1 citations · 2 across the 5 of their papers we have counts for
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Video-IFBench: Evaluating Instruction Following of Multimodal LLMs in Video Understanding Scenarios
Hongbo Liu, Peixian Chen, Sihan Liu +12
Multimodal Large Language Models (MLLMs) have shown strong performance in video understanding. However, their ability to follow instructions in this domain remains under-explored.…
OmniView-Space: Reinforcing Spatial Reasoning via Multi-Perspective Spatial Mapping
Xudong Li, Mengdan Zhang, Peixian Chen +7
Spatial intelligence remains a persistent challenge for Multimodal Large Language Models (MLLMs), as it requires coherent spatial scene representations beyond basic object recognit…
VEGA: Learning Interleaved Image-Text Comprehension in Vision-Language Large Models
Chenyu Zhou, Mengdan Zhang, Peixian Chen +5
The swift progress of Multi-modal Large Models (MLLMs) has showcased their impressive ability to tackle tasks blending vision and language. Yet, most current models and benchmarks…
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…
Cantor: Inspiring Multimodal Chain-of-Thought of MLLM
Timin Gao, Peixian Chen, Mengdan Zhang +8
With the advent of large language models(LLMs) enhanced by the chain-of-thought(CoT) methodology, visual reasoning problem is usually decomposed into manageable sub-tasks and tackl…
Towards Artwork Explanation in Large-scale Vision Language Models
Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2
Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…