The purpose of this work is to explore self-supervised learning ( SSL ) strategy to capture a better feature with spatiotemporal 3D convolution . Although one of the next frontier in video recognition must be spatiotemporal 3D CNN , the convergence of the 3D convolutions is really difficult because of their enormous parameters or missing temporal(motion ) feature . One of the effective solutions is to collect a \(10 ^ 5\)-order video database such as Kinetics/Moments in Time . Motivated to circumvent the challenges associated with training complex network architectures , we introduce the concept of spatio-temporal activation reprojection ( STAR ) . More specifically , we reproject the spatio-temporal activations generated by human pose estimation layers in space and time using a stack of 3D convolutions . Experimental results on UTD-MHAD and J-HMDB demonstrate that an end-to-end architecture based on the proposed STAR framework ( which we nickname STAR-Net ) is proficient in single-environment and small-scale applications . The input frames are considered as a cube in our framework . 3-D convolution is performed on it to extract features along spatial and temporal dimension . Image prior knowledge , such as optical flows , is introduced in reconstruction . We propose a novel 3-D convolution method , cv3dconv , for extracting spatiotemporal features from videos . It reduces the number of sum-of-products operations in 3-D convolution by thousands of times by assuming the constant moving velocity of the features . In view of the problem that 3D-CNN can better extract the spatio-temporal features in video , but it requires a high amount of computation and memory , this paper designs an efficient 3D convolutional block to replace the 3 × 3 × 3 convolutional layer with a high amount of computation , and then proposes a 3D-efficient dense residual networks ( 3D-EDRNs ) integrating 3D convolutional blocks for human action recognition . The efficient 3D convolutional block is composed of 1 × 3 × 3 convolutional layers for obtaining spatial features of video and 3 × 1 × 1 convolutional layers for obtaining temporal features of video . In this paper , we first propose a novel hybrid 2D and 3D convolution-based recurrent neural network ( HCRN ) for video-based person re-id task . Specifically , the 3D convolutional module can explore the local short-term fast-varying motion information , while the recurrent layer can leverage the global long-term spatial – temporal information . Based on HCRN , we design a scale-fusion framework to make full use of features of different scales to further improve the performance of video-based person re-id . To remedy this problem , we propose an iterative framework to generate enough face-normal pairs , based on which a convolutional neural networks ( CNNs ) based scheme is designed for guidance normal learning . Moreover , to facilitate the 3D convolution operation in CNNs , for each face in mesh , we propose a voxelization strategy to transform irregular local mesh structure into regular 4D-array form . Finally , guided normal filtering is performed to obtain filtered face normals , according to which denoised positions of vertices are derived . Secondly , the specific structure of the generator is given . How to extract the spatial eigenvalue of energy consumption through 3D convolution operation is introduced , and the experiment is carried out by changing the pooling parameters of the energy consumption to be predicted . The results show that the RessNetGAN model with generated confrontation structure has better prediction performance than the convolutional neural network model , and when the input energy consumption data is very low precision , there will be no serious distortion of the prediction result . The approaches based on spatio-temporal features for video action recognition have emerged such as two-stream based methods and 3D convolution based methods . However , current methods suffer from the problems caused by partial observation , or restricted to single information modeling , and so on . The proposed caching scheme , inspired by the irradiance caching method , is integrated into a hierarchical rendering technique for translucent materials . We propose a split-disk model to determine the cache distribution and derive the subsurface illuminance gradient used for interpolation by reformulating the equation of dipole diffusion approximation as a 3D convolution process . Our experiments show that only a few caches are required to interpolate the entire image , while the visual difference is negligible . Recent hardware developments , in particular those related to graphic processing units ( GPUs ) , have increased the amount of memory available during inference . This has led to a rise in CNN architectures based on 3D convolutions , which have a much larger number of parameters than their 2D counterpart . To fit volumetric data into memory , 3D CNNs typically perform pooling operations that down - sample feature maps across the network . In their work for structural brain segmentation , extracted large 2D patches from multiple scales of the image and combined them with small single - scale 3D patches , in order to avoid the memory requirements of fully 3D networks . One of the reasons that discouraged the use of 3D CNNs is the slow inference due to the computationally expensive 3D convolutions . In contrast to the 2D / 3D hybrid variants ( ) , 3D CNNs can fully exploit dense - inference ( ) , a technique that greatly decreases inference times and which we will further discuss in section [ reference ] . To start the iteration , we simply attach a 1×1 convolutional layer on C 5 to produce the coarsest resolution map . Finally , we append a 3×3 convolution on each merged map to generate the final feature map , which is to reduce the aliasing effect of upsampling . This final set of feature maps is called { P 2 , P 3 , P 4 , P 5 } , corresponding to { C 2 , C 3 , C 4 , C 5 } that are respectively of the same spatial sizes . The first part of the module is a modified version of the residual block . In this modified residual block , we replace the first 3 × 3 convolution module with a corner pooling module , which first processes the features from the backbone network by two 3 × 3 convolution modules 1 with 128 channels and then applies a corner pooling layer . Following the design of a residual block , we then feed the pooled features into a 3 × 3 Conv - BN layer with 256 channels and add back the projection shortcut . The modified residual block is followed by a 3×3 convolution module with 256 channels , and 3 Conv - ReLU - Conv layers to produce the heatmaps , embeddings and offsets . section : Hourglass Network Similar to SSH , we apply 1 1 convolution layers after conv4_3 and conv5_3 to reduce dimension , and then apply a 3 3 convolution layer on the concatenation of these two dimension reduced features . The output feature of the 3 3 convolution layer is the final detection feature map , which will be fed into the detection head for classification and bounding - box regression . The detection feature map has a resolution of of the original image ( of size ) . We conduct more experiments on CIFAR - 10 and 100 datasets [ reference ] . We use the architectures as in [ reference ] and replace the basic residual block by the bottleneck template Our networks start with a single 3×3 conv layer , followed by 3 stages each having 3 residual blocks , and end with average pooling and a fully - connected classifier ( total 29 - layer deep ) , following [ reference ] . We adopt the same translation and flipping data augmentation as [ reference ] . Implementation details are in the appendix . In this paper , to break the limit of the traditional linear models for synthetic aperture radar ( SAR ) image despeckling , we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network ( SAR-DRN ) . SAR-DRN is based on dilated convolutions , which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure . In addition , skip connections and residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem . A classification method of trash types in cotton using depth-wise separable convolutions to enrich features in deep neural networks is proposed . 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 . In this work , we propose two highly compact neural network architectures for semantic segmentation of images , which are up to 100 000 times less complex than state-of-the-art architectures while approaching their accuracy . To decrease the complexity of existing networks , our main ideas consist in exploiting lightweight encoders and decoders with depth-wise separable convolutions and decreasing memory usage with the removal of skip connections between encoder and decoder . Our architectures are designed to be implemented on a basic FPGA such as the one featured on the Intel Altera Cyclone V family of SoCs . We revisit the conventional definition of activity and restrict it to Complex Action : a set of one-actions with a weak temporal pattern that serves a specific purpose . Related works use spatiotemporal 3D convolutions with fixed kernel size , too rigid to capture the varieties in temporal extents of complex actions , and too short for long-range temporal modeling . In contrast , we use multi-scale temporal convolutions , and we reduce the complexity of 3D convolutions . In order to achieve better performance for point cloud analysis , many researchers apply deeper neural networks using stacked Multi-Layer-Perceptron ( MLP ) convolutions over irregular point cloud . However , applying dense MLP convolutions over large amount of points ( e.g. autonomous driving application ) leads to inefficiency in memory and computation . To achieve high performance but less complexity , we propose a deep-wide neural network , called ShufflePointNet , to exploit fine-grained local features and reduce redundancy in parallel using group convolution and channel shuffle operation . The first two strategies help to build a detector with low computation complexity and enough capacity . We use convolution factorization to change traditional convolutions into more sparse depth-wise convolutions to save computation costs and we use successive downsampling convolutions at the beginning of the face detection network . The latter three strategies significantly improve the accuracy of the light-weight detector without adding too much computation costs . We show that group morphology is a proper setting for unifying , formulating and solving a number of important problems , including translational and rotational configuration space problems , mechanism workspace computation , and symmetry detection . The proposed computational approach is based on group convolution algebras , which extend classical convolutions and the Fourier transform to non-commutative groups . In particular , we show that all Minkowski product and quotient operations may be represented implicitly as sublevel sets of the same real-valued convolution function . It captures multi-scale contextual features effectively with very limited extra parameters . Instead of utilizing parallel standard convolutions with different kernel sizes as the existing inception module , our proposed dilated inception module ( DIM ) uses parallel dilated convolutions with different dilation rates which can significantly reduce the computation load while enriching the diversity of receptive fields in feature maps . Moreover , the performance of our saliency model is further improved by using a set of linear normalization-based probability distribution distance metrics as loss functions . These advantages contribute to this algorithm being so easy that it can compute convolutions rapidly . Based on the proposed algorithm a very simple and scalable systolic array without multipliers and ROM has been developed leading to more efficient VLSI implementation of convolutions . subsection : Dilation The masked convolutions use dilation to increase the receptive field of the target network . Dilation makes the receptive field grow exponentially in terms of the depth of the networks , as opposed to linearly . We also consider a second , simplified architecture which shares the same core components as the PixelRNN . We observe that Convolutional Neural Networks ( CNN ) can also be used as sequence model with a fixed dependency range , by using Masked convolutions . The PixelCNN architecture is a fully convolutional network of fifteen layers that preserves the spatial resolution of its input throughout the layers and outputs a conditional distribution at each location . We treated the pixel values as discrete random variables by using a softmax layer in the conditional distributions . We employed masked convolutions to allow PixelRNNs to model full dependencies between the color channels . We proposed and evaluated architectural improvements in these models resulting in PixelRNNs with up to 12 LSTM layers . The architectures of our VGG networks and their performances are presented in Table [ reference ] . We observe that ‘ ‘ regular ’ ’ C convolutions and ‘ ‘ sparse ’ ’ SC convolutions achieve the same error : this result suggests that discarding the ground state has essentially no negative impact on performance . This is an argument for always discarding ground states , as it makes things easier computationally and algorithmically . Recent work has studied two approaches to dealing with the conflicting demands of multi - scale reasoning and full - resolution dense prediction . One approach involves repeated up - convolutions that aim to recover lost resolution while carrying over the global perspective from downsampled layers . This leaves open the question of whether severe intermediate downsampling was truly necessary . The overlapping high - resolution