19 citations · 125 across the 28 of their papers we have counts for
7 papers · 2 filters
Learning Tracking Representations via Dual-Branch Fully Transformer Networks
Fei Xie, Chunyu Wang, Guangting Wang +2
We present a Siamese-like Dual-branch network based on solely Transformers for tracking. Given a template and a search image, we divide them into non-overlapping patches and extrac…
Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware Association
Sen Yang, Zhicheng Wang, Ze Chen +7
This paper presents a new method to solve keypoint detection and instance association by using Transformer. For bottom-up multi-person pose estimation models, they need to detect k…
Video Based Fall Detection Using Human Poses
Ziwei Chen, Yiye Wang, Wankou Yang
Video based fall detection accuracy has been largely improved due to the recent progress on deep convolutional neural networks. However, there still exists some challenges, such as…
SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation
Yanjie Li, Sen Yang, Peidong Liu +5
The 2D heatmap-based approaches have dominated Human Pose Estimation (HPE) for years due to high performance. However, the long-standing quantization error problem in the 2D heatma…
TokenPose: Learning Keypoint Tokens for Human Pose Estimation
Yanjie Li, Shoukui Zhang, Zhicheng Wang +4
Human pose estimation deeply relies on visual clues and anatomical constraints between parts to locate keypoints. Most existing CNN-based methods do well in visual representation,…
SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud
Ziyu Li, Yuncong Yao, Zhibin Quan +2
LiDAR-based 3D object detection pushes forward an immense influence on autonomous vehicles. Due to the limitation of the intrinsic properties of LiDAR, fewer points are collected a…
Separable Batch Normalization for Robust Facial Landmark Localization with Cross-protocol Network Training
Shuangping Jin, Zhenhua Feng, Wankou Yang +1
A big, diverse and balanced training data is the key to the success of deep neural network training. However, existing publicly available datasets used in facial landmark localizat…