We then benchmark the dataset by designing a novel neural network which can generate density map of an input image . The proposed network consists of three parts namely convolution block attention module ( CBAM ) , scale pyramid module ( SPM ) and deformable convolution module ( DCM ) . Experiments on the proposed dataset and comparisons with state of the art methods demonstrate the challenging of the proposed dataset , and superiority and effectiveness of our method . This is because the traditional convolutional operation is performed on fixed grids , and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions . In this paper , we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework . We aim to capture the target appearance variations via deformable convolution , which adaptively enhances its original features . In order to improve the accuracy of general target detection by the surveillance camera in the monitoring area , an improved SSD model named DC-SSD that integrates the deformable convolution network was proposed . This model is based on the SSD convolutional neural network model , and the idea of deformable convolution is used for reference . The shared convolutional layer makes end-to-end training come true . Additionally , deformable convolution network makes feature extraction better . We adopt the proposed framework to implement experiments on DOTA dataset , attaining mean average precision(mAP)value of 0.834 on the testing dataset , which is an increase of 23 % than the classic Faster RCNN . In addition , the attention-based RNN in this study outperforms other feature fusion methods such as feature-stacking . The deformable convolution operation also yields higher classification accuracy than that of a standard convolution unit . Results demonstrate that the ARCNN could provide an effective way for extracting and aggregating discriminative spatial-temporal features for vegetable mapping from multi-temporal UAV RGB imagery . However , this reliance on a single rigid appearance model is insufficient in situations where the target undergoes non-rigid transformations . In this paper , we propose a unified formulation for learning a deformable convolution filter . In our framework , the deformable filter is represented as a linear combination of sub-filters . However , this reliance on a single rigid appearance model is insufficient in situations where the target undergoes non-rigid transformations . In this paper , we propose a unified formulation for learning a deformable convolution filter . In our framework , the deformable filter is represented as a linear combination of sub-filters . In order to solve the problem of broadband satellite modulation signal with SNR fluctuation of complex channel and various modulation signal recognizing , we propose a Deformable Convolutional Neural Networks ( DCNN ) classification model based on broadband satellite communication systems . In our algorithm , we propose a deformable convolution kernel , which only need to calculate the 2/3 pixel convoluting . Our algorithm not only can be used to reduce the complexity and improve the robustness , but also used to improve the accuracy . After comparing the results of both methods , we found out that deformable U-Net outperformed the previously built improved VGG-Net . These results show that U-Net with deformable convolution structure has a good diagnostic capability and a better performance than the improved VGG-Net . document : Deformable Convolutional Networks Convolutional neural networks ( CNNs ) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules . Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from the target tasks , without additional supervision . The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end - to - end by standard back - propagation , giving rise to deformable convolutional networks . Extensive experiments validate the performance of our approach . To generate new feature maps , it has a weighted summation step , which is absent in STN . Deformable convolution is easy to integrate into any CNN architectures . Its training is easy . The regularization over the filter function space is shown to improve the generalization ability when training data are small . Above works are related to ours in that , when multiple filters , especially with different scales , are combined , the resulting filter could have complex weights and resemble our deformable convolution filter . However , deformable convolution learns sampling locations instead of filter weights . In the remaining experiments , we use in the feature extraction networks . We empirically observed that the learned offsets in the deformable convolution layers are highly adaptive to the image content , as illustrated in Figure [ reference ] and Figure [ reference ] . To better understand the mechanism of deformable convolution , we define a metric called effective dilation for a deformable convolution filter . appendix : Deformable Convolution / RoI Pooling Back - propagation In the deformable convolution Eq . ( [ reference ] ) , The two features maps computed by the very last convolutional layer , having kernel size and producing outputs of the same size as the input volume , are converted to probabilistic segmentations of the foreground and background regions by applying soft - max voxelwise . After each