patches are then averaged . As discussed above , this is equivalent to linear convolutions on the n 2 feature maps . If the high - resolution patches used for reconstruction are of size f 3 × f 3 , then the linear filters have an equivalent spatial support of size f 3 × 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 . A series of Partial differential equations are used to extend i sophotes in to the missing area in [1],[2 ] and[11 ] . In [ 12 ] a convolution mask is used to extend the gray levels in to the inpainting area . The curvatures are exten ded into the inpainting area in [ 5 ] . By nature , the clouds are deformable objects with fuzzy , blurred and indefinite contours . The detection methods usually used ( convolution mask for edge detection , linear filtering ... ) are ineffective for this kind of pattern . Nevertheless , the description of clouds , as closed geometrical patterns , is of great practical interest : it allows one to describe the moving cloudy shape with a lot of geometrical parameters ( perimeter , center of gravity , area , energy , orientation of the pattern ... ) that are not valid for an unclosed shape . The processes carried out in transforming scanned schematics to the textual files of a few CAD packages are discussed . After separation of text from graphics , mask convolution is implemented for noise reduction and thinning . Chain vectors are used to establish the possible occurrence of components while tracking . Texture classification is an important issue for a number of applications in machine vision , which could be addressed through learning texture features by using a convolutional mask . However , the traditional convolutional mask has a single orientation , and the learning ability is limited . In addition , the optimization process often falls into the local optima , and the discrimination capacity of the learned mask is unsatisfactory . The neural network parameters ( the type of coupling function and type of element response to the external influence ) are selected in accordance with the solution which we want to receive . The program uses function similar to the Mask Convolution function . This function as any other functions basic for image processing are realized in the Intel Corporation ` Recognition Primitives Library ( RPL ) for the Pentium Processor ' with great efficiency . The neural network parameters ( the type of coupling function and type of element response to the external influence ) are selected in accordance with the solution which we want to receive . The programme uses function similar to the Mask Convolution function . This function as any other functions basic for image processing are realised in the Intel Corporation Recognition Primitives Library ( RPL ) for the Pentium Processor with great efficiency . Various methods , which are based on differential operation , such as Sobel , Prewitt , Roberts , Canny , and etc . have been proposed and widely used . The methods are based on a linear convolution of mask with pre-assigned coefficients . In this paper , we propose an edge detection method based on Dempster-Shafer 's evidence theory to evaluate edgeness of the given pixel . A fuzzy filtering technique based on the tuning of fuzzy membership functions in the frequency domain results in improved restoration of an image which is degraded by additive random noise . An improved performance compared with traditional mask convolution filtering is also evident from the SNR improvement of 4.03 dB. The fuzzy edge detection algorithm provides a variety of edge information . The fuzzy edge detection algorithm is compared with some existing edge detection algorithms to show its novelty.<<ETX > > A new method is proposed and tested to use deep neural network models to detect and repair such defects reliable . The neural network uses masked convolution to predict valid three-dimensional data based on damaged three-dimensional data and a set of two-dimensional images of the scene . The neural model is trained using an advanced online learning system , taking data from and feeding results to multiple inspection systems at the same time . Based on this philosophy , a novel method named scale estimation and spatial attention proposal ( S2AP ) is proposed to pay attention to some specific scales in image pyramid and valid locations in each scales layer . Furthermore , we adopt a masked-convolution operation based on the attention result to accelerate FCN calculation . Experiments show that FCN-based method RPN can be accelerated by about 4Ã— with the help of S2AP and masked-FCN and at the same time it can also achieve the state-of-the-art on FDDB , AFW and MALF face detection benchmarks as well . Specifically , we first introduce the proxy similarity functions and spatial masks to handle the missing reference problem in context modeling . Then , we combine the local and the global context via a non-local attention block and employ it in masked convolutional networks for entropy modeling . The entropy model is further adopted as the rate loss in a joint rate-distortion optimization to guide the training of the analysis transform and the synthesis transform network in transforming coding framework . There are no across the board pooling layers between any two Inception modules , but stride - 2 convolution / pooling layers are employed before the filter concatenation in the modules 3c , 4e . Our model employed separable convolution with depth multiplier on the first convolutional layer . This reduces the computational cost while increasing the memory consumption at training time . Even though decoding must remain entirely sequential during inference , models can take advantage of this parallelism during training . One such approach replaces recurrent layers in the decoder with masked convolution layers kalchbrenner2016neural , gehring2017convolutional that provide the causal structure required by the autoregressive factorization . A recently introduced option which reduces sequential computation still further is to construct the decoder layers out of self - attention computations that have been causally masked in an analogous way . In the upsampling process , one uses a convolutional network with deconvolutional layers to construct an enlarged feature map of size , where is the number of features in the output map of the upsampling network . Then , in the biasing process , for each layer in the conditional PixelRNN , one simply maps the conditioning map into a map that is added to the input - to - state map of the corresponding layer ; this is performed using a unmasked convolution . The larger image is then generated as usual . Moreover , enhancements to the reasoner improve the performance of the entire system as all components use the same reasoner . Mixed-depth representations allow " shallow " or " deep " representations as required . Some representations can always be shallow , and this eliminates unnecessary interpretation . 