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20232025
most citedWoodpecker: Hallucination Correction for Multimodal Large Language Models

93 citations · 124 across the 13 of their papers we have counts for

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cs.CV2025

Zooming from Context to Cue: Hierarchical Preference Optimization for Multi-Image MLLMs

Xudong Li, Mengdan Zhang, Peixian Chen +8

Multi-modal Large Language Models (MLLMs) excel at single-image tasks but struggle with multi-image understanding due to cross-modal misalignment, leading to hallucinations (contex…

cs.CV2025

Long-VITA: Scaling Large Multi-modal Models to 1 Million Tokens with Leading Short-Context Accuracy

Yunhang Shen, Chaoyou Fu, Shaoqi Dong +14

We introduce Long-VITA, a simple yet effective large multi-modal model for long-context visual-language understanding tasks. It is adept at concurrently processing and analyzing mo…

cs.CV2025

VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction

Chaoyou Fu, Haojia Lin, Xiong Wang +13

Recent Multimodal Large Language Models (MLLMs) have typically focused on integrating visual and textual modalities, with less emphasis placed on the role of speech in enhancing in…

cs.CV2024

FlashSloth: Lightning Multimodal Large Language Models via Embedded Visual Compression

Bo Tong, Bokai Lai, Yiyi Zhou +5

Despite a big leap forward in capability, multimodal large language models (MLLMs) tend to behave like a sloth in practical use, i.e., slow response and large latency. Recent effor…

cs.CV2024★ 11 cited

Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Chaoyou Fu, Yuhan Dai, Yongdong Luo +18

In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs) have emerged as a focal point in recent advancements. However, the predominant focus rem…

cs.CV2024

Feature Denoising Diffusion Model for Blind Image Quality Assessment

Xudong Li, Jingyuan Zheng, Runze Hu +8

Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks. Currently, deep learning BIQA methods typically de…

cs.CV2023★ 13 cited

A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise

Chaoyou Fu, Renrui Zhang, Zihan Wang +15

The surge of interest towards Multi-modal Large Language Models (MLLMs), e.g., GPT-4V(ision) from OpenAI, has marked a significant trend in both academia and industry. They endow L…

cs.CV2023★ 2 cited

Aligning and Prompting Everything All at Once for Universal Visual Perception

Yunhang Shen, Chaoyou Fu, Peixian Chen +6

Vision foundation models have been explored recently to build general-purpose vision systems. However, predominant paradigms, driven by casting instance-level tasks as an object-wo…

cs.CV2023★ 1 cited

CAPro: Webly Supervised Learning with Cross-Modality Aligned Prototypes

Yulei Qin, Xingyu Chen, Yunhang Shen +5

Webly supervised learning has attracted increasing attention for its effectiveness in exploring publicly accessible data at scale without manual annotation. However, most existing…

cs.CV2023★ 93 cited

Woodpecker: Hallucination Correction for Multimodal Large Language Models

Shukang Yin, Chaoyou Fu, Sirui Zhao +7

Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with t…