The Middle Block learns to preserve global-context semantic information , long-distance spatial information and relationships , and different feature channels ’ information and dependencies . It is different from other road segmentation methods which lose spatial information , such as those using dilated convolution and multiscale feature fusion to record local-context semantic information . The Middle Block consists of three important steps : ( 1 ) forming a feature resource pool to gather high-order global spatial information ; ( 2 ) selecting a feature weight distribution , enabling each pixel position to obtain complementary features according to its own needs ; and ( 3 ) inversely mapping the intermediate output feature encoding to the size of the input image by expanding the number of channels of the intermediate output feature . With the help of these high-level representations , we use global residual learning to improve reconstruction quality in the second phase . In order to reduce cumbersome computation , between two phases we introduce dilated convolution which enlarges receptive field without increasing additional parameters . Comprehensive experiments show that ETRN has a significant improvement in both quantitation and visual perception . In this paper , we propose a novel saliency object detection method , which predicts the saliency by incorporating both pixel-level and region-level predictions . First , in order to alleviate the scale-space problem , modified dilated convolution layers and short connections are integrated into the FCN model , and the pixel-level saliency maps is generated by a pixel-wise salient classifier . Then , we employ a superpixel based manifold learning algorithm to obtain a better boundary of salient object , by which a region-level saliency map with clearer object boundary is generated . The dilated convolution operation is used to extract the correlation between the predicted variable and correlational variables . The features extracted by dilated convolution operation and historical data of predicted variable are input into LSTM to obtain the desired multi-step prediction result . Furthermore , cross-correlation analyses ( CCA ) are applied to calculate the reasonable maximum prediction steps of chaotic time series . These models lack a hierarchical structure that can aid them in dissecting the input structure at different levels to allow flow of context between sentences . In this paper , we propose a hierarchical neural network comprising of Bi-LSTMs , Dilated Convolution operation , Capsules and Conditional Random Field ( CRF ) to understand the discourse/ abstract structure and predict next probable label by using label history . We have performed experiments on 3 publicly available datasets through which we have demonstrated that our model has achieved state-of-art performance on these datasets . The distance-view transformation is newly proposed to overcome the low density of top-view data of lidar . In addition , a dilated convolution structure is proposed to expand the receptive field of a convolutional neural network . The proposed network improves the accuracy of segmentation . The transformed images of height , red , green , and blue are input to the CNN . A dilated convolution structure is newly proposed to improve the learning accuracy by expanding the receptive field of CNN . The experimental results using the KITTI data set are finally presented to show the usefulness of the proposed method . Recently , deep-convolutional-neural-networks based methods have achieved promising results in the monocular depth estimation field . Specifically , the framework that combines the multi-scale features extracted by the dilated convolution based block ( atrous spatial pyramid pooling , ASPP ) has gained the significant improvement in the dense labeling task . However , the discretized and predefined dilation rates can not capture the continuous context information that differs in diverse scenes and easily introduce the grid artifacts in depth estimation . subsubsection : Simplifying WaveNet A defining feature of WaveNet is its use of dilated convolution to increase the receptive field exponentially with the number of layers . We evaluate models with varying receptive field sizes and number of layers to test our hypothesis that a shallow network with a small receptive field may solve the problem satisfactorily since mel spectrograms are a much closer representation of the waveform than linguistic features and already capture long - term dependencies across frames . However , the current proposed deep learning based restoration methods do not take the multi - scale information into account . In this paper , we propose a dilated convolution based inception module to learn multi - scale information and design a deep network for single image super - resolution . Different dilated convolution learns different scale feature , then the inception module concatenates all these features to fuse multi - scale information . In order to increase the reception field of our network to catch more contextual information , we cascade multiple inception modules to constitute a deep network to conduct single image super - resolution . Our proposed new inception module contains multiple different scale dilated convolution that makes it can learn multi - scale image information . Furthermore , we cascade multiple dilated convolution based inception modules to constitute a deep network for single image SR . In short , the contributions of this work are mainly in three aspects : 1 ) we proposed a dilated convolution based inception module , which can learn multi - scale information with only single scale image input ; 2 ) we design a novel deep network with the proposed dilated convolution based inception module for single image SR . We refer the reader to for more information about the dilated convolution . Dilated Convolution based Inception Module : To make full use of the image multi - scale information , Szegedy et al . proposed an inception module for image classification . As shown in Fig . We replace the last max - pooling layer by the SPP layer , and the last fc layer as well as the softmax loss layer by the layers described in Section [ reference ] . To increase the feature map size from the last conv layer , we replace the penultimate max - pooling layer and its subsequent conv layers by the dilated conv layers . The newly added layers are initialized using Gaussian distributions with - mean and standard deviations . We replace the last max - pooling layer of the two models by SPP layer , and the last