2D CNNS for video-based action modeling ignore the temporal information and treat the multiple frames analogously to channels . In view of this , a mixed convolution structure implemented with ResNet-18 residual network is designed for video feature extracting . The 3D convolution and the (2 + 1)D convolution are interleaved in sequence throughout the network . Finally , 3D convolution is performed for spatiotemporal modeling simultaneously . Results show that the mixed convolution structure enhances the transmission of temporal information , improves the ability of video feature extraction and the action recognition accuracy obviously We theoretically analyze the receptive field and intensity of mixed convolutions to discover their role in SR . Mixed convolutions remove blind spots and capture the correlation between low-resolution ( LR ) and high-resolution ( HR ) image pairs successfully , thus achieving good generalization ability . We verify those properties of mixed convolutions by training 5-layer and 10-layer networks . Additionally , model skill against climatology also improved significantly . Further analysis discounted changes to model resolution outside of the Australian region having a significant impact on these results , leaving the change in vertical mixing scheme as the main factor in the assessed improvements to mixed layer depth representation . To address these issues , in this paper , we propose a mixed convolutional network ( MCNet ) for hyperspectral image super-resolution . We design a novel mixed convolutional module ( MCM ) to extract the potential features by 2D/3D convolution instead of one convolution , which enables the network to more mine spatial features of hyperspectral image . To explore the effective features from 2D unit , we design the local feature fusion to adaptively analyze from all the hierarchical features in 2D units . First , 2.3 GHz Ground coupled GPR system was used to survey moisture damages on 16 asphalt pavement bridges to create three moisture damage datasets with different resolutions including 2135 moisture damages and 474 steel joints . On this basis , we propose mixed deep convolutional neural networks ( CNN ) including ResNet50 network , for feature extraction , and YOLO v2 network , for recognition , to detect and localize moisture damages . In addition , to prepare the input for the deep learning models , an IRS algorithm is proposed to generate suitable GPR images from GPR data to feed the CNN . In this paper , we propose a novel neural network ( HybridNetSeg ) dedicated to solving this drawback while further improving overall performance . Considering deformable convolution can extract complex and variable structural information , and larger kernel in mixed depthwise convolution makes contribution to higher accuracy . We have integrated these two modules and propose a Hybrid Convolution Block ( HCB ) using the idea of heuristic learning . 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 . Based on this observation , we propose a new mixed depthwise convolution ( MixConv ) , which naturally mixes up multiple kernel sizes in a single convolution . As a simple drop-in replacement of vanilla depthwise convolution , our MixConv improves the accuracy and efficiency for existing MobileNets on both ImageNet classification and COCO object detection . This paper mainly aims at the change of head pose angle to judge people 's attention , and puts forward multi-stage head pose estimation to meet the demand of real-time engineering calculation . In this paper , the first novelty is that the average speed on Windows with CPU(i5 - 7500 3.40GHz ) is 35ms/frame , and the second novelty is that we have collected 64101 face images under the surveillance camera , and the third novelty is that we have mixed depthwise separable convolution for head pose recognition . The method is tested on the UMDFaces Dataset , and the consequences show that compared with extra approaches , the proposed method can obtain improvements in efficiency . As a simple drop-in replacement of vanilla depthwise convolution , our MixConv improves the accuracy and efficiency for existing MobileNets on both ImageNet classification and COCO object detection . To demonstrate the effectiveness of MixConv , we integrate it into AutoML search space and develop a new family of models , named as MixNets , which outperform previous mobile models including MobileNetV2 [ 20 ] ( ImageNet top-1 accuracy + 4.2 % ) , ShuffleNetV2 [ 16 ] ( + 3.5 % ) , MnasNet [ 26 ] ( + 1.3 % ) , ProxylessNAS [ 2 ] ( + 2.2 % ) , and FBNet [ 27 ] ( + 2.0 % ) . In particular , our MixNet-L achieves a new state-of-the-art 78.9 % ImageNet top-1 accuracy under typical mobile settings ( < 600 M FLOPS ) .
The purpose of this work is to explore self-supervised learning ( SSL ) strategy to capture a better feature with spatiotemporal 3D convolution . Although one of the next frontier in video recognition must be spatiotemporal 3D CNN , the convergence of the 3D convolutions is really difficult because of their enormous parameters or missing temporal(motion ) feature . One of the effective solutions is to collect a \(10 ^ 5\)-order video database such as Kinetics/Moments in Time .
Motivated to circumvent the challenges associated with training complex network architectures , we introduce the concept of spatio-temporal activation reprojection ( STAR ) . More specifically , we reproject the spatio-temporal activations generated by human pose estimation layers in space and time using a stack of 3D convolutions . Experimental results on UTD-MHAD and J-HMDB demonstrate that an end-to-end architecture based on the proposed STAR framework ( which we nickname STAR-Net ) is proficient in single-environment and small-scale applications .
The input frames are considered as a cube in our framework . 3-D convolution is performed on it to extract features along spatial and temporal dimension . Image prior knowledge , such as optical flows , is introduced in reconstruction .
We propose a novel 3-D convolution method , cv3dconv , for extracting spatiotemporal features from videos . It reduces the number of sum-of-products operations in 3-D convolution by thousands of times by assuming the constant moving velocity of the features .
In view of the problem that 3D-CNN can better extract the spatio-temporal features in video , but it requires a high amount of computation and memory , this paper designs an efficient 3D convolutional block to replace the 3 × 3 × 3 convolutional layer with a high amount of computation , and then proposes a 3D-efficient dense residual networks ( 3D-EDRNs ) integrating 3D convolutional blocks for human action recognition . The efficient 3D convolutional block is composed of 1 × 3 × 3 convolutional layers for obtaining spatial features of video and 3 × 1 × 1 convolutional layers for obtaining temporal features of video .
