activity
20142023
most citedResidual Attention Network for Image Classification

313 citations · 1k across the 44 of their papers we have counts for

collaborators

44 papers

cs.CV2023

GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition

Ruijie Yao, Sheng Jin, Lumin Xu +5

Multi-Label Image Recognition (MLIR) is a challenging task that aims to predict multiple object labels in a single image while modeling the complex relationships between labels and…

cs.CV2022★ 43 cited

ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild

Lumin Xu, Sheng Jin, Wentao Liu +4

This paper investigates the task of 2D whole-body human pose estimation, which aims to localize dense landmarks on the entire human body including body, feet, face, and hands. We p…

cs.CV2021★ 1 cited

Exploring Versatile Prior for Human Motion via Motion Frequency Guidance

Jiachen Xu, Min Wang, Jingyu Gong +4

Prior plays an important role in providing the plausible constraint on human motion. Previous works design motion priors following a variety of paradigms under different circumstan…

cs.CV2021★ 6 cited

Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary

Siyuan Zhou, Li Niu, Jianlou Si +2

Weakly-supervised semantic segmentation (WSSS) with image-level labels has been widely studied to relieve the annotation burden of the traditional segmentation task. In this paper,…

cs.CV2021★ 6 cited

Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images

Size Wu, Sheng Jin, Wentao Liu +4

This paper studies the task of estimating the 3D human poses of multiple persons from multiple calibrated camera views. Following the top-down paradigm, we decompose the task into…

cs.CV2021★ 41 cited

ReSSL: Relational Self-Supervised Learning with Weak Augmentation

Mingkai Zheng, Shan You, Fei Wang +4

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most o…