148 citations · 468 across the 24 of their papers we have counts for
26 papers
UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling
Peiming Li, Ziyi Wang, Yulin Yuan +4
Point cloud videos capture dynamic 3D motion while reducing the effects of lighting and viewpoint variations, making them highly effective for recognizing subtle and continuous hum…
TCPFormer: Learning Temporal Correlation with Implicit Pose Proxy for 3D Human Pose Estimation
Jiajie Liu, Mengyuan Liu, Hong Liu +1
Recent multi-frame lifting methods have dominated the 3D human pose estimation. However, previous methods ignore the intricate dependence within the 2D pose sequence and learn sing…
NanoHTNet: Nano Human Topology Network for Efficient 3D Human Pose Estimation
Jialun Cai, Mengyuan Liu, Hong Liu +2
The widespread application of 3D human pose estimation (HPE) is limited by resource-constrained edge devices, requiring more efficient models. A key approach to enhancing efficienc…
ClickDiff: Click to Induce Semantic Contact Map for Controllable Grasp Generation with Diffusion Models
Peiming Li, Ziyi Wang, Mengyuan Liu +2
Grasp generation aims to create complex hand-object interactions with a specified object. While traditional approaches for hand generation have primarily focused on visibility and…
Identity-aware Dual-constraint Network for Cloth-Changing Person Re-identification
Peini Guo, Mengyuan Liu, Hong Liu +3
Cloth-Changing Person Re-Identification (CC-ReID) aims to accurately identify the target person in more realistic surveillance scenarios, where pedestrians usually change their clo…
Learning Mutual Excitation for Hand-to-Hand and Human-to-Human Interaction Recognition
Mengyuan Liu, Chen Chen, Songtao Wu +2
Recognizing interactive actions, including hand-to-hand interaction and human-to-human interaction, has attracted increasing attention for various applications in the field of vide…
Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation
Wenhao Li, Mengyuan Liu, Hong Liu +3
Transformers have been successfully applied in the field of video-based 3D human pose estimation. However, the high computational costs of these video pose transformers (VPTs) make…
Double-chain Constraints for 3D Human Pose Estimation in Images and Videos
Hongbo Kang, Yong Wang, Mengyuan Liu +3
Reconstructing 3D poses from 2D poses lacking depth information is particularly challenging due to the complexity and diversity of human motion. The key is to effectively model the…
Learning Snippet-to-Motion Progression for Skeleton-based Human Motion Prediction
Xinshun Wang, Qiongjie Cui, Chen Chen +2
Existing Graph Convolutional Networks to achieve human motion prediction largely adopt a one-step scheme, which output the prediction straight from history input, failing to exploi…
Integrating Human Parsing and Pose Network for Human Action Recognition
Runwei Ding, Yuhang Wen, Jinfu Liu +3
Human skeletons and RGB sequences are both widely-adopted input modalities for human action recognition. However, skeletons lack appearance features and color data suffer large amo…
Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action Recognition
Yuhang Wen, Zixuan Tang, Yunsheng Pang +2
Recognizing interactive action plays an important role in human-robot interaction and collaboration. Previous methods use late fusion and co-attention mechanism to capture interact…
Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
Mengyuan Liu, Hong Liu, Tianyu Guo
Considering the instance-level discriminative ability, contrastive learning methods, including MoCo and SimCLR, have been adapted from the original image representation learning ta…
A Gated Cross-domain Collaborative Network for Underwater Object Detection
Linhui Dai, Hong Liu, Pinhao Song +1
Underwater object detection (UOD) plays a significant role in aquaculture and marine environmental protection. Considering the challenges posed by low contrast and low-light condit…
Explore In-Context Learning for 3D Point Cloud Understanding
Zhongbin Fang, Xiangtai Li, Xia Li +3
With the rise of large-scale models trained on broad data, in-context learning has become a new learning paradigm that has demonstrated significant potential in natural language pr…
Part Aware Contrastive Learning for Self-Supervised Action Recognition
Yilei Hua, Wenhan Wu, Ce Zheng +4
In recent years, remarkable results have been achieved in self-supervised action recognition using skeleton sequences with contrastive learning. It has been observed that the seman…
Graph-Guided MLP-Mixer for Skeleton-Based Human Motion Prediction
Xinshun Wang, Qiongjie Cui, Chen Chen +2
In recent years, Graph Convolutional Networks (GCNs) have been widely used in human motion prediction, but their performance remains unsatisfactory. Recently, MLP-Mixer, initially…
PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation
Qitao Zhao, Ce Zheng, Mengyuan Liu +2
Recently, transformer-based methods have gained significant success in sequential 2D-to-3D lifting human pose estimation. As a pioneering work, PoseFormer captures spatial relation…
Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Models
Mengyi Zhao, Mengyuan Liu, Bin Ren +2
Diffusion-based generative models have recently emerged as powerful solutions for high-quality synthesis in multiple domains. Leveraging the bidirectional Markov chains, diffusion…
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition
Tianyu Guo, Hong Liu, Zhan Chen +3
In recent years, self-supervised representation learning for skeleton-based action recognition has been developed with the advance of contrastive learning methods. The existing con…
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…
Consistency-based Active Learning for Object Detection
Weiping Yu, Sijie Zhu, Taojiannan Yang +1
Active learning aims to improve the performance of task model by selecting the most informative samples with a limited budget. Unlike most recent works that focused on applying act…
A Survey on 3D Skeleton-Based Action Recognition Using Learning Method
Bin Ren, Mengyuan Liu, Runwei Ding +1
3D skeleton-based action recognition (3D SAR) has gained significant attention within the computer vision community, owing to the inherent advantages offered by skeleton data. As a…
RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices
Chen Chen, Mengyuan Liu, Xiandong Meng +2
Previous state-of-the-art real-time object detectors have been reported on GPUs which are extremely expensive for processing massive data and in resource-restricted scenarios. Ther…
Self-Refining Deep Symmetry Enhanced Network for Rain Removal
Hong Liu, Hanrong Ye, Xia Li +3
Rain removal aims to remove the rain streaks on rain images. The state-of-the-art methods are mostly based on Convolutional Neural Network~(CNN). However, as CNN is not equivariant…