In this paper , we first propose a novel hybrid 2D and 3D convolution-based recurrent neural network ( HCRN ) for video-based person re-id task . Specifically , the 3D convolutional module can explore the local short-term fast-varying motion information , while the recurrent layer can leverage the global long-term spatial – temporal information . Based on HCRN , we design a scale-fusion framework to make full use of features of different scales to further improve the performance of video-based person re-id .
To remedy this problem , we propose an iterative framework to generate enough face-normal pairs , based on which a convolutional neural networks ( CNNs ) based scheme is designed for guidance normal learning . Moreover , to facilitate the 3D convolution operation in CNNs , for each face in mesh , we propose a voxelization strategy to transform irregular local mesh structure into regular 4D-array form . Finally , guided normal filtering is performed to obtain filtered face normals , according to which denoised positions of vertices are derived .
Secondly , the specific structure of the generator is given . How to extract the spatial eigenvalue of energy consumption through 3D convolution operation is introduced , and the experiment is carried out by changing the pooling parameters of the energy consumption to be predicted . The results show that the RessNetGAN model with generated confrontation structure has better prediction performance than the convolutional neural network model , and when the input energy consumption data is very low precision , there will be no serious distortion of the prediction result .
The approaches based on spatio-temporal features for video action recognition have emerged such as two-stream based methods and 3D convolution based methods . However , current methods suffer from the problems caused by partial observation , or restricted to single information modeling , and so on .
The proposed caching scheme , inspired by the irradiance caching method , is integrated into a hierarchical rendering technique for translucent materials . We propose a split-disk model to determine the cache distribution and derive the subsurface illuminance gradient used for interpolation by reformulating the equation of dipole diffusion approximation as a 3D convolution process . Our experiments show that only a few caches are required to interpolate the entire image , while the visual difference is negligible .
Recent hardware developments , in particular those related to graphic processing units ( GPUs ) , have increased the amount of memory available during inference . This has led to a rise in CNN architectures based on 3D convolutions , which have a much larger number of parameters than their 2D counterpart . To fit volumetric data into memory , 3D CNNs typically perform pooling operations that down - sample feature maps across the network .
In their work for structural brain segmentation , extracted large 2D patches from multiple scales of the image and combined them with small single - scale 3D patches , in order to avoid the memory requirements of fully 3D networks . One of the reasons that discouraged the use of 3D CNNs is the slow inference due to the computationally expensive 3D convolutions . In contrast to the 2D / 3D hybrid variants ( ) , 3D CNNs can fully exploit dense - inference ( ) , a technique that greatly decreases inference times and which we will further discuss in section [ reference ] .
To start the iteration , we simply attach a 1×1 convolutional layer on C 5 to produce the coarsest resolution map . Finally , we append a 3×3 convolution on each merged map to generate the final feature map , which is to reduce the aliasing effect of upsampling . This final set of feature maps is called { P 2 , P 3 , P 4 , P 5 } , corresponding to { C 2 , C 3 , C 4 , C 5 } that are respectively of the same spatial sizes .
The first part of the module is a modified version of the residual block . In this modified residual block , we replace the first 3 × 3 convolution module with a corner pooling module , which first processes the features from the backbone network by two 3 × 3 convolution modules 1 with 128 channels and then applies a corner pooling layer . Following the design of a residual block , we then feed the pooled features into a 3 × 3 Conv - BN layer with 256 channels and add back the projection shortcut . The modified residual block is followed by a 3×3 convolution module with 256 channels , and 3 Conv - ReLU - Conv layers to produce the heatmaps , embeddings and offsets . section : Hourglass Network
Similar to SSH , we apply 1 1 convolution layers after conv4_3 and conv5_3 to reduce dimension , and then apply a 3 3 convolution layer on the concatenation of these two dimension reduced features . The output feature of the 3 3 convolution layer is the final detection feature map , which will be fed into the detection head for classification and bounding - box regression . The detection feature map has a resolution of of the original image ( of size ) .
We conduct more experiments on CIFAR - 10 and 100 datasets [ reference ] . We use the architectures as in [ reference ] and replace the basic residual block by the bottleneck template Our networks start with a single 3×3 conv layer , followed by 3 stages each having 3 residual blocks , and end with average pooling and a fully - connected classifier ( total 29 - layer deep ) , following [ reference ] . We adopt the same translation and flipping data augmentation as [ reference ] . Implementation details are in the appendix .
In this paper , to break the limit of the traditional linear models for synthetic aperture radar ( SAR ) image despeckling , we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network ( SAR-DRN ) . SAR-DRN is based on dilated convolutions , which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure . In addition , skip connections and residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem .
A classification method of trash types in cotton using depth-wise separable convolutions to enrich features in deep neural networks is proposed . 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 .
In this work , we propose two highly compact neural network architectures for semantic segmentation of images , which are up to 100 000 times less complex than state-of-the-art architectures while approaching their accuracy . To decrease the complexity of existing networks , our main ideas consist in exploiting lightweight encoders and decoders with depth-wise separable convolutions and decreasing memory usage with the removal of skip connections between encoder and decoder . Our architectures are designed to be implemented on a basic FPGA such as the one featured on the Intel Altera Cyclone V family of SoCs .
