7 papers · 1 filter
VisualClaw: A Real-Time, Personalized Agent for the Physical World
Haoqin Tu, Jianwen Chen, Zijun Wang +14
Vision language models are serving as general-purpose interfaces for complex multimodal tasks. However, deployment still faces three gaps: VLMs typically incur high latency and cos…
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…
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…
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…
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…