stage of the right portion of the CNN , a de - convolution operation is employed in order increase the size of the inputs ( Figure [ reference ] ) followed by one to three convolutional layers involving half the number of kernels employed in the previous layer . Similar to the left part of the network , also in this case we resort to learn residual functions in the convolutional stages . To address this problem , we propose propagative Hough voting for activity analysis . Instead of letting local features vote individually , we perform feature voting using random projection trees ( RPT ) which leverage the low-dimension manifold structure to match feature points in the high-dimensional feature space . Our RPT can index the unlabeled feature points in an unsupervised way . Using a bag-of-words feature vector we achieve essentially single-instance learning on a benchmark forms database , following an unsupervised clustering stage . Further performance gains are achieved on a more difficult dataset using a feature voting method and feature selection procedure . In this approach , there is no feature selection process in learning phase ; but realtime feature selection is executed in classification phase . Our results show that feature value voting with document-side feature selection can allow fast and accurate text classification system , which seems to be competitive in classification performance with Support Vector Machines , the state-of-the-art text categorization algorithms . This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks ( CNNs ) . In particular , this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the sparsity encountered in the input . To this end , we examine the trade-off between accuracy and speed for different architectures and additionally propose to use an L1 penalty on the filter activations to further encourage sparsity in the intermediate representations . GACEM trains a number of classifiers based on different combinations of feature vectors at first and then selects the classifiers whose weight is higher than the pre-set threshold to make up the ensemble . An empirical study shows that , compared with the conventional feature-level voting and decision-level voting , not only can GACEM achieve better and more robust performance , but also simplify the system markedly . In this paper we develop a theory of the total variation distance between two LMCs , with emphasis on the algorithmic aspects : ( 1 ) we provide a polynomial-time algorithm for determining whether two LMCs have distance 1 , i.e. , whether they can almost always be distinguished ; ( 2 ) we provide an algorithm for approximating the distance with arbitrary precision ; and ( 3 ) we show that the threshold problem , i.e. , whether the distance exceeds a given threshold , is NP-hard and hard for the square-root-sum problem . We also make a connection between the total variation distance and Bernoulli convolutions . We consider the adversarial convex bandit problem and we build the first poly(T)-time algorithm with poly(n ) √T-regret for this problem . To do so we introduce three new ideas in the derivative-free optimization literature : ( i ) kernel methods , ( ii ) a generalization of Bernoulli convolutions , and ( iii ) a new annealing schedule for exponential weights ( with increasing learning rate ) . The basic version of our algorithm achieves Õ(n9.5 # 8730;T)-regret , and we show that a simple variant of this algorithm can be run in poly(n log(T))-time per step at the cost of an additional poly(n ) To(1 ) factor in the regret . To facilitate the incorporation of geometry in deep learning on 3D graphs , we define three types of geometric graph representations : positional , angle-geometric and distance-geometric . For proof of concept , we use the distance-geometric graph representation for geometric graph convolutions . Further , to utilize standard graph convolution networks , we employ a simple edge weight / edge distance correlation scheme , whose parameters can be fixed using reference values or determined through Bayesian hyperparameter optimization . The results of geometric graph convolutions , for the ESOL and Freesol datasets , show significant improvement over those of standard graph convolutions . Our work demonstrates the feasibility and promise of incorporating geometry , using the distance-geometric graph representation , in deep learning on 3D graphs . We present Temporal Aggregation Network ( TAN ) which decomposes 3D convolutions into spatial and temporal aggregation blocks . By stacking spatial and temporal convolutions repeatedly , TAN forms a deep hierarchical representation for capturing spatio-temporal information in videos . Since we do not apply 3D convolutions in each layer but only apply temporal aggregation blocks once after each spatial downsampling layer in the network , we significantly reduce the model complexity . A standard CMOS silicon retina for the acquisition and processing of images is presented . Feature extraction and edge enhancement are obtained through 1-D Gabor convolutions . Low-power ( 1µW per pixel ) and real-time ( less than 20 µsec for complete processing ) operations make this device suitable for embedded smart vision systems . It takes into account a set of conflicting partial criteria , divided into two groups belonging to a certain category of