Recently , impressive improvement has achieved for this task by using deep fully convolutional networks ( FCNs ) . Most of state-of-the-art models rely on 3D convolutions as well as model cascade/ensemble strategies , but at the expense of a significant increase in computational overheads and undesired system complexity . In this study , we segment 3D volume in one-pass with a hierarchical decoupled convolution network ( HDC-Net ) , which is a light-weight but efficient pseudo-3D model . However , we propose a novel SISR method that uses relatively less number of computations . On training , we get group convolutions that have unused connections removed . We have refined this system specifically for the task at hand by removing unnecessary modules from original CondenseNet . With the deformable sampling locations , deformable feature images are then created by compressing neighboring similar structural information of each pixel into fixed grids . Therefore , applying the regular convolutions on the deformable feature images can reflect complex structures more effectively . Moreover , instead of adopting the pooling layers , the strided convolution is further introduced on the feature images , which can be learned for feature downsampling according to spatial contexts . We use these results for finding sharp constants of best approximation in L 1 ( R ) and L ∞ ( R ) on some complex convolution classes . For classes of real-valued convolutions those constants were found by Akhiezer . As an example , we apply these results to the Schwarz-type kernel and to the corresponding convolution classes . Operation with functions known as pseudo-convolution and its generalization as well as theirs basic properties has been presented . Then , it has been proved that pseudo-convolution which core is pseudo-integral based on max or min decomposable measure can be obtained as limit of g-convolutions , i.e. , pseudo-convolutions with pseudo-integrals based on ⊕-decomposable measures where ⊕ is generated pseudo-addition , as their cores . We introduce Group equivariant Convolutional Neural Networks ( G-CNNs ) , a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries . G-CNNs use G-convolutions , a new type of layer that enjoys a substantially higher degree of weight sharing than regular convolution layers . G-convolutions increase the expressive capacity of the network without increasing the number of parameters . As we show in our experiments , the size of the receptive field is directly related to the performance of dense tasks such as semantic segmentation . We look at different network architectures and mechanisms to increase the receptive field size of point convolutions and propose in particular dilated point convolutions . Importantly , our dilation mechanism can easily be integrated into all existing methods using nearest-neighbor-based point convolutions . JsrNet utilizes the whole group of frames as the reference to reconstruct each frame , regardless of key frames and non-key frames , while the existing frameworks only utilize key frames as the reference to reconstruct non-key frames . Moreover , different from the existing frameworks which only focus on exploiting complementary information between frames in joint reconstruction , JsrNet also applies this conception in joint sampling by adopting learnable convolutions to sample multiple frames jointly and simultaneously in an encoder . JsrNet fully exploits spatial – temporal correlation in both sampling and reconstruction , and achieves a competitive performance in both the quality of reconstruction and computational complexity , making it a promising candidate in source-limited , real-time scenarios . Targeting the drawbacks of the low accuracy rate and high memory resource consumption of current multilabel annotation methods , this study proposed a CM-supplement network model . This model combines the merits of cavity convolutions , Inception modules and a supplement network . The replacement of common convolutions with cavity convolutions enlarged the receptive field without increasing the number of parameters . It defines a deep convolution network model , whose contraction properties can be analyzed mathematically . A cascade of wavelet transform convolutions are computed with a multirate filter bank , and adapted with permutations . Unsupervised learning of permutations optimize the contraction directions , by maximizing the average discriminability of training data . A deep network that incorporates this one binary extremely large and inflecting sparse kernel ( OBELISK ) filter requires fewer trainable parameters and less memory while achieving high quality results compared to fully‐convolutional U‐Net architectures on two challenging 3D CT multi‐organ segmentation tasks . Extensive validation experiments indicate that the performance of sparse deformable convolutions is due to their ability to capture large spatial context with few expressive filter parameters and that network depth is not always necessary to learn complex shape and appearance features . A combination with conventional CNNs further improves the delineation of small organs with large shape variations and the fast inference time using flexible image sampling may offer new potential use cases for deep networks in computer‐assisted , image‐guided interventions . In this paper , we propose the Laplacian Pyramid Super - Resolution Network ( LapSRN ) to progressively reconstruct the sub - band residuals of high - resolution images . At each pyramid level , our model takes coarse - resolution feature maps as input , predicts the high - frequency residuals , and uses transposed convolutions for upsampling to the finer level . Our method does not require the bicubic interpolation as the pre - processing step and thus dramatically reduces the computational complexity . This technique reduces runtime by an order of magnitude and enables real - time video SR by independently processing frames with a single frame model . Similar solutions to improve efficiency have also been proposed based on transposed convolutions . subsection : Motivation and contributions section : Conclusion In this paper we combine the efficiency advantages of sub - pixel convolutions with temporal fusion strategies to present real - time spatio - temporal models for video SR . The spatio - temporal models used are shown to facilitate an improvement in reconstruction accuracy and temporal consistency or reduce computational complexity relative to independent single frame processing . We apply a 5×5 kernel with 