fc layer and softmax loss layer by the layers described in Section [ reference ] . To increase the feature map size from the last conv layer , we replace the penultimate max - pooling layer and its subsequent conv layers by the dilated conv layers . The new added layers are initialized using Gaussian distributions with - mean and standard deviations . This comprehensive data augmentation scheme makes the network resist overfitting . Our network contains dilated convolution following . During the course of experiments , we notice that an appropriately large “ cropsize ” can yield good performance and “ batchsize ” in the batch normalization layer is of great importance . The main reason is that the classic convolution filters with kernel size , and overlap with each other at the same output position and produce redundant information . To make it clearer , we show the dilated convolution kernel ( size ) with dilation ratio ranging from to in Figure [ reference ] . For the same output position which is shown in red circle , the convolution kernel with larger dilation ratio has larger receptive field , while only the center position is overlapped with other convolution kernels . We first apply upsampling rate 2 on through bilinear interpolation , yielding the same spatial size as . Then a dilated convolution layer with kernel size and dilation 2 is applied to refine the upsampled features . The resulting feature is with size . The familiar discrete convolution is simply the - dilated convolution . The dilated convolution operator has been referred to in the past as “ convolution with a dilated filter ” . It plays a key role in the algorithme à trous , an algorithm for wavelet decomposition . Dilated convolutions which is very successful in dense detection tasks increases accuracy 2 mAP over R50 architecture . However , dilated convolutional filters add more computational complexity , consequently hinder realtime performance . We showed that concatenation of features and intermediate supervision ( see Section [ reference ] for explanations ) is crucial for good perfomance . However , 3D convolutional neural network has a serious drawback which is that its parameter quantity is too large . Depthwise convolution is a form of group convolution , which can effectively reduce the parameter of convolution kernel , and has been widely applied in 2D convolutional neural network . Therefore , we propose to introduce depthwise convolution into the 3D convolutional neural network . These vectors are clustered by a vector dot product , and each category represents a special semantic . The grouping attention algorithm implements the functions of group convolution and feature clustering , which can greatly reduce the network parameters and improve the recognition rate and interpretability of the network . Finally , the low-level visual features and high-level semantic information are merged by a multi-level feature fusion method to accurately classify fine-grained images . We present Group-size Series ( GroSS ) decomposition , a mathematical formulation of tensor factorisation into a series of approximations of increasing rank terms . GroSS allows for dynamic and differentiable selection of factorisation rank , which is analogous to a grouped convolution . Therefore , to the best of our knowledge , GroSS is the first method to simultaneously train differing numbers of groups within a single layer , as well as all possible combinations between layers . Group-size Series ( GroSS ) decomposition is a mathematical formulation of tensor factorisation into a series of approximations of increasing rank terms . GroSS allows for dynamic and differentiable selection of factorisation rank , which is analogous to a grouped convolution . Therefore , to the best of our knowledge , GroSS is the first method to enable simultaneously train differing numbers of groups within a single layer , as well as all possible combinations between layers . We encode this geometric structure into convolutional neural networks ( CNNs ) via $ SE(2)$ group convolutional layers , which fit into the standard 2D CNN framework , and which allow to generically deal with rotated input samples without the need for data augmentation . We introduce three layers : a lifting layer which lifts a 2D ( vector valued ) image to an $ SE(2)$-image , i.e. , 3D ( vector valued ) data whose domain is $ SE(2)$ ; a group convolution layer from and to an $ SE(2)$-image ; and a projection layer from an $ SE(2)$-image to a 2D image . The lifting and group convolution layers are $ SE(2)$ covariant ( the output roto-translates with the input ) . We argue that this operation discards important information and leads to subpar global descriptors . In this paper , we propose a group convolutional approach to multiple view aggregation where convolutions are performed over a discrete subgroup of the rotation group , enabling , thus , joint reasoning over all views in an equivariant ( instead of invariant ) fashion , up to the very last layer . We further develop this idea to operate on smaller discrete homogeneous spaces of the rotation group , where a polar view representation is used to maintain equivariance with only a fraction of the number of input views . In contrast , regular convolutional layers break this creed by learning filters that must simultaneously perform the extraction of spatial features and their merger into channel dimensions ; an inefficient and ineffective use of parameters . Grouped convolutions ( or " sub - separable convolutions " ) are an intermediary step between regular convolutions and depthwise separable convolutions . They consist in splitting the channels of an input into several non - overlapping segments ( or " groups " ) , performing a regular spatial convolution over each segment independently , then concatenating the resulting feature maps along the channel axis . The successes of deep convolutional neural nets ( CNNs ) at image classification has spawned a flurry of work in computer vision on adapting these models to pixel - level image understanding tasks , such as boundary detection , semantic segmentation , optical flow , and pose estimation . The key ideas that have enabled this adaption thus far are : ( 1 ) deconvolution schemes that allow for upsampling coarse pooled feature maps to make detailed predictions at the spatial resolution of individual pixels , ( 2 ) skip connections and hyper - columns which concatenate representations across multi - resolution feature maps , ( 3 ) atrous convolution which allows efficient computation with large receptive fields while maintaining