Search Results for author: Erjin Zhou

Found 16 papers, 8 papers with code

Guiding Query Position and Performing Similar Attention for Transformer-Based Detection Heads

no code implementations22 Aug 2021 Xiaohu Jiang, Ze Chen, Zhicheng Wang, Erjin Zhou, ChunYuan

After DETR was proposed, this novel transformer-based detection paradigm which performs several cross-attentions between object queries and feature maps for predictions has subsequently derived a series of transformer-based detection heads.

Adaptive Dilated Convolution For Human Pose Estimation

no code implementations22 Jul 2021 Zhengxiong Luo, Zhicheng Wang, Yan Huang, Liang Wang, Tieniu Tan, Erjin Zhou

It can generate and fuse multi-scale features of the same spatial sizes by setting different dilation rates for different channels.

Pose Estimation

Graph-MLP: Node Classification without Message Passing in Graph

1 code implementation8 Jun 2021 Yang Hu, Haoxuan You, Zhecan Wang, Zhicheng Wang, Erjin Zhou, Yue Gao

Graph Neural Network (GNN) has been demonstrated its effectiveness in dealing with non-Euclidean structural data.

Node Classification

TokenPose: Learning Keypoint Tokens for Human Pose Estimation

1 code implementation ICCV 2021 YanJie Li, Shoukui Zhang, Zhicheng Wang, Sen yang, Wankou Yang, Shu-Tao Xia, Erjin Zhou

Most existing CNN-based methods do well in visual representation, however, lacking in the ability to explicitly learn the constraint relationships between keypoints.

Pose Estimation

V2F-Net: Explicit Decomposition of Occluded Pedestrian Detection

no code implementations7 Apr 2021 Mingyang Shang, Dawei Xiang, Zhicheng Wang, Erjin Zhou

V2F-Net consists of two sub-networks: Visible region Detection Network (VDN) and Full body Estimation Network (FEN).

Pedestrian Detection

Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation

1 code implementation CVPR 2021 Zhengxiong Luo, Zhicheng Wang, Yan Huang, Tieniu Tan, Erjin Zhou

However, for bottom-up methods, which need to handle a large variance of human scales and labeling ambiguities, the current practice seems unreasonable.

Pose Estimation

Efficient Human Pose Estimation by Learning Deeply Aggregated Representations

no code implementations13 Dec 2020 Zhengxiong Luo, Zhicheng Wang, Yuanhao Cai, GuanAn Wang, Yan Huang, Liang Wang, Erjin Zhou, Tieniu Tan, Jian Sun

Instead, we focus on exploiting multi-scale information from layers with different receptive-field sizes and then making full of use this information by improving the fusion method.

Pose Estimation

DPGN: Distribution Propagation Graph Network for Few-shot Learning

1 code implementation CVPR 2020 Ling Yang, Liangliang Li, Zilun Zhang, Xinyu Zhou, Erjin Zhou, Yu Liu

To combine the distribution-level relations and instance-level relations for all examples, we construct a dual complete graph network which consists of a point graph and a distribution graph with each node standing for an example.

Few-Shot Learning

High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification

2 code implementations CVPR 2020 Guan'an Wang, Shuo Yang, Huanyu Liu, Zhicheng Wang, Yang Yang, Shuliang Wang, Gang Yu, Erjin Zhou, Jian Sun

When aligning two groups of local features from two images, we view it as a graph matching problem and propose a cross-graph embedded-alignment (CGEA) layer to jointly learn and embed topology information to local features, and straightly predict similarity score.

Graph Matching Person Re-Identification

Learning Delicate Local Representations for Multi-Person Pose Estimation

3 code implementations ECCV 2020 Yuanhao Cai, Zhicheng Wang, Zhengxiong Luo, Binyi Yin, Angang Du, Haoqian Wang, Xiangyu Zhang, Xinyu Zhou, Erjin Zhou, Jian Sun

To tackle this problem, we propose an efficient attention mechanism - Pose Refine Machine (PRM) to make a trade-off between local and global representations in output features and further refine the keypoint locations.

Multi-Person Pose Estimation

GridFace: Face Rectification via Learning Local Homography Transformations

no code implementations ECCV 2018 Erjin Zhou, Zhimin Cao, Jian Sun

In this paper, we propose a method, called GridFace, to reduce facial geometric variations and improve the recognition performance.

Face Recognition Image Generation +1

Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression

no code implementations16 Nov 2015 Zhiao Huang, Erjin Zhou, Zhimin Cao

Facial landmark localization plays an important role in face recognition and analysis applications.

Face Alignment Face Recognition

Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?

no code implementations20 Jan 2015 Erjin Zhou, Zhimin Cao, Qi Yin

In this paper, we report our observations on how big data impacts the recognition performance.

Face Recognition

Cannot find the paper you are looking for? You can Submit a new open access paper.