performance . As a result of the assessment , the technology of nested convolutions was applied . The formation of the model is based on the use of methods for solving multi-criteria problems , in particular , the method of reducing it to a single-criteria form by convoluting partial criteria . We further leverage the factorized bilinear model in linear complexity and a bottleneck network design to build our TB blocks , which also constrains the parameters and computation cost . We consider two schemes in terms of the incorporation of TB blocks and the original 2D spatial convolutions , namely wide and deep Temporal Bilinear Networks ( TBN ) . Finally , we perform experiments on several widely adopted datasets including Kinetics , UCF101 and HMDB51 . In this paper , we propose a Joint Spectral Convolutional Network ( JSCN ) for cross-domain recommendation . JSCN will simultaneously operate multi-layer spectral convolutions on different graphs , and jointly learn a domain-invariant user representation with a domain adaptive user mapping module . As a result , the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant user mapping . After training , the activation function g together with AccNet weights automatically define the new splatting , blurring and slicing operations . Experiments demonstrate AccNet is able to design acceleration algorithms for a ton of convolutions including Gaussian/non-Gaussian convolutions and produce state-of-the-art results . As convolutions during forward/backward passes can now operate on low bitwidth weights and activations/gradients respectively , DoReFa-Net can use bit convolution kernels to accelerate both training and inference . Moreover , as bit convolutions can be efficiently implemented on CPU , FPGA , ASIC and GPU , DoReFa-Net opens the way to accelerate training of low bitwidth neural network on these hardware . Our experiments on SVHN and ImageNet datasets prove that DoReFa-Net can achieve comparable prediction accuracy as 32-bit counterparts . CNNs produce estimates for the voxel - wise segmentation labels by classifying each voxel in an image independently taking the neighborhood , i.e. local and contextual image information , into account . This is achieved by sequential convolutions of the input with multiple filters at the cascaded layers of the network . Each layer consists of feature maps ( FMs ) , also referred to as channels . Table [ reference ] shows our results , along with the other two top entries ( ) . Among the other participating methods was the CNN of with 3 layers of 2D convolutions . That method perfomed less well on this challenging task ( ) . This is in contrast to the original GAN paper , which used the maxout activation goodfellow2013maxout . Architecture guidelines for stable Deep Convolutional GANs Replace any pooling layers with strided convolutions ( discriminator ) and fractional - strided convolutions ( generator ) . Use batchnorm in both the generator and the discriminator . More importantly , ResNets and DenseNets generate such large receptive fields that sparsity would almost immediately be destroyed by the explosion in the number of active sites . section : ( Valid ) Sparse Convolutions : SC and VSC We define a sparse convolution SC ( with input feature planes , output feature planes , a filter size of , and stride . In a different yet related model , convert graphs locally into sequences that are fed into a conventional 1D convolutional neural network , which requires the definition of a node ordering in a pre - processing step . Our method is based on spectral graph convolutional neural networks , introduced in and later extended by with fast localized convolutions . In contrast to these works , we consider here the task of transductive node classification within networks of significantly larger scale .
We then benchmark the dataset by designing a novel neural network which can generate density map of an input image . The proposed network consists of three parts namely convolution block attention module ( CBAM ) , scale pyramid module ( SPM ) and deformable convolution module ( DCM ) . Experiments on the proposed dataset and comparisons with state of the art methods demonstrate the challenging of the proposed dataset , and superiority and effectiveness of our method .
This is because the traditional convolutional operation is performed on fixed grids , and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions . In this paper , we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework . We aim to capture the target appearance variations via deformable convolution , which adaptively enhances its original features .
In order to improve the accuracy of general target detection by the surveillance camera in the monitoring area , an improved SSD model named DC-SSD that integrates the deformable convolution network was proposed . This model is based on the SSD convolutional neural network model , and the idea of deformable convolution is used for reference .
The shared convolutional layer makes end-to-end training come true . Additionally , deformable convolution network makes feature extraction better . We adopt the proposed framework to implement experiments on DOTA dataset , attaining mean average precision(mAP)value of 0.834 on the testing dataset , which is an increase of 23 % than the classic Faster RCNN .
