1.9k citations · 4.9k across the 47 of their papers we have counts for
32 papers · 1 filter
DAT++: Spatially Dynamic Vision Transformer with Deformable Attention
Zhuofan Xia, Xuran Pan, Shiji Song +2
Transformers have shown superior performance on various vision tasks. Their large receptive field endows Transformer models with higher representation power than their CNN counterp…
Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition
Sheng-Lan Liu, Yu-Ning Ding, Jin-Rong Zhang +4
Graph convolutional networks have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the simi…
DiSparse: Disentangled Sparsification for Multitask Model Compression
Xinglong Sun, Ali Hassani, Zhangyang Wang +2
Despite the popularity of Model Compression and Multitask Learning, how to effectively compress a multitask model has been less thoroughly analyzed due to the challenging entanglem…
SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation
Binhui Xie, Shuang Li, Mingjia Li +3
Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the supervised model trained on a labeled source do…
Assessing a Single Image in Reference-Guided Image Synthesis
Jiayi Guo, Chaoqun Du, Jiangshan Wang +3
Assessing the performance of Generative Adversarial Networks (GANs) has been an important topic due to its practical significance. Although several evaluation metrics have been pro…
Integrating Large Circular Kernels into CNNs through Neural Architecture Search
Kun He, Chao Li, Yixiao Yang +2
The square kernel is a standard unit for contemporary CNNs, as it fits well on the tensor computation for convolution operation. However, the retinal ganglion cells in the biologic…
Generalized Domain Conditioned Adaptation Network
Shuang Li, Binhui Xie, Qiuxia Lin +3
Domain Adaptation (DA) attempts to transfer knowledge learned in the labeled source domain to the unlabeled but related target domain without requiring large amounts of target supe…
Dynamic Neural Networks: A Survey
Yizeng Han, Gao Huang, Shiji Song +3
Dynamic neural network is an emerging research topic in deep learning. Compared to static models which have fixed computational graphs and parameters at the inference stage, dynami…
Revisiting Locally Supervised Learning: an Alternative to End-to-end Training
Yulin Wang, Zanlin Ni, Shiji Song +2
Due to the need to store the intermediate activations for back-propagation, end-to-end (E2E) training of deep networks usually suffers from high GPUs memory footprint. This paper a…
3D Object Detection with Pointformer
Xuran Pan, Zhuofan Xia, Shiji Song +2
Feature learning for 3D object detection from point clouds is very challenging due to the irregularity of 3D point cloud data. In this paper, we propose Pointformer, a Transformer…
Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving
Mu Cai, Hong Zhang, Huijuan Huang +3
Image-to-image translation has been revolutionized with GAN-based methods. However, existing methods lack the ability to preserve the identity of the source domain. As a result, sy…
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
Yulin Wang, Kangchen Lv, Rui Huang +3
The accuracy of deep convolutional neural networks (CNNs) generally improves when fueled with high resolution images. However, this often comes at a high computational cost and hig…
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation
Hao Li, Chenxin Tao, Xizhou Zhu +3
Designing proper loss functions is essential in training deep networks. Especially in the field of semantic segmentation, various evaluation metrics have been proposed for diverse…
Regularizing Deep Networks with Semantic Data Augmentation
Yulin Wang, Gao Huang, Shiji Song +3
Data augmentation is widely known as a simple yet surprisingly effective technique for regularizing deep networks. Conventional data augmentation schemes, e.g., flipping, translati…
Domain Conditioned Adaptation Network
Shuang Li, Chi Harold Liu, Qiuxia Lin +4
Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature r…
Deep Residual Correction Network for Partial Domain Adaptation
Shuang Li, Chi Harold Liu, Qiuxia Lin +4
Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different but related unlabeled t…
Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation
Zhenda Xie, Zheng Zhang, Xizhou Zhu +2
In the feature maps of CNNs, there commonly exists considerable spatial redundancy that leads to much repetitive processing. Towards reducing this superfluous computation, we propo…
Resolution Adaptive Networks for Efficient Inference
Le Yang, Yizeng Han, Xi Chen +3
Adaptive inference is an effective mechanism to achieve a dynamic tradeoff between accuracy and computational cost in deep networks. Existing works mainly exploit architecture redu…
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning
Wenjie Shi, Gao Huang, Shiji Song +3
Deep reinforcement learning (RL) has recently led to many breakthroughs on a range of complex control tasks. However, the agent's decision-making process is generally not transpare…
FSD-10: A Dataset for Competitive Sports Content Analysis
Shenlan Liu, Xiang Liu, Gao Huang +6
Action recognition is an important and challenging problem in video analysis. Although the past decade has witnessed progress in action recognition with the development of deep lea…
Gated Path Selection Network for Semantic Segmentation
Qichuan Geng, Hong Zhang, Xiaojuan Qi +3
Semantic segmentation is a challenging task that needs to handle large scale variations, deformations and different viewpoints. In this paper, we develop a novel network named Gate…
Implicit Semantic Data Augmentation for Deep Networks
Yulin Wang, Xuran Pan, Shiji Song +3
In this paper, we propose a novel implicit semantic data augmentation (ISDA) approach to complement traditional augmentation techniques like flipping, translation or rotation. Our…
Improved Techniques for Training Adaptive Deep Networks
Hao Li, Hong Zhang, Xiaojuan Qi +2
Adaptive inference is a promising technique to improve the computational efficiency of deep models at test time. In contrast to static models which use the same computation graph f…
Domain-Aware SE Network for Sketch-based Image Retrieval with Multiplicative Euclidean Margin Softmax
Peng Lu, Gao Huang, Hangyu Lin +3
This paper proposes a novel approach for Sketch-Based Image Retrieval (SBIR), for which the key is to bridge the gap between sketches and photos in terms of the data representation…