111 citations · 219 across the 21 of their papers we have counts for
21 papers
Revisiting Vision Transformer from the View of Path Ensemble
Shuning Chang, Pichao Wang, Hao Luo +2
Vision Transformers (ViTs) are normally regarded as a stack of transformer layers. In this work, we propose a novel view of ViTs showing that they can be seen as ensemble networks…
DOAD: Decoupled One Stage Action Detection Network
Shuning Chang, Pichao Wang, Fan Wang +2
Localizing people and recognizing their actions from videos is a challenging task towards high-level video understanding. Existing methods are mostly two-stage based, with one stag…
Making Vision Transformers Efficient from A Token Sparsification View
Shuning Chang, Pichao Wang, Ming Lin +4
The quadratic computational complexity to the number of tokens limits the practical applications of Vision Transformers (ViTs). Several works propose to prune redundant tokens to a…
EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
Hansheng Chen, Wei Tian, Pichao Wang +3
Locating 3D objects from a single RGB image via Perspective-n-Point (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest…
Head-Free Lightweight Semantic Segmentation with Linear Transformer
Bo Dong, Pichao Wang, Fan Wang
Existing semantic segmentation works have been mainly focused on designing effective decoders; however, the computational load introduced by the overall structure has long been ign…
A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion Recognition
Benjia Zhou, Pichao Wang, Jun Wan +2
Motion recognition is a promising direction in computer vision, but the training of video classification models is much harder than images due to insufficient data and considerable…
Effective Vision Transformer Training: A Data-Centric Perspective
Benjia Zhou, Pichao Wang, Jun Wan +2
Vision Transformers (ViTs) have shown promising performance compared with Convolutional Neural Networks (CNNs), but the training of ViTs is much harder than CNNs. In this paper, we…
Dynamic Gradient Reactivation for Backward Compatible Person Re-identification
Xiao Pan, Hao Luo, Weihua Chen +6
We study the backward compatible problem for person re-identification (Re-ID), which aims to constrain the features of an updated new model to be comparable with the existing featu…
EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
Hansheng Chen, Pichao Wang, Fan Wang +3
Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest…
Image-to-Video Re-Identification via Mutual Discriminative Knowledge Transfer
Pichao Wang, Fan Wang, Hao Li
The gap in representations between image and video makes Image-to-Video Re-identification (I2V Re-ID) challenging, and recent works formulate this problem as a knowledge distillati…
TAGPerson: A Target-Aware Generation Pipeline for Person Re-identification
Kai Chen, Weihua Chen, Tao He +5
Nowadays, real data in person re-identification (ReID) task is facing privacy issues, e.g., the banned dataset DukeMTMC-ReID. Thus it becomes much harder to collect real data for R…
ELSA: Enhanced Local Self-Attention for Vision Transformer
Jingkai Zhou, Pichao Wang, Fan Wang +3
Self-attention is powerful in modeling long-range dependencies, but it is weak in local finer-level feature learning. The performance of local self-attention (LSA) is just on par w…
Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition
Benjia Zhou, Pichao Wang, Jun Wan +6
Decoupling spatiotemporal representation refers to decomposing the spatial and temporal features into dimension-independent factors. Although previous RGB-D-based motion recognitio…
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation
Zhaoyuan Yin, Pichao Wang, Fan Wang +4
Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations. Most existing methods are bottom-up ap…
Self-Supervised Pre-Training for Transformer-Based Person Re-Identification
Hao Luo, Pichao Wang, Yi Xu +5
Transformer-based supervised pre-training achieves great performance in person re-identification (ReID). However, due to the domain gap between ImageNet and ReID datasets, it usual…
Achieving Human Parity on Visual Question Answering
Ming Yan, Haiyang Xu, Chenliang Li +14
The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image. It has been a popular research topi…
2nd Place Solution to Google Landmark Retrieval 2021
Zhang Yuqi, Xu Xianzhe, Chen Weihua +4
This paper presents the 2nd place solution to the Google Landmark Retrieval 2021 Competition on Kaggle. The solution is based on a baseline with training tricks from person re-iden…
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation
Tongkun Xu, Weihua Chen, Pichao Wang +3
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to a different unlabeled target domain. Most existing UDA methods focus on lear…
Scaled ReLU Matters for Training Vision Transformers
Pichao Wang, Xue Wang, Hao Luo +5
Vision transformers (ViTs) have been an alternative design paradigm to convolutional neural networks (CNNs). However, the training of ViTs is much harder than CNNs, as it is sensit…
Exploring the Quality of GAN Generated Images for Person Re-Identification
Yiqi Jiang, Weihua Chen, Xiuyu Sun +3
Recently, GAN based method has demonstrated strong effectiveness in generating augmentation data for person re-identification (ReID), on account of its ability to bridge the gap be…
Graph Convolution for Re-ranking in Person Re-identification
Yuqi Zhang, Qian Qi, Chong Liu +4
Nowadays, deep learning is widely applied to extract features for similarity computation in person re-identification (re-ID) and have achieved great success. However, due to the no…