spatial resolution , and ( 4 ) fully convolutional operation which handles variable sized input images . In contrast , there has been less innovation in the development of specialized loss functions for training . [ reference ] In a group conv layer [ reference ] , input and output channels are divided into C groups , and convolutions are separately performed within each group . We note that the reformulations produce nontrivial topologies only when the block has depth ≥3 . The output of the last convolutional layer is split into two parts : the first part is element - wisely added to the residual path , and the second part is concatenated with the densly connected path . To enhance the leaning capacity of each micro - block , we use the grouped convolution layer in the second layer as the ResNeXt . Considering that the residual networks are more wildly used than the densely connected networks in practice , we choose the residual network as the backbone and add a thin densely connected path to build the dual path network . The recent evolution of Artificial Intelligence ( AI ) and deep learning models coupled with advancements of assistive robotic systems have shown great potential in significantly improving myoelectric control of prosthetic devices . In this regard , the paper proposes a novel deep-learning-based architecture for processing surface Electromyography ( sEMG ) signals to classify and recognize upper-limb hand gestures via incorporation of dilated causal convolutions . The proposed approach has the potential to significantly improve the overall recognition accuracy due to the specific design of the convolutional layers . Wavenet is a deep neural network originally developed as a deep generative model of raw audio wave-forms . Wavenet architecture is based on dilated causal convolutions , which exhibit very large receptive fields . In this short paper we suggest using the Wavenet architecture , in particular its large receptive filed in order to learn PESQ algorithm . However , there are still two major challenges in predicting flows . First , it is difficult to train the model with the ability to capture both the nearby and distant spatial dependency by deep local convolutions . Second , daily and weekly patterns in temporal dependency are not strictly periodic for their dynamic temporal shifting in each region . In contrast with other operations used in state-of-the-art normalizing flows , Woodbury transformations enable ( 1 ) high-dimensional interactions , ( 2 ) efficient sampling , and ( 3 ) efficient likelihood evaluation . Other similar operations , such as 1x1 convolutions , emerging convolutions , or periodic convolutions allow at most two of these three advantages . In our experiments on multiple image datasets , we find that Woodbury transformations allow learning of higher-likelihood models than other flow architectures while still enjoying their efficiency advantages . We seek a balance between speed and accuracy by building an effective and efficient video classification system through systematic exploration of critical network design choices . In particular , we show that it is possible to replace many of the 3D convolutions by low-cost 2D convolutions . Rather surprisingly , best result ( in both speed and accuracy ) is achieved when replacing the 3D convolutions at the bottom of the network , suggesting that temporal representation learning on high-level “ semantic ” features is more useful . Finally , motivated by medical applications suchas angiography , we derive a fast algorithm for segmenting images into twonon-overlapping smooth regions . Compared to existing piecewise-smoothapproaches , this last model has the unique advantage of featuring closed-form solutions for the approximation functions in each region based onnormalized convolutions . Results are shown on synthetic 2D images andreal 3D volumes . Alternatively , this linear scale space can also be obtained by applying Gaussian filters of increasing variances to the original image . The authors compare ( by looking at theoretical properties , running time and output differences ) five ways of discretizing this Gaussian scale-space : sampling Gaussian distributions ; recursively calculating Gaussian approximations ; using splines ; approximating by first-order generators ; and finally , by a new method we call " Crossed Convolutions " . In particular , we explicitly present a correct way of initializing the recursive method to approximate Gaussian convolutions . In this paper we discuss a recently developed version of the code that can take advantage of CUDA-enabled graphics processing units ( GPUs ) to achieve significantly improved performance for a large class of simulations that are important in practice . The GPU version is largely defined by a framework that simplifies implementations of the fundamental kernel types that are used by Elegant : particle operations , reductions , particle loss , histograms , array convolutions and random number generation . Accelerated performance on the Titan Cray XK-7 supercomputer is approximately 6 - 10 times better with the GPU than all the CPU cores associated with the same node count . Long length convolutions implemented on a FPGA are not area , power efficient and also it can not be implemented on a single FPGA . To increase the speed of long convolutions and to meet the calculation capacity of each single FPGA chip , the long coefficient sequence can be partitioned into short sub-sequences . Each short length convolution can then be made area efficient at the expense of decrease in speed by implementing them as a convolution ASIP . Results demonstrated that the interplay of heterogeneous cortex growth and mechanical forces along axons plays a vital role in the regulation of circumferential convolutions . In contrast , gyral geometry controls the complexity of circumferential convolutions . These findings offer insight into the mystery of circumferential convolutions in primate brains . Whenever an output site is determined to be active , its output feature vector is calculated by the SC operation . Table [ reference ] presents the computational and memory requirements of a regular convolution ( C ) and of our SC and VSC convolutions . To construct convolutional networks using SC and VSC , we also need activation functions , batch normalization , and pooling . Although increased model size and computational cost tend to translate to immediate quality gains for most tasks ( as long as enough labeled data is provided for training ) , computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big - data scenarios . Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization . We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art : top - and top - error for single frame evaluation using a network with a computational cost of billion multiply - adds per inference and with using less than 25 million parameters . The first step , which is considered to be a preprocessing , is a discrete Gaussian deconvolution performed only once in the frequency domain . Afterwards , the second step is a spatial-domain convolution with a truncated Gaussian kernel , which is used to interpolate arbitrary samples for ray casting . Since the preprocessing is actually a discrete prefiltering , we call our technique prefiltered Gaussian reconstruction ( PGR ) . This paper introduces a generalization of Convolutional Neural Networks ( CNNs ) to graphs with irregular linkage structures , especially heterogeneous graphs with typed nodes and schemas . We propose a novel spatial convolution operation to model the key properties of local connectivity and translation invariance , using high-order connection patterns or motifs . We develop a novel deep architecture Motif-CNN that employs an attention model to combine the features extracted from multiple patterns , thus effectively capturing high-order structural and feature information . Simulation results confirm the tractability of our model for the deconvolution of large images . Moreover , in terms of accuracy metrics , the quality of reconstruction using our model is superior to that obtained using spatially invariant convolution . Of particular interest is the Dirac delta function basis spatially-varying OIS , a technique particularly applicable to images with asymmetric and non-Gaussian point spread functions ( PSFs ) . This study demonstrates the ability to accelerate the spatially-varying convolution components of OIS by two to three orders of magnitude . This order of speed-up is needed to apply these techniques to real-time analysis of gigapixel survey telescopes that acquire an image every 30 - 60 seconds . Subject to constraints on the spatial support of the kernel , the algorithm generates the kernal values that minimize the expected mean-square error of the estimate of the scene characteristic . This development is consistent with the derivation of the spatially unconstrained Wiener characteristic filter , but leads to a small , spatially constrained convolution kernel . Simulation experiments demonstrate that the algorithm is more flexible than traditional small-kernel techniques and yields more accurate estimates.<<ETX > > The prior probability of the latent representations is modeled by a hyperprior autoencoder and trained jointly with the MV-Residual network . Specially , the spatially-displaced convolution is applied for video frame prediction , in which a motion kernel for each pixel is learned to generate predicted pixel by applying the kernel at a displaced location in the source image . Finally , novel rate allocation and post-processing strategies are used to produce the final compressed bits , considering the bits constraint of the challenge . Many blind motion deblur methods model the motion blur as a spatially invariant convolution process . However , motion blur caused by the camera movement in 3D space during shutter time often leads to spatially varying blurring effect over the image . Semantic understanding of 3D environments is critical for both the unmanned system and the human involved virtual/augmented reality ( VR/AR ) immersive experience . Spatially-sparse convolution , taking advantage of the intrinsic sparsity of 3D point cloud data , makes high resolution 3D convolutional neural networks tractable with state-of-the-art results on 3D semantic segmentation problems . However , the exhaustive computations limits the practical usage of semantic 3D perception for VR/AR applications in portable devices . Although the exact receptive field may vary due to different padding strategies , we use a simple estimation which has been reported to work well in practice . Denote the sampling stride of a spatial convolutional layer as and the kernel size of a max pooling layer as , the overall sampling stride is given by where is the collection of all convolutional layers and is the collection of all max pooling layers . Implementation .
The Middle Block learns to preserve global-context semantic information , long-distance spatial information and relationships , and different feature channels ’ information and dependencies . It is different from other road segmentation methods which lose spatial information , such as those using dilated convolution and multiscale feature fusion to record local-context semantic information . The Middle Block consists of three important steps : ( 1 ) forming a feature resource pool to gather high-order global spatial information ; ( 2 ) selecting a feature weight distribution , enabling each pixel position to obtain complementary features according to its own needs ; and ( 3 ) inversely mapping the intermediate output feature encoding to the size of the input image by expanding the number of channels of the intermediate output feature .
With the help of these high-level representations , we use global residual learning to improve reconstruction quality in the second phase . In order to reduce cumbersome computation , between two phases we introduce dilated convolution which enlarges receptive field without increasing additional parameters . Comprehensive experiments show that ETRN has a significant improvement in both quantitation and visual perception .
In this paper , we propose a novel saliency object detection method , which predicts the saliency by incorporating both pixel-level and region-level predictions . First , in order to alleviate the scale-space problem , modified dilated convolution layers and short connections are integrated into the FCN model , and the pixel-level saliency maps is generated by a pixel-wise salient classifier . Then , we employ a superpixel based manifold learning algorithm to obtain a better boundary of salient object , by which a region-level saliency map with clearer object boundary is generated .
The dilated convolution operation is used to extract the correlation between the predicted variable and correlational variables . The features extracted by dilated convolution operation and historical data of predicted variable are input into LSTM to obtain the desired multi-step prediction result . Furthermore , cross-correlation analyses ( CCA ) are applied to calculate the reasonable maximum prediction steps of chaotic time series .
