Chen Zhong

CogVis redefines change detection by decoupling temporal perception from semantic categorization, achieving unprecedented efficiency and accuracy across multiple benchmarks.

DIVE achieves an impressive 88.9% reduction in visual tokens without sacrificing performance, redefining efficiency in vision-language models.

NebulaExp reveals that a meticulously curated dataset and innovative reinforcement learning strategies can boost LLM performance significantly, achieving up to 4.43 points improvement in instruction-following tasks with minimal data.