2 citations · 2 across the 9 of their papers we have counts for
10 papers
Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning
Yinan Zhou, Haokun Lin, Yichen Wu +7
Large multimodal models have achieved strong reasoning on complex visual tasks, but their inference efficiency is often restricted by long chains of thought. A promising solution i…
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…
mRAG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA
Tao Zhang, Ziqi Zhang, Zongyang Ma +10
Advanced Multimodal Large Language Models (MLLMs) struggle with recent Knowledge-based Visual Question Answering (VQA) tasks, such as INFOSEEK and Encyclopedic-VQA, due to their li…
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…
How to Make Cross Encoder a Good Teacher for Efficient Image-Text Retrieval?
Yuxin Chen, Zongyang Ma, Ziqi Zhang +7
Dominant dual-encoder models enable efficient image-text retrieval but suffer from limited accuracy while the cross-encoder models offer higher accuracy at the expense of efficienc…
EA-VTR: Event-Aware Video-Text Retrieval
Zongyang Ma, Ziqi Zhang, Yuxin Chen +8
Understanding the content of events occurring in the video and their inherent temporal logic is crucial for video-text retrieval. However, web-crawled pre-training datasets often l…