These models lack a hierarchical structure that can aid them in dissecting the input structure at different levels to allow flow of context between sentences . In this paper , we propose a hierarchical neural network comprising of Bi-LSTMs , Dilated Convolution operation , Capsules and Conditional Random Field ( CRF ) to understand the discourse/ abstract structure and predict next probable label by using label history . We have performed experiments on 3 publicly available datasets through which we have demonstrated that our model has achieved state-of-art performance on these datasets .
The distance-view transformation is newly proposed to overcome the low density of top-view data of lidar . In addition , a dilated convolution structure is proposed to expand the receptive field of a convolutional neural network . The proposed network improves the accuracy of segmentation .
The transformed images of height , red , green , and blue are input to the CNN . A dilated convolution structure is newly proposed to improve the learning accuracy by expanding the receptive field of CNN . The experimental results using the KITTI data set are finally presented to show the usefulness of the proposed method .
Recently , deep-convolutional-neural-networks based methods have achieved promising results in the monocular depth estimation field . Specifically , the framework that combines the multi-scale features extracted by the dilated convolution based block ( atrous spatial pyramid pooling , ASPP ) has gained the significant improvement in the dense labeling task . However , the discretized and predefined dilation rates can not capture the continuous context information that differs in diverse scenes and easily introduce the grid artifacts in depth estimation .
subsubsection : Simplifying WaveNet A defining feature of WaveNet is its use of dilated convolution to increase the receptive field exponentially with the number of layers . We evaluate models with varying receptive field sizes and number of layers to test our hypothesis that a shallow network with a small receptive field may solve the problem satisfactorily since mel spectrograms are a much closer representation of the waveform than linguistic features and already capture long - term dependencies across frames .
However , the current proposed deep learning based restoration methods do not take the multi - scale information into account . In this paper , we propose a dilated convolution based inception module to learn multi - scale information and design a deep network for single image super - resolution . Different dilated convolution learns different scale feature , then the inception module concatenates all these features to fuse multi - scale information . In order to increase the reception field of our network to catch more contextual information , we cascade multiple inception modules to constitute a deep network to conduct single image super - resolution .
Our proposed new inception module contains multiple different scale dilated convolution that makes it can learn multi - scale image information . Furthermore , we cascade multiple dilated convolution based inception modules to constitute a deep network for single image SR . In short , the contributions of this work are mainly in three aspects : 1 ) we proposed a dilated convolution based inception module , which can learn multi - scale information with only single scale image input ; 2 ) we design a novel deep network with the proposed dilated convolution based inception module for single image SR .
We refer the reader to for more information about the dilated convolution . Dilated Convolution based Inception Module : To make full use of the image multi - scale information , Szegedy et al . proposed an inception module for image classification . As shown in Fig .
We replace the last max - pooling layer by the SPP layer , and the last fc layer as well as the softmax loss layer by the layers described in Section [ reference ] . To increase the feature map size from the last conv layer , we replace the penultimate max - pooling layer and its subsequent conv layers by the dilated conv layers . The newly added layers are initialized using Gaussian distributions with - mean and standard deviations .
We replace the last max - pooling layer of the two models by SPP layer , and the last fc layer and softmax loss layer by the layers described in Section [ reference ] . To increase the feature map size from the last conv layer , we replace the penultimate max - pooling layer and its subsequent conv layers by the dilated conv layers . The new added layers are initialized using Gaussian distributions with - mean and standard deviations .
This comprehensive data augmentation scheme makes the network resist overfitting . Our network contains dilated convolution following . During the course of experiments , we notice that an appropriately large “ cropsize ” can yield good performance and “ batchsize ” in the batch normalization layer is of great importance .
The main reason is that the classic convolution filters with kernel size , and overlap with each other at the same output position and produce redundant information . To make it clearer , we show the dilated convolution kernel ( size ) with dilation ratio ranging from to in Figure [ reference ] . For the same output position which is shown in red circle , the convolution kernel with larger dilation ratio has larger receptive field , while only the center position is overlapped with other convolution kernels .
We first apply upsampling rate 2 on through bilinear interpolation , yielding the same spatial size as . Then a dilated convolution layer with kernel size and dilation 2 is applied to refine the upsampled features . The resulting feature is with size .
The familiar discrete convolution is simply the - dilated convolution . The dilated convolution operator has been referred to in the past as “ convolution with a dilated filter ” . It plays a key role in the algorithme à trous , an algorithm for wavelet decomposition .
Dilated convolutions which is very successful in dense detection tasks increases accuracy 2 mAP over R50 architecture . However , dilated convolutional filters add more computational complexity , consequently hinder realtime performance . We showed that concatenation of features and intermediate supervision ( see Section [ reference ] for explanations ) is crucial for good perfomance .