In addition , the attention-based RNN in this study outperforms other feature fusion methods such as feature-stacking . The deformable convolution operation also yields higher classification accuracy than that of a standard convolution unit . Results demonstrate that the ARCNN could provide an effective way for extracting and aggregating discriminative spatial-temporal features for vegetable mapping from multi-temporal UAV RGB imagery .
However , this reliance on a single rigid appearance model is insufficient in situations where the target undergoes non-rigid transformations . In this paper , we propose a unified formulation for learning a deformable convolution filter . In our framework , the deformable filter is represented as a linear combination of sub-filters .
However , this reliance on a single rigid appearance model is insufficient in situations where the target undergoes non-rigid transformations . In this paper , we propose a unified formulation for learning a deformable convolution filter . In our framework , the deformable filter is represented as a linear combination of sub-filters .
In order to solve the problem of broadband satellite modulation signal with SNR fluctuation of complex channel and various modulation signal recognizing , we propose a Deformable Convolutional Neural Networks ( DCNN ) classification model based on broadband satellite communication systems . In our algorithm , we propose a deformable convolution kernel , which only need to calculate the 2/3 pixel convoluting . Our algorithm not only can be used to reduce the complexity and improve the robustness , but also used to improve the accuracy .
After comparing the results of both methods , we found out that deformable U-Net outperformed the previously built improved VGG-Net . These results show that U-Net with deformable convolution structure has a good diagnostic capability and a better performance than the improved VGG-Net .
document : Deformable Convolutional Networks Convolutional neural networks ( CNNs ) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules .
Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from the target tasks , without additional supervision . The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end - to - end by standard back - propagation , giving rise to deformable convolutional networks . Extensive experiments validate the performance of our approach .
To generate new feature maps , it has a weighted summation step , which is absent in STN . Deformable convolution is easy to integrate into any CNN architectures . Its training is easy .
The regularization over the filter function space is shown to improve the generalization ability when training data are small . Above works are related to ours in that , when multiple filters , especially with different scales , are combined , the resulting filter could have complex weights and resemble our deformable convolution filter . However , deformable convolution learns sampling locations instead of filter weights .
In the remaining experiments , we use in the feature extraction networks . We empirically observed that the learned offsets in the deformable convolution layers are highly adaptive to the image content , as illustrated in Figure [ reference ] and Figure [ reference ] . To better understand the mechanism of deformable convolution , we define a metric called effective dilation for a deformable convolution filter .
appendix : Deformable Convolution / RoI Pooling Back - propagation In the deformable convolution Eq . ( [ reference ] ) ,
The two features maps computed by the very last convolutional layer , having kernel size and producing outputs of the same size as the input volume , are converted to probabilistic segmentations of the foreground and background regions by applying soft - max voxelwise . After each stage of the right portion of the CNN , a de - convolution operation is employed in order increase the size of the inputs ( Figure [ reference ] ) followed by one to three convolutional layers involving half the number of kernels employed in the previous layer . Similar to the left part of the network , also in this case we resort to learn residual functions in the convolutional stages .
To address this problem , we propose propagative Hough voting for activity analysis . Instead of letting local features vote individually , we perform feature voting using random projection trees ( RPT ) which leverage the low-dimension manifold structure to match feature points in the high-dimensional feature space . Our RPT can index the unlabeled feature points in an unsupervised way .
Using a bag-of-words feature vector we achieve essentially single-instance learning on a benchmark forms database , following an unsupervised clustering stage . Further performance gains are achieved on a more difficult dataset using a feature voting method and feature selection procedure .
In this approach , there is no feature selection process in learning phase ; but realtime feature selection is executed in classification phase . Our results show that feature value voting with document-side feature selection can allow fast and accurate text classification system , which seems to be competitive in classification performance with Support Vector Machines , the state-of-the-art text categorization algorithms .
