCAI (4) IEEE Transactions on Pattern Analysis and Machine Intelligence NIPS PCM CVPR ICIP ICCV CHASE CVPR CACM
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