512 outputs , followed by sibling fully connected layers to predict a 14×14 mask ( 14 2 outputs ) and object score ( 1 output ) . The model is implemented in a fully convolutional manner ( using 1×1 convolutions in place of fully connected layers ) . The 7×7 MLP for handling objects at half octave scales is identical to the 5×5 MLP except for its larger input region . The projection shortcut in Eqn . ( 2 ) is used to match dimensions ( done by 1×1 convolutions ) . For both options , when the shortcuts go across feature maps of two sizes , they are performed with a stride of 2 . It consists of a depthwise convolution , i.e. a spatial convolution performed independently over every channel of an input , followed by a pointwise convolution , i.e. a regular convolution with 1x1 windows , projecting the channels computed by the depthwise convolution onto a new channel space . The depthwise separable convolution operation should not be confused with spatially separable convolutions , which are also often called “ separable convolutions ” in the image processing community . Their mathematical formulation is as follow ( we use to denote the element - wise product ) : Fully convolutional neural machine translation without this bottleneck was first achieved in and . The model in ( Extended Neural GPU ) used a recurrent stack of gated convolutional layers , while the model in ( ByteNet ) did away with recursion and used left - padded convolutions in the decoder . This idea , introduced in WaveNet , significantly improves efficiency of the model . We define a sparse convolution SC ( with input feature planes , output feature planes , a filter size of , and stride . We assume and to be odd integers , but we can allow generalization to non - square filters , e.g. , or , if we want to implement Inception - style factorised convolutions . An SC convolution computes the set of active sites in the same way as a regular convolution : it looks for the presence of any active sites in its receptive field of size . Deeper networks have greater discriminative power due to the additional non - linearities and better quality of local optima ( ) . However , convolutions with 3D kernels are computationally expensive in comparison to the 2D variants , which hampers the addition of more layers . Additionally , 3D architectures have a larger number of trainable parameters , with each layer adding weights to the model . Then each voxel exams a local neighborhood to determine the final value . We implement this as a trained 3D convolutinoal neural network with 6 layers of 3x3x3 convolutions . This post - processing network is trained by IoU on the same training partition as the point cloud generation network . In order to address this issue , this paper proposes a novel and efficient end-to-end 3D face alignment framework . We build an efficient and stable network model through Depthwise Separable Convolution and Densely Connected Convolutional , named MobDenseNet . Simultaneously , different loss functions are used to constrain 3D parameters based on 3D Morphable Model ( 3DMM ) and 3D vertices . We present a logarithmic-scale efficient convolutional neural network architecture for edge devices , named WaveletNet . Our model is based on the well-known depthwise convolution , and on two new layers , which we introduce in this work : a wavelet convolution and a depthwise fast wavelet transform . By breaking the symmetry in channel dimensions and applying a fast algorithm , WaveletNet shrinks the complexity of convolutional blocks by an O(logD/D ) factor , where D is the number of channels . Depthwise convolution is becoming increasingly popular in modern efficient ConvNets , but its kernel size is often overlooked . In this paper , we systematically study the impact of different kernel sizes , and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency . These methods suffer from high computation and memory cost . To address these issues , we present Depth-aware CNN by introducing two intuitive , flexible and effective operations : depth-aware convolution and depth-aware average pooling . By leveraging depth similarity between pixels in the process of information propagation , geometry is seamlessly incorporated into CNN . One stream is based on the motion characteristics of the human body for detection of falls , while the other is an improved lightweight VGG network , named the MobileVGG , put forward in the paper . The MobileVGG is constructed as a lightweight network model through replacing the traditional convolution with a simplified and efficient combination of point convolution , depth convolution and point convolution . The residual connection between layers is designed to overcome the gradient disappeared and the obstruction of gradient reflux in the deep model . It can adaptively recalibrate neuron-wise feature responses by modelling interdependencies and mutual influence between neurons . Our NA architecture consists of Depthwise Conv and Pointwise Conv , which has slight computation cost and higher performance than SE block by our contrasted experiments . For another , we propose a stage-by-stage unified pattern network architecture , the stage-by-stage strategy guides the later stage by incorporating the useful information in previous stage . In this paper , we proposed a bottleneck-based 3D depthwise separable CNN architecture where the video is disentangled into spatial and temporal information . By applying the basic depthwise convolution concept to each video frame , spatial information can be efficiently learned ; for temporal information , the 3D pointwise convolution can be used to learn the linear combination among sequential frames . To validate and train the proposed model , we collected large quantities of datasets , which are video clips of the physical interactions between two objects under different conditions ( illumination and angle variations ) and the corresponding interaction forces measured by the haptic sensor ( as the ground truth ) . To address this problem , an automatic object detection system based on convolutional neural network ( CNN ) is proposed to detect objects ahead in shunting mode , which is called Feature Fusion Refine neural network ( FR-Net ) . It consists of three connected modules , i.e. , the depthwise-pointwise convolution , the coarse detection module , and the object detection module . Depth-wise-pointwise convolutions are used to improve the detection in real time . document : Depthwise Separable Convolutions for Neural Machine Translation Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency . They have been shown to be successful in image classification models , both in obtaining better models than previously possible for a given parameter count ( the Xception architecture ) and considerably reducing the number of parameters required to perform at a given level ( the MobileNets family of architectures ) . subsection : Separable convolutions and grouped convolutions The depthwise separable convolution operation can be understood as related to both grouped convolutions and the " inception modules " used by the Inception family of convolutional network architectures , a connection explored in Xception xception2016 . It consists of a depthwise convolution , i.e. a spatial convolution performed independently over every channel of an input , followed by a pointwise convolution , i.e. a regular convolution with 1x1 windows , projecting the channels computed by the depthwise convolution onto a new channel space . The depthwise separable convolution operation should not be confused with spatially separable convolutions , which are also often called “ separable convolutions ” in the image processing community . Notably , the use of depthwise separable convolutions in our network in place of regular convolutions makes each convolution operation significantly cheaper ( we are able to cut the number of non - embedding model parameters by half ) , thus lifting the computational and memory limitations that guided the development of filter dilation in the first place . In our experiments , we explore the trade - off between using lower dilation rates and increasing the size of the convolution windows for our depthwise separable convolution layers . In contrast to the conclusions drawn in WaveNet and ByteNet , we find that the computational savings brought on by depthwise separable convolutions allow us to do away with dilation entirely . Our experimental results allow us to draw the following conclusions : Depthwise separable convolutions are strictly superior to regular convolutions in a ByteNet - like architecture , resulting in models that are more accurate while requiring fewer parameters and being computationally cheaper to train and run . Using sub - separable convolutions with groups of size 16 instead of full depthwise separable convolutions results in a performance dip , which may indicate that higher separability ( i.e. groups as small as possible , tending to full depthwise separable convolutions ) is preferable in this setup , this further confirming the advantages of depthwise separable convolutions . The need for dilation can be completely removed by using correspondingly larger convolution windows , which is made computationally tractable by the use of depthwise separable convolutions . We introduce a novel matching algorithm , called DeepMatching , to compute dense correspondences between images . DeepMatching relies on a hierarchical , multi - layer , correlational architecture designed for matching images and was inspired by deep convolutional approaches . The proposed matching algorithm can handle non - rigid deformations and repetitive textures and efficiently determines dense correspondences in the presence of significant changes between images . However , the state-of-the-art CNNs that adopt depthwise separable convolution still require a powerful computing platform such as a Graphics Processing Unit ( GPU ) . In this paper : firstly , we design three kinds of work models for process element(PE ) based on FPGA : standard convolution model , depthwise convolution model and pointwise convolution model ; then we implement MobileNet-SSD on SoC for object detection , and our method requires only limited resources ; besides , we propose a method to transform float-point data into fixed-point data , making computation more convenient . And the results show that our work can detect object at 13 fps . The method based on Vgg16 what is a typical neural network adopts depth-wise separable convolutions and deals with the output feature of every layer to rich feature much further . Then , the pointwise convolution is used to connect the features . Finally , the fully connected layer combines the whole features to classify trash types . On the Receptive Field of Point Convolutions . In this work , we propose Dilated Point Convolutions ( DPC ) which drastically increase the receptive field of convolutions on 3D point clouds . The proposed method uses an adaptive 1D separable convolution with residual connection network to classify gear pitting faults with mixed operating conditions . Compared to the traditional convolutional neural network , the separable convolution with residual connection network can carry out the channel convolution with point-by-point convolution to effectively reduce the number of network parameters . The residual connection can solve the representational bottleneck problem of the features in the model . It can adaptively recalibrate neuron-wise feature responses by modelling interdependencies and mutual influence between neurons . Our NA architecture consists of Depthwise Conv and Pointwise Conv , which has slight computation cost and higher performance than SE block by our contrasted experiments . For another , we propose a stage-by-stage unified pattern network architecture , the stage-by-stage strategy guides the later stage by incorporating the useful information in previous stage . Nevertheless , existing convolutional neural networks commonly have a lot of weight parameters from the Pointwise Convolution ( 1x1 convolution ) , which also induces a high computational cost . In this paper , we propose a new Pointwise Convolution operation with one dimensional Fast Discrete Cosine Transform ( FDCT ) , resulting in dramatically reducing the number of learnable weight parameters and speeding up the process of computation . We propose light-weight convolutional neural networks in two specific aspects : 1 ) Application of DCT on the block structure and 2 ) Application of DCT on the hierarchy level in the CNN models . ; we refer to blocks and layers interchangeably . Each block contains a one dimensional convolution followed by a non - linearity . For a decoder network with a single block and kernel width k , each resulting state h 1 i contains information over k input elements . subsection : Row LSTM The Row LSTM is a unidirectional layer that processes the image row by row from top to bottom computing features for a whole row at once ; the computation is performed with a one - dimensional convolution . For a pixel the layer captures