7 papers · 1 filter
OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Model
Qiguang Chen, Chengyu Luan, Jiajun Wu +7
Large vision-language models (LVLMs) have made substantial advances in reasoning tasks at the Olympiad level. Nevertheless, current Olympiad-level multimodal reasoning benchmarks f…
Visual Thoughts: A Unified Perspective of Understanding Multimodal Chain-of-Thought
Zihui Cheng, Qiguang Chen, Xiao Xu +8
Large Vision-Language Models (LVLMs) have achieved significant success in multimodal tasks, with multimodal chain-of-thought (MCoT) further enhancing performance and interpretabili…
ViTCoT: Video-Text Interleaved Chain-of-Thought for Boosting Video Understanding in Large Language Models
Yongheng Zhang, Xu Liu, Ruihan Tao +4
Video understanding plays a vital role in bridging low-level visual signals with high-level cognitive reasoning, and is fundamental to applications such as autonomous driving, embo…
Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs
Xiao Xu, Libo Qin, Wanxiang Che +1
Two-Tower Vision--Language Models (VLMs) have demonstrated strong performance across various downstream VL tasks. While BridgeTower further enhances performance by building bridges…
CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models
Zihui Cheng, Qiguang Chen, Jin Zhang +5
Large Vision-Language Models (LVLMs) have recently demonstrated amazing success in multi-modal tasks, including advancements in Multi-modal Chain-of-Thought (MCoT) reasoning. Despi…
Exploring Multi-Grained Concept Annotations for Multimodal Large Language Models
Xiao Xu, Tianhao Niu, Yuxi Xie +3
Multimodal Large Language Models (MLLMs) excel in vision--language tasks by pre-training solely on coarse-grained concept annotations (e.g., image captions). We hypothesize that in…