29 citations · 56 across the 28 of their papers we have counts for
16 papers · 1 filter
STORM: Internalized Modeling for Spatial-Temporal Reasoning in Video-Language Models
Yiming Liang, Yixiao Chen, Yiyang Zhou +8
Many video reasoning tasks require tracking motion, temporal order, and evolving visual states across frames. Existing methods built on large vision-language models (LVLMs) often a…
SimpleOCR: Rendering Visualized Questions to Teach MLLMs to Read
Yibo Peng, Peng Xia, Ding Zhong +6
Despite the rapid advancements in Multimodal Large Language Models (MLLMs), a critical question regarding their visual grounding mechanism remains unanswered: do these models genui…
Knowing the Answer Isn't Enough: Fixing Reasoning Path Failures in LVLMs
Chaoyang Wang, Yangfan He, Yiyang Zhou +6
We reveal a critical yet underexplored flaw in Large Vision-Language Models (LVLMs): even when these models know the correct answer, they frequently arrive there through incorrect…
Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning
Jiaqi Liu, Kaiwen Xiong, Peng Xia +6
Vision-language agents have achieved remarkable progress in a variety of multimodal reasoning tasks; however, their learning remains constrained by the limitations of human-annotat…
When Visualizing is the First Step to Reasoning: MIRA, a Benchmark for Visual Chain-of-Thought
Yiyang Zhou, Haoqin Tu, Zijun Wang +11
We propose MIRA, a new benchmark designed to evaluate models in scenarios where generating intermediate visual images is essential for successful reasoning. Unlike traditional CoT…
Improving Alignment in LVLMs with Debiased Self-Judgment
Sihan Yang, Chenhang Cui, Zihao Zhao +4
The rapid advancements in Large Language Models (LLMs) and Large Visual-Language Models (LVLMs) have opened up new opportunities for integrating visual and linguistic modalities. H…
IKOD: Mitigating Visual Attention Degradation in Large Vision-Language Models
Jiabing Yang, Chenhang Cui, Yiyang Zhou +6
Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated significant progress across multiple domains. However, these models still face the inherent challenge…
GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them?
Yiyang Zhou, Linjie Li, Shi Qiu +10
Existing video benchmarks often resemble image-based benchmarks, with question types like "What actions does the person perform throughout the video?" or "What color is the woman's…
ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
Yiyang Zhou, Yangfan He, Yaofeng Su +5
Video understanding is fundamental to tasks such as action recognition, video reasoning, and robotic control. Early video understanding methods based on large vision-language model…
MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation
Haibo Tong, Zhaoyang Wang, Zhaorun Chen +11
Recent advancements in video generation have significantly improved the ability to synthesize videos from text instructions. However, existing models still struggle with key challe…
VHELM: A Holistic Evaluation of Vision Language Models
Tony Lee, Haoqin Tu, Chi Heem Wong +8
Current benchmarks for assessing vision-language models (VLMs) often focus on their perception or problem-solving capabilities and neglect other critical aspects such as fairness,…
Fine-Grained Verifiers: Preference Modeling as Next-token Prediction in Vision-Language Alignment
Chenhang Cui, An Zhang, Yiyang Zhou +4
The recent advancements in large language models (LLMs) and pre-trained vision models have accelerated the development of vision-language large models (VLLMs), enhancing the intera…
MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models
Peng Xia, Siwei Han, Shi Qiu +9
Interleaved multimodal comprehension and generation, enabling models to produce and interpret both images and text in arbitrary sequences, have become a pivotal area in multimodal…
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
Zhaorun Chen, Yichao Du, Zichen Wen +16
While text-to-image models like DALLE-3 and Stable Diffusion are rapidly proliferating, they often encounter challenges such as hallucination, bias, and the production of unsafe, l…
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
Xiyao Wang, Jiuhai Chen, Zhaoyang Wang +8
Large vision-language models (LVLMs) have achieved impressive results in visual question-answering and reasoning tasks through vision instruction tuning on specific datasets. Howev…
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs
Haoqin Tu, Chenhang Cui, Zijun Wang +6
This work focuses on the potential of Vision LLMs (VLLMs) in visual reasoning. Different from prior studies, we shift our focus from evaluating standard performance to introducing…