a roughly triangular context above the pixel as shown in Figure [ reference ] ( center ) . subsection : N - gram Feature Extraction through Convolution The one - dimensional convolution involves a filter vector sliding over a sequence and detecting features at different positions . Let be the - dimensional word vectors for the - th word in a sentence . In previous work , e.g. , the form of the two - dimension filter , is a normalised Gaussian . Instead , we used a simple summing filter , equivalent to a kernel with all entries equal to the same value , i.e. In implementing the two - dimensional convolution operation required for filtering using , we obtained the ‘ full ’ convolutional region , which for images of size is , given the ‘ valid ’ convolution first applied using , as described above . The remaining part of the pooling step is to downsample each image dimension by a factor of , resulting in a total of features per image . When formalized like this it becomes clear that both operations depend on the same elements of the previous layer feature map . The pooling layer can be seen as performing a feature - wise convolution in which the activation function is replaced by the p - norm . One can therefore ask the question whether and why such special layers need to be introduced into the network . 4 . The ReLU can be equivalently considered as a part of the second operation ( Non - linear mapping ) , and the first operation ( Patch extraction and representation ) becomes purely linear convolution . Here W 3 corresponds to c filters of a size n 2 × f 3 × f 3 , and B 3 is a c - dimensional vector . The depthwise separable convolution operation can be understood as related to both grouped convolutions and the " inception modules " used by the Inception family of convolutional network architectures , a connection explored in Xception xception2016 . It consists of a depthwise convolution , i.e. a spatial convolution performed independently over every channel of an input , followed by a pointwise convolution , i.e. a regular convolution with 1x1 windows , projecting the channels computed by the depthwise convolution onto a new channel space . The depthwise separable convolution operation should not be confused with spatially separable convolutions , which are also often called “ separable convolutions ” in the image processing community . At test time , these layers are effectively replaced by convolution operations with kernels of 1x1 spatial extent . The entire ConvNet is then simply a sequence of convolutions , max - pooling and thresholding operations exclusively . section : Localization For example , the phrase “ riding bicycles on the streets ” in Table [ reference ] can be well recognized by TBCNN via the dependency relations dobj ( riding , bicycles ) and prep_on ( riding , street ) . As we can see , TBCNN is more robust than sequential convolution in terms of word order distortion , which may be introduced by determinators , modifiers , etc . A pooling layer then aggregates information along the tree , serving as a way of semantic compositonality . In this work , the factoid query answer retrieval task is introduced ; moreover , the effective solving of this task under a deep learning framework is investigated . An attention-based convolutional neural network model is proposed to obtain word- and phrase-level interactive information and generate correct probability to re-rank candidate answers . The performance of the proposed model is compared with other models using the popular benchmark text retrieval conference QA data . We explore two methods to learn continuous node representations from either 1 ) the network structure with node2vec ( Grover and Leskovec , 2016 ) , or 2 ) textual user mentions via doc2vec ( Le and Mikolov , 2014 ) . We combine both methods with input from social media posts in an attention-based convolutional neural network and evaluate the contribution of each component on geolocation performance . Our method enables us to incorporate arbitrarily large networks in a fixed-length vector , without limiting the network size . It also learns an inverse transformation such that cycle NAC PET image generated from the sCT is close to true NAC PET image . Both generators are implemented by a fully convolutional attention network ( FCAN ) , and followed by a discriminator which is structured as a fully convolutional network . A retrospective study was performed with a total of 60 sets of whole-body PET/CT , 40 sets for training and 20 sets for testing .
Recently , impressive improvement has achieved for this task by using deep fully convolutional networks ( FCNs ) . Most of state-of-the-art models rely on 3D convolutions as well as model cascade/ensemble strategies , but at the expense of a significant increase in computational overheads and undesired system complexity . In this study , we segment 3D volume in one-pass with a hierarchical decoupled convolution network ( HDC-Net ) , which is a light-weight but efficient pseudo-3D model .
However , we propose a novel SISR method that uses relatively less number of computations . On training , we get group convolutions that have unused connections removed . We have refined this system specifically for the task at hand by removing unnecessary modules from original CondenseNet .
With the deformable sampling locations , deformable feature images are then created by compressing neighboring similar structural information of each pixel into fixed grids . Therefore , applying the regular convolutions on the deformable feature images can reflect complex structures more effectively . Moreover , instead of adopting the pooling layers , the strided convolution is further introduced on the feature images , which can be learned for feature downsampling according to spatial contexts .
We use these results for finding sharp constants of best approximation in L 1 ( R ) and L ∞ ( R ) on some complex convolution classes . For classes of real-valued convolutions those constants were found by Akhiezer . As an example , we apply these results to the Schwarz-type kernel and to the corresponding convolution classes .
Operation with functions known as pseudo-convolution and its generalization as well as theirs basic properties has been presented . Then , it has been proved that pseudo-convolution which core is pseudo-integral based on max or min decomposable measure can be obtained as limit of g-convolutions , i.e. , pseudo-convolutions with pseudo-integrals based on ⊕-decomposable measures where ⊕ is generated pseudo-addition , as their cores .