This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks ( CNNs ) . In particular , this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the sparsity encountered in the input . To this end , we examine the trade-off between accuracy and speed for different architectures and additionally propose to use an L1 penalty on the filter activations to further encourage sparsity in the intermediate representations .
GACEM trains a number of classifiers based on different combinations of feature vectors at first and then selects the classifiers whose weight is higher than the pre-set threshold to make up the ensemble . An empirical study shows that , compared with the conventional feature-level voting and decision-level voting , not only can GACEM achieve better and more robust performance , but also simplify the system markedly .
In this paper we develop a theory of the total variation distance between two LMCs , with emphasis on the algorithmic aspects : ( 1 ) we provide a polynomial-time algorithm for determining whether two LMCs have distance 1 , i.e. , whether they can almost always be distinguished ; ( 2 ) we provide an algorithm for approximating the distance with arbitrary precision ; and ( 3 ) we show that the threshold problem , i.e. , whether the distance exceeds a given threshold , is NP-hard and hard for the square-root-sum problem . We also make a connection between the total variation distance and Bernoulli convolutions .
We consider the adversarial convex bandit problem and we build the first poly(T)-time algorithm with poly(n ) √T-regret for this problem . To do so we introduce three new ideas in the derivative-free optimization literature : ( i ) kernel methods , ( ii ) a generalization of Bernoulli convolutions , and ( iii ) a new annealing schedule for exponential weights ( with increasing learning rate ) . The basic version of our algorithm achieves Õ(n9.5 # 8730;T)-regret , and we show that a simple variant of this algorithm can be run in poly(n log(T))-time per step at the cost of an additional poly(n ) To(1 ) factor in the regret .
To facilitate the incorporation of geometry in deep learning on 3D graphs , we define three types of geometric graph representations : positional , angle-geometric and distance-geometric . For proof of concept , we use the distance-geometric graph representation for geometric graph convolutions . Further , to utilize standard graph convolution networks , we employ a simple edge weight / edge distance correlation scheme , whose parameters can be fixed using reference values or determined through Bayesian hyperparameter optimization . The results of geometric graph convolutions , for the ESOL and Freesol datasets , show significant improvement over those of standard graph convolutions . Our work demonstrates the feasibility and promise of incorporating geometry , using the distance-geometric graph representation , in deep learning on 3D graphs .
We present Temporal Aggregation Network ( TAN ) which decomposes 3D convolutions into spatial and temporal aggregation blocks . By stacking spatial and temporal convolutions repeatedly , TAN forms a deep hierarchical representation for capturing spatio-temporal information in videos . Since we do not apply 3D convolutions in each layer but only apply temporal aggregation blocks once after each spatial downsampling layer in the network , we significantly reduce the model complexity .
A standard CMOS silicon retina for the acquisition and processing of images is presented . Feature extraction and edge enhancement are obtained through 1-D Gabor convolutions . Low-power ( 1µW per pixel ) and real-time ( less than 20 µsec for complete processing ) operations make this device suitable for embedded smart vision systems .
It takes into account a set of conflicting partial criteria , divided into two groups belonging to a certain category of performance . As a result of the assessment , the technology of nested convolutions was applied . The formation of the model is based on the use of methods for solving multi-criteria problems , in particular , the method of reducing it to a single-criteria form by convoluting partial criteria .
We further leverage the factorized bilinear model in linear complexity and a bottleneck network design to build our TB blocks , which also constrains the parameters and computation cost . We consider two schemes in terms of the incorporation of TB blocks and the original 2D spatial convolutions , namely wide and deep Temporal Bilinear Networks ( TBN ) . Finally , we perform experiments on several widely adopted datasets including Kinetics , UCF101 and HMDB51 .
In this paper , we propose a Joint Spectral Convolutional Network ( JSCN ) for cross-domain recommendation . JSCN will simultaneously operate multi-layer spectral convolutions on different graphs , and jointly learn a domain-invariant user representation with a domain adaptive user mapping module . As a result , the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant user mapping .