We revisit the conventional definition of activity and restrict it to Complex Action : a set of one-actions with a weak temporal pattern that serves a specific purpose . Related works use spatiotemporal 3D convolutions with fixed kernel size , too rigid to capture the varieties in temporal extents of complex actions , and too short for long-range temporal modeling . In contrast , we use multi-scale temporal convolutions , and we reduce the complexity of 3D convolutions .
In order to achieve better performance for point cloud analysis , many researchers apply deeper neural networks using stacked Multi-Layer-Perceptron ( MLP ) convolutions over irregular point cloud . However , applying dense MLP convolutions over large amount of points ( e.g. autonomous driving application ) leads to inefficiency in memory and computation . To achieve high performance but less complexity , we propose a deep-wide neural network , called ShufflePointNet , to exploit fine-grained local features and reduce redundancy in parallel using group convolution and channel shuffle operation .
The first two strategies help to build a detector with low computation complexity and enough capacity . We use convolution factorization to change traditional convolutions into more sparse depth-wise convolutions to save computation costs and we use successive downsampling convolutions at the beginning of the face detection network . The latter three strategies significantly improve the accuracy of the light-weight detector without adding too much computation costs .
We show that group morphology is a proper setting for unifying , formulating and solving a number of important problems , including translational and rotational configuration space problems , mechanism workspace computation , and symmetry detection . The proposed computational approach is based on group convolution algebras , which extend classical convolutions and the Fourier transform to non-commutative groups . In particular , we show that all Minkowski product and quotient operations may be represented implicitly as sublevel sets of the same real-valued convolution function .
It captures multi-scale contextual features effectively with very limited extra parameters . Instead of utilizing parallel standard convolutions with different kernel sizes as the existing inception module , our proposed dilated inception module ( DIM ) uses parallel dilated convolutions with different dilation rates which can significantly reduce the computation load while enriching the diversity of receptive fields in feature maps . Moreover , the performance of our saliency model is further improved by using a set of linear normalization-based probability distribution distance metrics as loss functions .
These advantages contribute to this algorithm being so easy that it can compute convolutions rapidly . Based on the proposed algorithm a very simple and scalable systolic array without multipliers and ROM has been developed leading to more efficient VLSI implementation of convolutions .
subsection : Dilation The masked convolutions use dilation to increase the receptive field of the target network . Dilation makes the receptive field grow exponentially in terms of the depth of the networks , as opposed to linearly .
We also consider a second , simplified architecture which shares the same core components as the PixelRNN . We observe that Convolutional Neural Networks ( CNN ) can also be used as sequence model with a fixed dependency range , by using Masked convolutions . The PixelCNN architecture is a fully convolutional network of fifteen layers that preserves the spatial resolution of its input throughout the layers and outputs a conditional distribution at each location .
We treated the pixel values as discrete random variables by using a softmax layer in the conditional distributions . We employed masked convolutions to allow PixelRNNs to model full dependencies between the color channels . We proposed and evaluated architectural improvements in these models resulting in PixelRNNs with up to 12 LSTM layers .
The architectures of our VGG networks and their performances are presented in Table [ reference ] . We observe that ‘ ‘ regular ’ ’ C convolutions and ‘ ‘ sparse ’ ’ SC convolutions achieve the same error : this result suggests that discarding the ground state has essentially no negative impact on performance . This is an argument for always discarding ground states , as it makes things easier computationally and algorithmically .
Recent work has studied two approaches to dealing with the conflicting demands of multi - scale reasoning and full - resolution dense prediction . One approach involves repeated up - convolutions that aim to recover lost resolution while carrying over the global perspective from downsampled layers . This leaves open the question of whether severe intermediate downsampling was truly necessary .
The overlapping high - resolution patches are then averaged . As discussed above , this is equivalent to linear convolutions on the n 2 feature maps . If the high - resolution patches used for reconstruction are of size f 3 × f 3 , then the linear filters have an equivalent spatial support of size f 3 ×
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 .
A series of Partial differential equations are used to extend i sophotes in to the missing area in [1],[2 ] and[11 ] . In [ 12 ] a convolution mask is used to extend the gray levels in to the inpainting area . The curvatures are exten ded into the inpainting area in [ 5 ] .
By nature , the clouds are deformable objects with fuzzy , blurred and indefinite contours . The detection methods usually used ( convolution mask for edge detection , linear filtering ... ) are ineffective for this kind of pattern . Nevertheless , the description of clouds , as closed geometrical patterns , is of great practical interest : it allows one to describe the moving cloudy shape with a lot of geometrical parameters ( perimeter , center of gravity , area , energy , orientation of the pattern ... ) that are not valid for an unclosed shape .
The processes carried out in transforming scanned schematics to the textual files of a few CAD packages are discussed . After separation of text from graphics , mask convolution is implemented for noise reduction and thinning . Chain vectors are used to establish the possible occurrence of components while tracking .
Texture classification is an important issue for a number of applications in machine vision , which could be addressed through learning texture features by using a convolutional mask . However , the traditional convolutional mask has a single orientation , and the learning ability is limited . In addition , the optimization process often falls into the local optima , and the discrimination capacity of the learned mask is unsatisfactory .
The neural network parameters ( the type of coupling function and type of element response to the external influence ) are selected in accordance with the solution which we want to receive . The program uses function similar to the Mask Convolution function . This function as any other functions basic for image processing are realized in the Intel Corporation ` Recognition Primitives Library ( RPL ) for the Pentium Processor ' with great efficiency .
