activity
20242026
most citedFlatten: Video Action Recognition is an Image Classification task

3 citations · 3 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

MultiRef-Compass: Towards Comprehensive Evaluation of Multi-Reference-to-Audio-Video Generation

Xiaohan Zhang, Yuqing Wen, Junlin Chen +9

Multi-reference-to-audio-video (MR2AV) generation aims to generate coherent audio-video content conditioned on multiple references and textual instructions. Existing benchmarks mai…

cs.CV2026

Shape of Thought: Progressive Object Assembly via Visual Chain-of-Thought

Yu Huo, Siyu Zhang, Kun Zeng +7

Multimodal models for text-to-image generation have achieved strong visual fidelity, yet they remain brittle under compositional structural constraints, notably generative numeracy…

cs.CV2025

VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results

Yixiao Li, Xin Li, Chris Wei Zhou +28

This paper presents the ISRGC-Q Challenge, built upon the Image Super-Resolution Generated Content Quality Assessment (ISRGen-QA) dataset, and organized as part of the Visual Quali…

cs.CV2025

DreamLight: Towards Harmonious and Consistent Image Relighting

Yong Liu, Wenpeng Xiao, Qianqian Wang +5

We introduce a model named DreamLight for universal image relighting in this work, which can seamlessly composite subjects into a new background while maintaining aesthetic uniform…

cs.CV2025

Tuning-Free Long Video Generation via Global-Local Collaborative Diffusion

Yongjia Ma, Junlin Chen, Donglin Di +6

Creating high-fidelity, coherent long videos is a sought-after aspiration. While recent video diffusion models have shown promising potential, they still grapple with spatiotempora…

cs.CV2024★ 3 cited

Flatten: Video Action Recognition is an Image Classification task

Junlin Chen, Chengcheng Xu, Yangfan Xu +3

In recent years, video action recognition, as a fundamental task in the field of video understanding, has been deeply explored by numerous researchers.Most traditional video action…