However , 3D convolutional neural network has a serious drawback which is that its parameter quantity is too large . Depthwise convolution is a form of group convolution , which can effectively reduce the parameter of convolution kernel , and has been widely applied in 2D convolutional neural network . Therefore , we propose to introduce depthwise convolution into the 3D convolutional neural network .
These vectors are clustered by a vector dot product , and each category represents a special semantic . The grouping attention algorithm implements the functions of group convolution and feature clustering , which can greatly reduce the network parameters and improve the recognition rate and interpretability of the network . Finally , the low-level visual features and high-level semantic information are merged by a multi-level feature fusion method to accurately classify fine-grained images .
We present Group-size Series ( GroSS ) decomposition , a mathematical formulation of tensor factorisation into a series of approximations of increasing rank terms . GroSS allows for dynamic and differentiable selection of factorisation rank , which is analogous to a grouped convolution . Therefore , to the best of our knowledge , GroSS is the first method to simultaneously train differing numbers of groups within a single layer , as well as all possible combinations between layers .
Group-size Series ( GroSS ) decomposition is a mathematical formulation of tensor factorisation into a series of approximations of increasing rank terms . GroSS allows for dynamic and differentiable selection of factorisation rank , which is analogous to a grouped convolution . Therefore , to the best of our knowledge , GroSS is the first method to enable simultaneously train differing numbers of groups within a single layer , as well as all possible combinations between layers .
We encode this geometric structure into convolutional neural networks ( CNNs ) via $ SE(2)$ group convolutional layers , which fit into the standard 2D CNN framework , and which allow to generically deal with rotated input samples without the need for data augmentation . We introduce three layers : a lifting layer which lifts a 2D ( vector valued ) image to an $ SE(2)$-image , i.e. , 3D ( vector valued ) data whose domain is $ SE(2)$ ; a group convolution layer from and to an $ SE(2)$-image ; and a projection layer from an $ SE(2)$-image to a 2D image . The lifting and group convolution layers are $ SE(2)$ covariant ( the output roto-translates with the input ) .
We argue that this operation discards important information and leads to subpar global descriptors . In this paper , we propose a group convolutional approach to multiple view aggregation where convolutions are performed over a discrete subgroup of the rotation group , enabling , thus , joint reasoning over all views in an equivariant ( instead of invariant ) fashion , up to the very last layer . We further develop this idea to operate on smaller discrete homogeneous spaces of the rotation group , where a polar view representation is used to maintain equivariance with only a fraction of the number of input views .
In contrast , regular convolutional layers break this creed by learning filters that must simultaneously perform the extraction of spatial features and their merger into channel dimensions ; an inefficient and ineffective use of parameters . Grouped convolutions ( or " sub - separable convolutions " ) are an intermediary step between regular convolutions and depthwise separable convolutions . They consist in splitting the channels of an input into several non - overlapping segments ( or " groups " ) , performing a regular spatial convolution over each segment independently , then concatenating the resulting feature maps along the channel axis .
The successes of deep convolutional neural nets ( CNNs ) at image classification has spawned a flurry of work in computer vision on adapting these models to pixel - level image understanding tasks , such as boundary detection , semantic segmentation , optical flow , and pose estimation . The key ideas that have enabled this adaption thus far are : ( 1 ) deconvolution schemes that allow for upsampling coarse pooled feature maps to make detailed predictions at the spatial resolution of individual pixels , ( 2 ) skip connections and hyper - columns which concatenate representations across multi - resolution feature maps , ( 3 ) atrous convolution which allows efficient computation with large receptive fields while maintaining spatial resolution , and ( 4 ) fully convolutional operation which handles variable sized input images . In contrast , there has been less innovation in the development of specialized loss functions for training .
[ reference ] In a group conv layer [ reference ] , input and output channels are divided into C groups , and convolutions are separately performed within each group . We note that the reformulations produce nontrivial topologies only when the block has depth ≥3 .
The output of the last convolutional layer is split into two parts : the first part is element - wisely added to the residual path , and the second part is concatenated with the densly connected path . To enhance the leaning capacity of each micro - block , we use the grouped convolution layer in the second layer as the ResNeXt . Considering that the residual networks are more wildly used than the densely connected networks in practice , we choose the residual network as the backbone and add a thin densely connected path to build the dual path network .
The recent evolution of Artificial Intelligence ( AI ) and deep learning models coupled with advancements of assistive robotic systems have shown great potential in significantly improving myoelectric control of prosthetic devices . In this regard , the paper proposes a novel deep-learning-based architecture for processing surface Electromyography ( sEMG ) signals to classify and recognize upper-limb hand gestures via incorporation of dilated causal convolutions . The proposed approach has the potential to significantly improve the overall recognition accuracy due to the specific design of the convolutional layers .
Wavenet is a deep neural network originally developed as a deep generative model of raw audio wave-forms . Wavenet architecture is based on dilated causal convolutions , which exhibit very large receptive fields . In this short paper we suggest using the Wavenet architecture , in particular its large receptive filed in order to learn PESQ algorithm .
