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20242026
most citedTaming Rectified Flow for Inversion and Editing

2 citations · 2 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.CV2024

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…

cs.AI2024

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…

cs.CV2024★ 2 cited

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…