We introduce Group equivariant Convolutional Neural Networks ( G-CNNs ) , a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries . G-CNNs use G-convolutions , a new type of layer that enjoys a substantially higher degree of weight sharing than regular convolution layers . G-convolutions increase the expressive capacity of the network without increasing the number of parameters .
As we show in our experiments , the size of the receptive field is directly related to the performance of dense tasks such as semantic segmentation . We look at different network architectures and mechanisms to increase the receptive field size of point convolutions and propose in particular dilated point convolutions . Importantly , our dilation mechanism can easily be integrated into all existing methods using nearest-neighbor-based point convolutions .
JsrNet utilizes the whole group of frames as the reference to reconstruct each frame , regardless of key frames and non-key frames , while the existing frameworks only utilize key frames as the reference to reconstruct non-key frames . Moreover , different from the existing frameworks which only focus on exploiting complementary information between frames in joint reconstruction , JsrNet also applies this conception in joint sampling by adopting learnable convolutions to sample multiple frames jointly and simultaneously in an encoder . JsrNet fully exploits spatial – temporal correlation in both sampling and reconstruction , and achieves a competitive performance in both the quality of reconstruction and computational complexity , making it a promising candidate in source-limited , real-time scenarios .
Targeting the drawbacks of the low accuracy rate and high memory resource consumption of current multilabel annotation methods , this study proposed a CM-supplement network model . This model combines the merits of cavity convolutions , Inception modules and a supplement network . The replacement of common convolutions with cavity convolutions enlarged the receptive field without increasing the number of parameters .
It defines a deep convolution network model , whose contraction properties can be analyzed mathematically . A cascade of wavelet transform convolutions are computed with a multirate filter bank , and adapted with permutations . Unsupervised learning of permutations optimize the contraction directions , by maximizing the average discriminability of training data .
A deep network that incorporates this one binary extremely large and inflecting sparse kernel ( OBELISK ) filter requires fewer trainable parameters and less memory while achieving high quality results compared to fully‐convolutional U‐Net architectures on two challenging 3D CT multi‐organ segmentation tasks . Extensive validation experiments indicate that the performance of sparse deformable convolutions is due to their ability to capture large spatial context with few expressive filter parameters and that network depth is not always necessary to learn complex shape and appearance features . A combination with conventional CNNs further improves the delineation of small organs with large shape variations and the fast inference time using flexible image sampling may offer new potential use cases for deep networks in computer‐assisted , image‐guided interventions .
In this paper , we propose the Laplacian Pyramid Super - Resolution Network ( LapSRN ) to progressively reconstruct the sub - band residuals of high - resolution images . At each pyramid level , our model takes coarse - resolution feature maps as input , predicts the high - frequency residuals , and uses transposed convolutions for upsampling to the finer level . Our method does not require the bicubic interpolation as the pre - processing step and thus dramatically reduces the computational complexity .
This technique reduces runtime by an order of magnitude and enables real - time video SR by independently processing frames with a single frame model . Similar solutions to improve efficiency have also been proposed based on transposed convolutions . subsection : Motivation and contributions
section : Conclusion In this paper we combine the efficiency advantages of sub - pixel convolutions with temporal fusion strategies to present real - time spatio - temporal models for video SR . The spatio - temporal models used are shown to facilitate an improvement in reconstruction accuracy and temporal consistency or reduce computational complexity relative to independent single frame processing .
We apply a 5×5 kernel with 512 outputs , followed by sibling fully connected layers to predict a 14×14 mask ( 14 2 outputs ) and object score ( 1 output ) . The model is implemented in a fully convolutional manner ( using 1×1 convolutions in place of fully connected layers ) . The 7×7 MLP for handling objects at half octave scales is identical to the 5×5 MLP except for its larger input region .
The projection shortcut in Eqn . ( 2 ) is used to match dimensions ( done by 1×1 convolutions ) . For both options , when the shortcuts go across feature maps of two sizes , they are performed with a stride of 2 .
It consists of a depthwise convolution , i.e. a spatial convolution performed independently over every channel of an input , followed by a pointwise convolution , i.e. a regular convolution with 1x1 windows , projecting the channels computed by the depthwise convolution onto a new channel space . The depthwise separable convolution operation should not be confused with spatially separable convolutions , which are also often called “ separable convolutions ” in the image processing community . Their mathematical formulation is as follow ( we use to denote the element - wise product ) :
Fully convolutional neural machine translation without this bottleneck was first achieved in and . The model in ( Extended Neural GPU ) used a recurrent stack of gated convolutional layers , while the model in ( ByteNet ) did away with recursion and used left - padded convolutions in the decoder . This idea , introduced in WaveNet , significantly improves efficiency of the model .
We define a sparse convolution SC ( with input feature planes , output feature planes , a filter size of , and stride . We assume and to be odd integers , but we can allow generalization to non - square filters , e.g. , or , if we want to implement Inception - style factorised convolutions . An SC convolution computes the set of active sites in the same way as a regular convolution : it looks for the presence of any active sites in its receptive field of size .