After training , the activation function g together with AccNet weights automatically define the new splatting , blurring and slicing operations . Experiments demonstrate AccNet is able to design acceleration algorithms for a ton of convolutions including Gaussian/non-Gaussian convolutions and produce state-of-the-art results .
As convolutions during forward/backward passes can now operate on low bitwidth weights and activations/gradients respectively , DoReFa-Net can use bit convolution kernels to accelerate both training and inference . Moreover , as bit convolutions can be efficiently implemented on CPU , FPGA , ASIC and GPU , DoReFa-Net opens the way to accelerate training of low bitwidth neural network on these hardware . Our experiments on SVHN and ImageNet datasets prove that DoReFa-Net can achieve comparable prediction accuracy as 32-bit counterparts .
CNNs produce estimates for the voxel - wise segmentation labels by classifying each voxel in an image independently taking the neighborhood , i.e. local and contextual image information , into account . This is achieved by sequential convolutions of the input with multiple filters at the cascaded layers of the network . Each layer consists of feature maps ( FMs ) , also referred to as channels .
Table [ reference ] shows our results , along with the other two top entries ( ) . Among the other participating methods was the CNN of with 3 layers of 2D convolutions . That method perfomed less well on this challenging task ( ) .
This is in contrast to the original GAN paper , which used the maxout activation goodfellow2013maxout . Architecture guidelines for stable Deep Convolutional GANs Replace any pooling layers with strided convolutions ( discriminator ) and fractional - strided convolutions ( generator ) . Use batchnorm in both the generator and the discriminator .
More importantly , ResNets and DenseNets generate such large receptive fields that sparsity would almost immediately be destroyed by the explosion in the number of active sites . section : ( Valid ) Sparse Convolutions : SC and VSC We define a sparse convolution SC ( with input feature planes , output feature planes , a filter size of , and stride .
In a different yet related model , convert graphs locally into sequences that are fed into a conventional 1D convolutional neural network , which requires the definition of a node ordering in a pre - processing step . Our method is based on spectral graph convolutional neural networks , introduced in and later extended by with fast localized convolutions . In contrast to these works , we consider here the task of transductive node classification within networks of significantly larger scale .
Counting Dense Objects in Remote Sensing Images Deformable Object Tracking With Gated Fusion General Target Detection Method Based on Improved SSD Object Detection of Optical Remote Sensing Image Based on Improved Faster RCNN Multi-Temporal Unmanned Aerial Vehicle Remote Sensing for Vegetable Mapping Using an Attention-Based Recurrent Convolutional Neural Network DCCO: Towards Deformable Continuous Convolution Operators DCCO: Towards Deformable Continuous Convolution Operators for Visual Tracking A modulation classification method based on deformable convolutional neural networks for broadband satellite communication systems Data Driven Intelligent Diagnostics for Parkinson’s Disease Deformable Convolutional Networks V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Propagative Hough Voting for Human Activity Recognition A Graph Lattice Approach to Maintaining and Learning Dense Collections of Subgraphs as Image Features Improving the Performance of a Fast Text Classifier with Document-side Feature Selection Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks GACEM: Genetic Algorithm Based Classifier Ensemble in a Multi-sensor System On the total variation distance of labelled Markov chains Kernel-based methods for bandit convex optimization Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on Three-Dimensional (3D) Graphs TAN: Temporal Aggregation Network for Dense Multi-Label Action Recognition A low-power CMOS silicon retina for feature extraction in real-time, embedded systems Методика багатокритеріального оцінювання відповідності програмного забезпечення вимогам замовника Temporal Bilinear Networks for Video Action Recognition JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation Designing by Training: Acceleration Neural Network for Fast High-Dimensional Convolution DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Submanifold Sparse Convolutional Networks Semi-Supervised Classification with Graph Convolutional Networks
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ICASSP CAIP (1) 2017 IEEE International Conference on Computer Vision (ICCV) 2016 Fourth International Conference on 3D Vision (3DV) ECCV (3) STOC WACV NeurIPS Medical Image Anal. ICLR ArXiv ICLR
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