2 citations · 2 across the 9 of their papers we have counts for
8 papers · 1 filter
MMhops-R1: Multimodal Multi-hop Reasoning
Tao Zhang, Ziqi Zhang, Zongyang Ma +7
The ability to perform multi-modal multi-hop reasoning by iteratively integrating information across various modalities and external knowledge is critical for addressing complex re…
UniPixel: Unified Object Referring and Segmentation for Pixel-Level Visual Reasoning
Ye Liu, Zongyang Ma, Junfu Pu +4
Recent advances in Large Multi-modal Models (LMMs) have demonstrated their remarkable success as general-purpose multi-modal assistants, with particular focuses on holistic image-…
DOGR: Towards Versatile Visual Document Grounding and Referring
Yinan Zhou, Yuxin Chen, Haokun Lin +6
With recent advances in Multimodal Large Language Models (MLLMs), grounding and referring capabilities have gained increasing attention for achieving detailed understanding and fle…
Taming Rectified Flow for Inversion and Editing
Jiangshan Wang, Junfu Pu, Zhongang Qi +6
Rectified-flow-based diffusion transformers like FLUX and OpenSora have demonstrated outstanding performance in the field of image and video generation. Despite their robust genera…
E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding
Ye Liu, Zongyang Ma, Zhongang Qi +3
Recent advances in Video Large Language Models (Video-LLMs) have demonstrated their great potential in general-purpose video understanding. To verify the significance of these mode…
SynopGround: A Large-Scale Dataset for Multi-Paragraph Video Grounding from TV Dramas and Synopses
Chaolei Tan, Zihang Lin, Junfu Pu +7
Video grounding is a fundamental problem in multimodal content understanding, aiming to localize specific natural language queries in an untrimmed video. However, current video gro…