Deeper networks have greater discriminative power due to the additional non - linearities and better quality of local optima ( ) . However , convolutions with 3D kernels are computationally expensive in comparison to the 2D variants , which hampers the addition of more layers . Additionally , 3D architectures have a larger number of trainable parameters , with each layer adding weights to the model .
Then each voxel exams a local neighborhood to determine the final value . We implement this as a trained 3D convolutinoal neural network with 6 layers of 3x3x3 convolutions . This post - processing network is trained by IoU on the same training partition as the point cloud generation network .
In order to address this issue , this paper proposes a novel and efficient end-to-end 3D face alignment framework . We build an efficient and stable network model through Depthwise Separable Convolution and Densely Connected Convolutional , named MobDenseNet . Simultaneously , different loss functions are used to constrain 3D parameters based on 3D Morphable Model ( 3DMM ) and 3D vertices .
We present a logarithmic-scale efficient convolutional neural network architecture for edge devices , named WaveletNet . Our model is based on the well-known depthwise convolution , and on two new layers , which we introduce in this work : a wavelet convolution and a depthwise fast wavelet transform . By breaking the symmetry in channel dimensions and applying a fast algorithm , WaveletNet shrinks the complexity of convolutional blocks by an O(logD/D ) factor , where D is the number of channels .
Depthwise convolution is becoming increasingly popular in modern efficient ConvNets , but its kernel size is often overlooked . In this paper , we systematically study the impact of different kernel sizes , and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency .
These methods suffer from high computation and memory cost . To address these issues , we present Depth-aware CNN by introducing two intuitive , flexible and effective operations : depth-aware convolution and depth-aware average pooling . By leveraging depth similarity between pixels in the process of information propagation , geometry is seamlessly incorporated into CNN .
One stream is based on the motion characteristics of the human body for detection of falls , while the other is an improved lightweight VGG network , named the MobileVGG , put forward in the paper . The MobileVGG is constructed as a lightweight network model through replacing the traditional convolution with a simplified and efficient combination of point convolution , depth convolution and point convolution . The residual connection between layers is designed to overcome the gradient disappeared and the obstruction of gradient reflux in the deep model .
It can adaptively recalibrate neuron-wise feature responses by modelling interdependencies and mutual influence between neurons . Our NA architecture consists of Depthwise Conv and Pointwise Conv , which has slight computation cost and higher performance than SE block by our contrasted experiments . For another , we propose a stage-by-stage unified pattern network architecture , the stage-by-stage strategy guides the later stage by incorporating the useful information in previous stage .
In this paper , we proposed a bottleneck-based 3D depthwise separable CNN architecture where the video is disentangled into spatial and temporal information . By applying the basic depthwise convolution concept to each video frame , spatial information can be efficiently learned ; for temporal information , the 3D pointwise convolution can be used to learn the linear combination among sequential frames . To validate and train the proposed model , we collected large quantities of datasets , which are video clips of the physical interactions between two objects under different conditions ( illumination and angle variations ) and the corresponding interaction forces measured by the haptic sensor ( as the ground truth ) .
To address this problem , an automatic object detection system based on convolutional neural network ( CNN ) is proposed to detect objects ahead in shunting mode , which is called Feature Fusion Refine neural network ( FR-Net ) . It consists of three connected modules , i.e. , the depthwise-pointwise convolution , the coarse detection module , and the object detection module . Depth-wise-pointwise convolutions are used to improve the detection in real time .
document : Depthwise Separable Convolutions for Neural Machine Translation Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency . They have been shown to be successful in image classification models , both in obtaining better models than previously possible for a given parameter count ( the Xception architecture ) and considerably reducing the number of parameters required to perform at a given level ( the MobileNets family of architectures ) .
subsection : Separable convolutions and grouped convolutions The depthwise separable convolution operation can be understood as related to both grouped convolutions and the " inception modules " used by the Inception family of convolutional network architectures , a connection explored in Xception xception2016 . It consists of a depthwise convolution , i.e. a spatial convolution performed independently over every channel of an input , followed by a pointwise convolution , i.e. a regular convolution with 1x1 windows , projecting the channels computed by the depthwise convolution onto a new channel space . The depthwise separable convolution operation should not be confused with spatially separable convolutions , which are also often called “ separable convolutions ” in the image processing community .
Notably , the use of depthwise separable convolutions in our network in place of regular convolutions makes each convolution operation significantly cheaper ( we are able to cut the number of non - embedding model parameters by half ) , thus lifting the computational and memory limitations that guided the development of filter dilation in the first place . In our experiments , we explore the trade - off between using lower dilation rates and increasing the size of the convolution windows for our depthwise separable convolution layers . In contrast to the conclusions drawn in WaveNet and ByteNet , we find that the computational savings brought on by depthwise separable convolutions allow us to do away with dilation entirely .
