136 citations · 823 across the 50 of their papers we have counts for
14 papers · 1 filter
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…
MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation
Wenhao Li, Hong Liu, Hao Tang +2
Estimating 3D human poses from monocular videos is a challenging task due to depth ambiguity and self-occlusion. Most existing works attempt to solve both issues by exploiting spat…
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…
KVT: k-NN Attention for Boosting Vision Transformers
Pichao Wang, Xue Wang, Fan Wang +4
Convolutional Neural Networks (CNNs) have dominated computer vision for years, due to its ability in capturing locality and translation invariance. Recently, many vision transforme…
TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection
Zitong Yu, Xiaobai Li, Pichao Wang +1
3D mask face presentation attack detection (PAD) plays a vital role in securing face recognition systems from emergent 3D mask attacks. Recently, remote photoplethysmography (rPPG)…
Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation
Shuning Chang, Pichao Wang, Fan Wang +2
Temporal action proposal generation (TAPG) is a fundamental and challenging task in video understanding, especially in temporal action detection. Most previous works focus on captu…
TransReID: Transformer-based Object Re-Identification
Shuting He, Hao Luo, Pichao Wang +3
Extracting robust feature representation is one of the key challenges in object re-identification (ReID). Although convolution neural network (CNN)-based methods have achieved grea…
Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation
Wenhao Li, Hong Liu, Runwei Ding +3
Despite the great progress in 3D human pose estimation from videos, it is still an open problem to take full advantage of a redundant 2D pose sequence to learn representative repre…
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition
Ming Lin, Pichao Wang, Zhenhong Sun +5
Accuracy predictor is a key component in Neural Architecture Search (NAS) for ranking architectures. Building a high-quality accuracy predictor usually costs enormous computation.…
Trear: Transformer-based RGB-D Egocentric Action Recognition
Xiangyu Li, Yonghong Hou, Pichao Wang +3
In this paper, we propose a \textbf{Tr}ansformer-based RGB-D \textbf{e}gocentric \textbf{a}ction \textbf{r}ecognition framework, called Trear. It consists of two modules, inter-fra…