However , there are still two major challenges in predicting flows . First , it is difficult to train the model with the ability to capture both the nearby and distant spatial dependency by deep local convolutions . Second , daily and weekly patterns in temporal dependency are not strictly periodic for their dynamic temporal shifting in each region .
In contrast with other operations used in state-of-the-art normalizing flows , Woodbury transformations enable ( 1 ) high-dimensional interactions , ( 2 ) efficient sampling , and ( 3 ) efficient likelihood evaluation . Other similar operations , such as 1x1 convolutions , emerging convolutions , or periodic convolutions allow at most two of these three advantages . In our experiments on multiple image datasets , we find that Woodbury transformations allow learning of higher-likelihood models than other flow architectures while still enjoying their efficiency advantages .
We seek a balance between speed and accuracy by building an effective and efficient video classification system through systematic exploration of critical network design choices . In particular , we show that it is possible to replace many of the 3D convolutions by low-cost 2D convolutions . Rather surprisingly , best result ( in both speed and accuracy ) is achieved when replacing the 3D convolutions at the bottom of the network , suggesting that temporal representation learning on high-level “ semantic ” features is more useful .
Finally , motivated by medical applications suchas angiography , we derive a fast algorithm for segmenting images into twonon-overlapping smooth regions . Compared to existing piecewise-smoothapproaches , this last model has the unique advantage of featuring closed-form solutions for the approximation functions in each region based onnormalized convolutions . Results are shown on synthetic 2D images andreal 3D volumes .
Alternatively , this linear scale space can also be obtained by applying Gaussian filters of increasing variances to the original image . The authors compare ( by looking at theoretical properties , running time and output differences ) five ways of discretizing this Gaussian scale-space : sampling Gaussian distributions ; recursively calculating Gaussian approximations ; using splines ; approximating by first-order generators ; and finally , by a new method we call " Crossed Convolutions " . In particular , we explicitly present a correct way of initializing the recursive method to approximate Gaussian convolutions .
In this paper we discuss a recently developed version of the code that can take advantage of CUDA-enabled graphics processing units ( GPUs ) to achieve significantly improved performance for a large class of simulations that are important in practice . The GPU version is largely defined by a framework that simplifies implementations of the fundamental kernel types that are used by Elegant : particle operations , reductions , particle loss , histograms , array convolutions and random number generation . Accelerated performance on the Titan Cray XK-7 supercomputer is approximately 6 - 10 times better with the GPU than all the CPU cores associated with the same node count .
Long length convolutions implemented on a FPGA are not area , power efficient and also it can not be implemented on a single FPGA . To increase the speed of long convolutions and to meet the calculation capacity of each single FPGA chip , the long coefficient sequence can be partitioned into short sub-sequences . Each short length convolution can then be made area efficient at the expense of decrease in speed by implementing them as a convolution ASIP .
Results demonstrated that the interplay of heterogeneous cortex growth and mechanical forces along axons plays a vital role in the regulation of circumferential convolutions . In contrast , gyral geometry controls the complexity of circumferential convolutions . These findings offer insight into the mystery of circumferential convolutions in primate brains .
Whenever an output site is determined to be active , its output feature vector is calculated by the SC operation . Table [ reference ] presents the computational and memory requirements of a regular convolution ( C ) and of our SC and VSC convolutions . To construct convolutional networks using SC and VSC , we also need activation functions , batch normalization , and pooling .
Although increased model size and computational cost tend to translate to immediate quality gains for most tasks ( as long as enough labeled data is provided for training ) , computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big - data scenarios . Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization . We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art : top - and top - error for single frame evaluation using a network with a computational cost of billion multiply - adds per inference and with using less than 25 million parameters .
The first step , which is considered to be a preprocessing , is a discrete Gaussian deconvolution performed only once in the frequency domain . Afterwards , the second step is a spatial-domain convolution with a truncated Gaussian kernel , which is used to interpolate arbitrary samples for ray casting . Since the preprocessing is actually a discrete prefiltering , we call our technique prefiltered Gaussian reconstruction ( PGR ) .
This paper introduces a generalization of Convolutional Neural Networks ( CNNs ) to graphs with irregular linkage structures , especially heterogeneous graphs with typed nodes and schemas . We propose a novel spatial convolution operation to model the key properties of local connectivity and translation invariance , using high-order connection patterns or motifs . We develop a novel deep architecture Motif-CNN that employs an attention model to combine the features extracted from multiple patterns , thus effectively capturing high-order structural and feature information .
Simulation results confirm the tractability of our model for the deconvolution of large images . Moreover , in terms of accuracy metrics , the quality of reconstruction using our model is superior to that obtained using spatially invariant convolution .
Of particular interest is the Dirac delta function basis spatially-varying OIS , a technique particularly applicable to images with asymmetric and non-Gaussian point spread functions ( PSFs ) . This study demonstrates the ability to accelerate the spatially-varying convolution components of OIS by two to three orders of magnitude . This order of speed-up is needed to apply these techniques to real-time analysis of gigapixel survey telescopes that acquire an image every 30 - 60 seconds .