Our experimental results allow us to draw the following conclusions : Depthwise separable convolutions are strictly superior to regular convolutions in a ByteNet - like architecture , resulting in models that are more accurate while requiring fewer parameters and being computationally cheaper to train and run . Using sub - separable convolutions with groups of size 16 instead of full depthwise separable convolutions results in a performance dip , which may indicate that higher separability ( i.e. groups as small as possible , tending to full depthwise separable convolutions ) is preferable in this setup , this further confirming the advantages of depthwise separable convolutions . The need for dilation can be completely removed by using correspondingly larger convolution windows , which is made computationally tractable by the use of depthwise separable convolutions .
We introduce a novel matching algorithm , called DeepMatching , to compute dense correspondences between images . DeepMatching relies on a hierarchical , multi - layer , correlational architecture designed for matching images and was inspired by deep convolutional approaches . The proposed matching algorithm can handle non - rigid deformations and repetitive textures and efficiently determines dense correspondences in the presence of significant changes between images .
However , the state-of-the-art CNNs that adopt depthwise separable convolution still require a powerful computing platform such as a Graphics Processing Unit ( GPU ) . In this paper : firstly , we design three kinds of work models for process element(PE ) based on FPGA : standard convolution model , depthwise convolution model and pointwise convolution model ; then we implement MobileNet-SSD on SoC for object detection , and our method requires only limited resources ; besides , we propose a method to transform float-point data into fixed-point data , making computation more convenient . And the results show that our work can detect object at 13 fps .
The method based on Vgg16 what is a typical neural network adopts depth-wise separable convolutions and deals with the output feature of every layer to rich feature much further . Then , the pointwise convolution is used to connect the features . Finally , the fully connected layer combines the whole features to classify trash types .
On the Receptive Field of Point Convolutions . In this work , we propose Dilated Point Convolutions ( DPC ) which drastically increase the receptive field of convolutions on 3D point clouds .
The proposed method uses an adaptive 1D separable convolution with residual connection network to classify gear pitting faults with mixed operating conditions . Compared to the traditional convolutional neural network , the separable convolution with residual connection network can carry out the channel convolution with point-by-point convolution to effectively reduce the number of network parameters . The residual connection can solve the representational bottleneck problem of the features in the model .
It can adaptively recalibrate neuron-wise feature responses by modelling interdependencies and mutual influence between neurons . Our NA architecture consists of Depthwise Conv and Pointwise Conv , which has slight computation cost and higher performance than SE block by our contrasted experiments . For another , we propose a stage-by-stage unified pattern network architecture , the stage-by-stage strategy guides the later stage by incorporating the useful information in previous stage .
Nevertheless , existing convolutional neural networks commonly have a lot of weight parameters from the Pointwise Convolution ( 1x1 convolution ) , which also induces a high computational cost . In this paper , we propose a new Pointwise Convolution operation with one dimensional Fast Discrete Cosine Transform ( FDCT ) , resulting in dramatically reducing the number of learnable weight parameters and speeding up the process of computation . We propose light-weight convolutional neural networks in two specific aspects : 1 ) Application of DCT on the block structure and 2 ) Application of DCT on the hierarchy level in the CNN models .
; we refer to blocks and layers interchangeably . Each block contains a one dimensional convolution followed by a non - linearity . For a decoder network with a single block and kernel width k , each resulting state h 1 i contains information over k input elements .
subsection : Row LSTM The Row LSTM is a unidirectional layer that processes the image row by row from top to bottom computing features for a whole row at once ; the computation is performed with a one - dimensional convolution . For a pixel the layer captures a roughly triangular context above the pixel as shown in Figure [ reference ] ( center ) .
subsection : N - gram Feature Extraction through Convolution The one - dimensional convolution involves a filter vector sliding over a sequence and detecting features at different positions . Let be the - dimensional word vectors for the - th word in a sentence .
In previous work , e.g. , the form of the two - dimension filter , is a normalised Gaussian . Instead , we used a simple summing filter , equivalent to a kernel with all entries equal to the same value , i.e. In implementing the two - dimensional convolution operation required for filtering using , we obtained the ‘ full ’ convolutional region , which for images of size is , given the ‘ valid ’ convolution first applied using , as described above . The remaining part of the pooling step is to downsample each image dimension by a factor of , resulting in a total of features per image .
When formalized like this it becomes clear that both operations depend on the same elements of the previous layer feature map . The pooling layer can be seen as performing a feature - wise convolution in which the activation function is replaced by the p - norm . One can therefore ask the question whether and why such special layers need to be introduced into the network .
4 . The ReLU can be equivalently considered as a part of the second operation ( Non - linear mapping ) , and the first operation ( Patch extraction and representation ) becomes purely linear convolution . Here W 3 corresponds to c filters of a size n 2 × f 3 × f 3 , and B 3 is a c - dimensional vector .