The neural network parameters ( the type of coupling function and type of element response to the external influence ) are selected in accordance with the solution which we want to receive . The programme uses function similar to the Mask Convolution function . This function as any other functions basic for image processing are realised in the Intel Corporation Recognition Primitives Library ( RPL ) for the Pentium Processor with great efficiency .
Various methods , which are based on differential operation , such as Sobel , Prewitt , Roberts , Canny , and etc . have been proposed and widely used . The methods are based on a linear convolution of mask with pre-assigned coefficients . In this paper , we propose an edge detection method based on Dempster-Shafer 's evidence theory to evaluate edgeness of the given pixel .
A fuzzy filtering technique based on the tuning of fuzzy membership functions in the frequency domain results in improved restoration of an image which is degraded by additive random noise . An improved performance compared with traditional mask convolution filtering is also evident from the SNR improvement of 4.03 dB. The fuzzy edge detection algorithm provides a variety of edge information . The fuzzy edge detection algorithm is compared with some existing edge detection algorithms to show its novelty.<<ETX > >
A new method is proposed and tested to use deep neural network models to detect and repair such defects reliable . The neural network uses masked convolution to predict valid three-dimensional data based on damaged three-dimensional data and a set of two-dimensional images of the scene . The neural model is trained using an advanced online learning system , taking data from and feeding results to multiple inspection systems at the same time .
Based on this philosophy , a novel method named scale estimation and spatial attention proposal ( S2AP ) is proposed to pay attention to some specific scales in image pyramid and valid locations in each scales layer . Furthermore , we adopt a masked-convolution operation based on the attention result to accelerate FCN calculation . Experiments show that FCN-based method RPN can be accelerated by about 4Ã— with the help of S2AP and masked-FCN and at the same time it can also achieve the state-of-the-art on FDDB , AFW and MALF face detection benchmarks as well .
Specifically , we first introduce the proxy similarity functions and spatial masks to handle the missing reference problem in context modeling . Then , we combine the local and the global context via a non-local attention block and employ it in masked convolutional networks for entropy modeling . The entropy model is further adopted as the rate loss in a joint rate-distortion optimization to guide the training of the analysis transform and the synthesis transform network in transforming coding framework .
There are no across the board pooling layers between any two Inception modules , but stride - 2 convolution / pooling layers are employed before the filter concatenation in the modules 3c , 4e . Our model employed separable convolution with depth multiplier on the first convolutional layer . This reduces the computational cost while increasing the memory consumption at training time .
Even though decoding must remain entirely sequential during inference , models can take advantage of this parallelism during training . One such approach replaces recurrent layers in the decoder with masked convolution layers kalchbrenner2016neural , gehring2017convolutional that provide the causal structure required by the autoregressive factorization . A recently introduced option which reduces sequential computation still further is to construct the decoder layers out of self - attention computations that have been causally masked in an analogous way .
In the upsampling process , one uses a convolutional network with deconvolutional layers to construct an enlarged feature map of size , where is the number of features in the output map of the upsampling network . Then , in the biasing process , for each layer in the conditional PixelRNN , one simply maps the conditioning map into a map that is added to the input - to - state map of the corresponding layer ; this is performed using a unmasked convolution . The larger image is then generated as usual .
Moreover , enhancements to the reasoner improve the performance of the entire system as all components use the same reasoner . Mixed-depth representations allow " shallow " or " deep " representations as required . Some representations can always be shallow , and this eliminates unnecessary interpretation .
2D CNNS for video-based action modeling ignore the temporal information and treat the multiple frames analogously to channels . In view of this , a mixed convolution structure implemented with ResNet-18 residual network is designed for video feature extracting . The 3D convolution and the (2 + 1)D convolution are interleaved in sequence throughout the network .
Finally , 3D convolution is performed for spatiotemporal modeling simultaneously . Results show that the mixed convolution structure enhances the transmission of temporal information , improves the ability of video feature extraction and the action recognition accuracy obviously
We theoretically analyze the receptive field and intensity of mixed convolutions to discover their role in SR . Mixed convolutions remove blind spots and capture the correlation between low-resolution ( LR ) and high-resolution ( HR ) image pairs successfully , thus achieving good generalization ability . We verify those properties of mixed convolutions by training 5-layer and 10-layer networks .
Additionally , model skill against climatology also improved significantly . Further analysis discounted changes to model resolution outside of the Australian region having a significant impact on these results , leaving the change in vertical mixing scheme as the main factor in the assessed improvements to mixed layer depth representation .
To address these issues , in this paper , we propose a mixed convolutional network ( MCNet ) for hyperspectral image super-resolution . We design a novel mixed convolutional module ( MCM ) to extract the potential features by 2D/3D convolution instead of one convolution , which enables the network to more mine spatial features of hyperspectral image . To explore the effective features from 2D unit , we design the local feature fusion to adaptively analyze from all the hierarchical features in 2D units .
First , 2.3 GHz Ground coupled GPR system was used to survey moisture damages on 16 asphalt pavement bridges to create three moisture damage datasets with different resolutions including 2135 moisture damages and 474 steel joints . On this basis , we propose mixed deep convolutional neural networks ( CNN ) including ResNet50 network , for feature extraction , and YOLO v2 network , for recognition , to detect and localize moisture damages . In addition , to prepare the input for the deep learning models , an IRS algorithm is proposed to generate suitable GPR images from GPR data to feed the CNN .