Subject to constraints on the spatial support of the kernel , the algorithm generates the kernal values that minimize the expected mean-square error of the estimate of the scene characteristic . This development is consistent with the derivation of the spatially unconstrained Wiener characteristic filter , but leads to a small , spatially constrained convolution kernel . Simulation experiments demonstrate that the algorithm is more flexible than traditional small-kernel techniques and yields more accurate estimates.<<ETX > >
The prior probability of the latent representations is modeled by a hyperprior autoencoder and trained jointly with the MV-Residual network . Specially , the spatially-displaced convolution is applied for video frame prediction , in which a motion kernel for each pixel is learned to generate predicted pixel by applying the kernel at a displaced location in the source image . Finally , novel rate allocation and post-processing strategies are used to produce the final compressed bits , considering the bits constraint of the challenge .
Many blind motion deblur methods model the motion blur as a spatially invariant convolution process . However , motion blur caused by the camera movement in 3D space during shutter time often leads to spatially varying blurring effect over the image .
Semantic understanding of 3D environments is critical for both the unmanned system and the human involved virtual/augmented reality ( VR/AR ) immersive experience . Spatially-sparse convolution , taking advantage of the intrinsic sparsity of 3D point cloud data , makes high resolution 3D convolutional neural networks tractable with state-of-the-art results on 3D semantic segmentation problems . However , the exhaustive computations limits the practical usage of semantic 3D perception for VR/AR applications in portable devices .
Although the exact receptive field may vary due to different padding strategies , we use a simple estimation which has been reported to work well in practice . Denote the sampling stride of a spatial convolutional layer as and the kernel size of a max pooling layer as , the overall sampling stride is given by where is the collection of all convolutional layers and is the collection of all max pooling layers . Implementation .
HsgNet: A Road Extraction Network Based on Global Perception of High-Order Spatial Information Enhanced two-phase residual network for single image super-resolution Pixel Meets Region: A Pratical Framework for Salient Object Detection A dilated convolution network-based LSTM model for multi-step prediction of chaotic time series Hierarchical Capsule Based Neural Network Architecture for Sequence Labeling Segmentation of Vehicles and Roads by a Low-Channel Lidar Fast Lidar - Camera Fusion for Road Detection by CNN and Spherical Coordinate Transformation Attention-based Context Aggregation Network for Monocular Depth Estimation Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions Single image super-resolution with dilated convolution based multi-scale information learning inception module PCL: Proposal Cluster Learning for Weakly Supervised Object Detection Multiple Instance Detection Network with Online Instance Classifier Refinement Pyramid Scene Parsing Network Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification ICNet for Real-Time Semantic Segmentation on High-Resolution Images Multi-Scale Context Aggregation by Dilated Convolutions MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network A Depthwise Separable Network for Action Recognition Two-Level Attentions and Grouping Attention Convolutional Network for Fine-Grained Image Classification GroSS: Group-Size Series Decomposition for Whole Search-Space Training GroSS: Group-Size Series Decomposition for Grouped Architecture Search. Roto-Translation Covariant Convolutional Networks for Medical Image Analysis Equivariant Multi-View Networks Depthwise Separable Convolutions for Neural Machine Translation Recurrent Pixel Embedding for Instance Grouping Aggregated Residual Transformations for Deep Neural Networks Dual Path Networks Semg-Based Hand Gesture Recognition Via Dilated Convolutional Neural Networks Perceptual audio loss function for deep learning Deep spatial-temporal networks for crowd flows prediction by dilated convolutions and region-shifting attention mechanism Woodbury Transformations for Deep Generative Flows Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification Fuzzy Region Competition: A Convex Two-Phase Segmentation Framework Discrete scale spaces via heat equation GPU acceleration and performance of the particle-beam-dynamics code Elegant Efficient implementation of fast convolution in ASIP Mechanisms of circumferential gyral convolution in primate brains Submanifold Sparse Convolutional Networks Rethinking the Inception Architecture for Computer Vision Prefiltered Gaussian reconstruction for high-quality rendering of volumetric data sampled on a body-centered cubic grid Motif-based Convolutional Neural Network on Graphs An Axially Variant Kernel Imaging Model Applied to Ultrasound Image Reconstruction High speed, high volume, optimal image subtraction for large volume astronomical data pipelines Optimal small kernels for edge detection End-to-end Optimized Video Compression with MV-Residual Prediction A two-stage approach to blind spatially-varying motion deblurring Live Semantic 3D Perception for Immersive Augmented Reality ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks
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ICME IJCNN 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2017 IEEE International Conference on Image Processing (ICIP) IEEE Transactions on Pattern Analysis and Machine Intelligence 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV ICLR ECCV ICLR 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS GlobalSIP ECCV (15) SSVM SIBGRAPI ArXiv 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Visualization ICPR (2) CVPR 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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