activity
20192026
most citedBiHRNet: A Binary high-resolution network for Human Pose Estimation

1 citations · 2 across the 14 of their papers we have counts for

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cs.CV2026

Identity-Aware Human-Object Interaction Motion Captioning

Yiming Wang, Yonghao Dang, Huilai Li +2

Existing human-object interaction (HOI) motion captioning methods typically describe what happens while referring to the subject using generic terms such as "a person" or "someone"…

cs.CV2026

EAR: Enhancing Uni-Modal Representations for Weakly Supervised Audio-Visual Video Parsing

Huilai Li, Xiaomeng Di, Ying Xing +3

Weakly supervised Audio-Visual Video Parsing (AVVP) aims to recognize and temporally localize audio, visual, and audio-visual events in videos using only coarse-grained labels. Fac…

cs.CV2025

ResDiT: Evoking the Intrinsic Resolution Scalability in Diffusion Transformers

Yiyang Ma, Feng Zhou, Xuedan Yin +3

Leveraging pre-trained Diffusion Transformers (DiTs) for high-resolution (HR) image synthesis often leads to spatial layout collapse and degraded texture fidelity. Prior work mitig…

cs.CV2025

MaskSem: Semantic-Guided Masking for Learning 3D Hybrid High-Order Motion Representation

Wei Wei, Shaojie Zhang, Yonghao Dang +1

Human action recognition is a crucial task for intelligent robotics, particularly within the context of human-robot collaboration research. In self-supervised skeleton-based action…

cs.CV2025

3DGAA: Realistic and Robust 3D Gaussian-based Adversarial Attack for Autonomous Driving

Yixun Zhang, Lizhi Wang, Junjun Zhao +4

Camera-based perception in connected and autonomous vehicles remains exposed to physical adversarial attacks. Prior attacks often either optimize image-plane textures, weakening cr…

cs.CV2025

L2HCount:Generalizing Crowd Counting from Low to High Crowd Density via Density Simulation

Guoliang Xu, Jianqin Yin, Ren Zhang +3

Since COVID-19, crowd-counting tasks have gained wide applications. While supervised methods are reliable, annotation is more challenging in high-density scenes due to small head s…