36 citations · 100 across the 15 of their papers we have counts for
15 papers
Domain Generalization via Balancing Training Difficulty and Model Capability
Xueying Jiang, Jiaxing Huang, Sheng Jin +1
Domain generalization (DG) aims to learn domain-generalizable models from one or multiple source domains that can perform well in unseen target domains. Despite its recent progress…
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…
Prompt Ensemble Self-training for Open-Vocabulary Domain Adaptation
Jiaxing Huang, Jingyi Zhang, Han Qiu +2
Traditional domain adaptation assumes the same vocabulary across source and target domains, which often struggles with limited transfer flexibility and efficiency while handling ta…
Vision-Language Models for Vision Tasks: A Survey
Jingyi Zhang, Jiaxing Huang, Sheng Jin +1
Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks (DNNs) training, and they usually train a DNN for each single visual recognition task, l…
Aligning Bag of Regions for Open-Vocabulary Object Detection
Size Wu, Wenwei Zhang, Sheng Jin +2
Pre-trained vision-language models (VLMs) learn to align vision and language representations on large-scale datasets, where each image-text pair usually contains a bag of semantic…
PoseTrans: A Simple Yet Effective Pose Transformation Augmentation for Human Pose Estimation
Wentao Jiang, Sheng Jin, Wentao Liu +3
Human pose estimation aims to accurately estimate a wide variety of human poses. However, existing datasets often follow a long-tailed distribution that unusual poses only occupy a…
3D Interacting Hand Pose Estimation by Hand De-occlusion and Removal
Hao Meng, Sheng Jin, Wentao Liu +4
Estimating 3D interacting hand pose from a single RGB image is essential for understanding human actions. Unlike most previous works that directly predict the 3D poses of two inter…
Pose for Everything: Towards Category-Agnostic Pose Estimation
Lumin Xu, Sheng Jin, Wang Zeng +5
Existing works on 2D pose estimation mainly focus on a certain category, e.g. human, animal, and vehicle. However, there are lots of application scenarios that require detecting th…
Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer
Wang Zeng, Sheng Jin, Wentao Liu +4
Vision transformers have achieved great successes in many computer vision tasks. Most methods generate vision tokens by splitting an image into a regular and fixed grid and treatin…
Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint Localization
Can Wang, Sheng Jin, Yingda Guan +4
Localizing keypoints of an object is a basic visual problem. However, supervised learning of a keypoint localization network often requires a large amount of data, which is expensi…
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…
Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation
Sheng Jin, Wentao Liu, Enze Xie +4
Multi-person pose estimation is challenging because it localizes body keypoints for multiple persons simultaneously. Previous methods can be divided into two streams, i.e. top-down…
Whole-Body Human Pose Estimation in the Wild
Sheng Jin, Lumin Xu, Jin Xu +5
This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet. As e…
RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning
Nan Jiang, Sheng Jin, Zhiyao Duan +1
This paper presents a deep reinforcement learning algorithm for online accompaniment generation, with potential for real-time interactive human-machine duet improvisation. Differen…
Multi-person Articulated Tracking with Spatial and Temporal Embeddings
Sheng Jin, Wentao Liu, Wanli Ouyang +1
We propose a unified framework for multi-person pose estimation and tracking. Our framework consists of two main components,~\ie~SpatialNet and TemporalNet. The SpatialNet accompli…