The depthwise separable convolution operation can be understood as related to both grouped convolutions and the " inception modules " used by the Inception family of convolutional network architectures , a connection explored in Xception xception2016 . It consists of a depthwise convolution , i.e. a spatial convolution performed independently over every channel of an input , followed by a pointwise convolution , i.e. a regular convolution with 1x1 windows , projecting the channels computed by the depthwise convolution onto a new channel space . The depthwise separable convolution operation should not be confused with spatially separable convolutions , which are also often called “ separable convolutions ” in the image processing community .
At test time , these layers are effectively replaced by convolution operations with kernels of 1x1 spatial extent . The entire ConvNet is then simply a sequence of convolutions , max - pooling and thresholding operations exclusively . section : Localization
For example , the phrase “ riding bicycles on the streets ” in Table [ reference ] can be well recognized by TBCNN via the dependency relations dobj ( riding , bicycles ) and prep_on ( riding , street ) . As we can see , TBCNN is more robust than sequential convolution in terms of word order distortion , which may be introduced by determinators , modifiers , etc . A pooling layer then aggregates information along the tree , serving as a way of semantic compositonality .
In this work , the factoid query answer retrieval task is introduced ; moreover , the effective solving of this task under a deep learning framework is investigated . An attention-based convolutional neural network model is proposed to obtain word- and phrase-level interactive information and generate correct probability to re-rank candidate answers . The performance of the proposed model is compared with other models using the popular benchmark text retrieval conference QA data .
We explore two methods to learn continuous node representations from either 1 ) the network structure with node2vec ( Grover and Leskovec , 2016 ) , or 2 ) textual user mentions via doc2vec ( Le and Mikolov , 2014 ) . We combine both methods with input from social media posts in an attention-based convolutional neural network and evaluate the contribution of each component on geolocation performance . Our method enables us to incorporate arbitrarily large networks in a fixed-length vector , without limiting the network size .
It also learns an inverse transformation such that cycle NAC PET image generated from the sCT is close to true NAC PET image . Both generators are implemented by a fully convolutional attention network ( FCAN ) , and followed by a discriminator which is structured as a fully convolutional network . A retrospective study was performed with a total of 60 sets of whole-body PET/CT , 40 sets for training and 20 sets for testing .
HDC-Net: Hierarchical Decoupled Convolution Network for Brain Tumor Segmentation. Efficient Single Image Super Resolution using Enhanced Learned Group Convolutions Deformable Convolutional Neural Networks for Hyperspectral Image Classification Exact errors of best approximation for complex-valued nonperiodic functions Pseudo-Convolution Based on Idempotent Operation as Limit of g-Convolution Group Equivariant Convolutional Networks Dilated Point Convolutions: On the Receptive Field of Point Convolutions JsrNet: A Joint Sampling–Reconstruction Framework for Distributed Compressive Video Sensing CM-supplement network model for reducing the memory consumption during multilabel image annotation Unsupervised Learning by Deep Scattering Contractions OBELISK‐Net: Fewer layers to solve 3D multi‐organ segmentation with sparse deformable convolutions Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation Feature Pyramid Networks for Object Detection Deep Residual Learning for Image Recognition Depthwise Separable Convolutions for Neural Machine Translation Submanifold Sparse Convolutional Networks Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation A Point Set Generation Network for 3D Object Reconstruction from a Single Image Robust 3D Face Alignment with Efficient Fully Convolutional Neural Networks WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices MixConv: Mixed Depthwise Convolutional Kernels Depth-aware CNN for RGB-D Segmentation A Two-Stream Approach to Fall Detection With MobileVGG NASNet: A Neuron Attention Stage-by-Stage Net for Single Image Deraining An Efficient Three-Dimensional Convolutional Neural Network for Inferring Physical Interaction Force from Video Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode Depthwise Separable Convolutions for Neural Machine Translation DeepMatching: Hierarchical Deformable Dense Matching SoC implementation of depthwise separable convolution for object detection Classification of Trash Types in Cotton Based on Deep Learning Dilated Point Convolutions: On the Receptive Field of Point Convolutions Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection NASNet: A Neuron Attention Stage-by-Stage Net for Single Image Deraining A New Light-Weight and Efficient Convolutional Neural Network Using Fast Discrete Cosine Transform Convolutional Sequence to Sequence Learning Pixel Recurrent Neural Networks A C-LSTM Neural Network for Text Classification Enhanced image classification with a fast-learning shallow convolutional neural network Striving for Simplicity: The All Convolutional Net Image Super-Resolution Using Deep Convolutional Networks Depthwise Separable Convolutions for Neural Machine Translation OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks Natural Language Inference by Tree-Based Convolution and Heuristic Matching Attention-based neural network for short-text question answering Dense Node Representation for Geolocation PET attenuation correction (AC) using non-AC PET-based synthetic CT
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ICONIP (6) 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) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR ArXiv Medical Image Anal. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICIG (2) ICLR International Journal of Computer Vision ICML ICML ArXiv 2015 International Joint Conference on Neural Networks (IJCNN) ICLR IEEE Transactions on Pattern Analysis and Machine Intelligence ICLR ICLR ACL W-NUT@EMNLP
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