In this paper , we propose a novel neural network ( HybridNetSeg ) dedicated to solving this drawback while further improving overall performance . Considering deformable convolution can extract complex and variable structural information , and larger kernel in mixed depthwise convolution makes contribution to higher accuracy . We have integrated these two modules and propose a Hybrid Convolution Block ( HCB ) using the idea of heuristic learning .
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 . Based on this observation , we propose a new mixed depthwise convolution ( MixConv ) , which naturally mixes up multiple kernel sizes in a single convolution . As a simple drop-in replacement of vanilla depthwise convolution , our MixConv improves the accuracy and efficiency for existing MobileNets on both ImageNet classification and COCO object detection .
This paper mainly aims at the change of head pose angle to judge people 's attention , and puts forward multi-stage head pose estimation to meet the demand of real-time engineering calculation . In this paper , the first novelty is that the average speed on Windows with CPU(i5 - 7500 3.40GHz ) is 35ms/frame , and the second novelty is that we have collected 64101 face images under the surveillance camera , and the third novelty is that we have mixed depthwise separable convolution for head pose recognition . The method is tested on the UMDFaces Dataset , and the consequences show that compared with extra approaches , the proposed method can obtain improvements in efficiency .
As a simple drop-in replacement of vanilla depthwise convolution , our MixConv improves the accuracy and efficiency for existing MobileNets on both ImageNet classification and COCO object detection . To demonstrate the effectiveness of MixConv , we integrate it into AutoML search space and develop a new family of models , named as MixNets , which outperform previous mobile models including MobileNetV2 [ 20 ] ( ImageNet top-1 accuracy + 4.2 % ) , ShuffleNetV2 [ 16 ] ( + 3.5 % ) , MnasNet [ 26 ] ( + 1.3 % ) , ProxylessNAS [ 2 ] ( + 2.2 % ) , and FBNet [ 27 ] ( + 2.0 % ) . In particular , our MixNet-L achieves a new state-of-the-art 78.9 % ImageNet top-1 accuracy under typical mobile settings ( < 600 M FLOPS ) .
Learning Spatiotemporal 3D Convolution with Video Order Self-supervision STAR-Net: Action Recognition using Spatio-Temporal Activation Reprojection Structure-preserving video super-resolution using three-dimensional convolutional neural networks Constant Velocity 3D Convolution Efficient 3D dense residual network and its application in human action recognition Scale-fusion framework for improving video-based person re-identification performance NormalNet: Learning based Guided Normal Filtering for Mesh Denoising Research on Deep Learning Energy Consumption Prediction Based on Generating Confrontation Network Video-level Multi-model Fusion for Action Recognition An efficient caching-based rendering of translucent materials Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Feature Pyramid Networks for Object Detection CornerNet: Detecting Objects as Paired Keypoints Robust Face Detection via Learning Small Faces on Hard Images Aggregated Residual Transformations for Deep Neural Networks Learning a Dilated Residual Network for SAR Image Despeckling Classification of Trash Types in Cotton Based on Deep Learning Low-Power Neural Networks for Semantic Segmentation of Satellite Images Timeception for Complex Action Recognition Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions Real-Time Multi-Scale Face Detector on Embedded Devices Group morphology with convolution algebras A Dilated Inception Network for Visual Saliency Prediction Novel Convolutions Using First-Order Moments Neural Machine Translation in Linear Time Pixel Recurrent Neural Networks Submanifold Sparse Convolutional Networks Multi-Scale Context Aggregation by Dilated Convolutions Image Super-Resolution Using Deep Convolutional Networks Depthwise Separable Convolutions for Neural Machine Translation Comparative Analysis of Structure and Texture based Image Inpainting Techniques Active contour models for infrared cloudy shapes segmentation An Hough based recognition system for electronic schematics conversion to CAD format Ant Lion Optimizer for Texture Classification: A Moving Convolutional Mask Image preprocessing by neuronlike algorithms Image pre-processing by neuron-like algorithms Dempster-Shafer's Evidence Theory-based Edge Detection Image processing-enhancement, filtering and edge detection using the fuzzy logic approach Deep Learning Based Fault Correction in 3D Measurements of Printed Circuit Boards Beyond Trade-Off: Accelerate FCN-Based Face Detector with Higher Accuracy Learning Context-Based Non-local Entropy Modeling for Image Compression Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Non-Autoregressive Neural Machine Translation Pixel Recurrent Neural Networks Efficient Representations for Multi-Modal Interaction Mixed 3D-(2+1)D convolution for action recognition DCSR: Dilated Convolutions for Single Image Super-Resolution Verification of the mixed layer depth in the OceanMAPS operational forecast model Mixed 2D/3D Convolutional Network for Hyperspectral Image Super-Resolution Automatic detection of moisture damages in asphalt pavements from GPR data with deep CNN and IRS method HybridNetSeg: A Compact Hybrid Network for Retinal Vessel Segmentation MixConv: Mixed Depthwise Convolutional Kernels Multi-stage Real-time Human Head Pose Estimation MixConv: Mixed Depthwise Convolutional Kernels
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ECCV Workshops (2) CIKM Comput. Medical Imaging Graph. Medical Image Anal. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICCV Workshops Symposium on Solid and Physical Modeling ArXiv ICML ArXiv ICLR IEEE Transactions on Pattern Analysis and Machine Intelligence ICLR ICML ICLR ICML ICSAI
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