1 citations · 5 across the 13 of their papers we have counts for
6 papers · 1 filter
Seeing Together: Multi-Robot Cooperative Egocentric Spatial Reasoning with Multimodal Large Language Models
Kunyu Peng, Zhikun Zhou, Kailun Yang +9
Multimodal Large Language Models (MLLMs) have made substantial progress in egocentric video understanding, but their ability to reason cooperatively from multiple embodied viewpoin…
EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models
Haozhe Shan, Xiancong Ren, Han Dong +10
While large vision-language models (VLMs) are increasingly adopted as the perceptual backbone for embodied agents, their fundamental vision-language grounding (VLG) capability in e…
VideoVista-CulturalLingo: 360 Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension
Xinyu Chen, Yunxin Li, Haoyuan Shi +4
Assessing the video comprehension capabilities of multimodal AI systems can effectively measure their understanding and reasoning abilities. Most video evaluation benchmarks are li…
Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models
Yunxin Li, Zhenyu Liu, Zitao Li +19
Reasoning lies at the heart of intelligence, shaping the ability to make decisions, draw conclusions, and generalize across domains. In artificial intelligence, as systems increasi…
VideoVista: A Versatile Benchmark for Video Understanding and Reasoning
Yunxin Li, Xinyu Chen, Baotian Hu +3
Despite significant breakthroughs in video analysis driven by the rapid development of large multimodal models (LMMs), there remains a lack of a versatile evaluation benchmark to c…
VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
Yunxin Li, Baotian Hu, Haoyuan Shi +3
Large Multimodal Models (LMMs) have achieved impressive success in visual understanding and reasoning, remarkably improving the performance of mathematical reasoning in a visual co…