most citedMME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs

4 citations · 4 across the 2 of their papers we have counts for

collaborators

5 papers

cs.RO2025

VITA-E: Natural Embodied Interaction with Concurrent Seeing, Hearing, Speaking, and Acting

Xiaoyu Liu, Chaoyou Fu, Chi Yan +15

Current Vision-Language-Action (VLA) models are often constrained by a rigid, static interaction paradigm, which lacks the ability to see, hear, speak, and act concurrently as well…

cs.CV2025

VITA-VLA: Efficiently Teaching Vision-Language Models to Act via Action Expert Distillation

Shaoqi Dong, Chaoyou Fu, Haihan Gao +12

Vision-Language Action (VLA) models significantly advance robotic manipulation by leveraging the strong perception capabilities of pretrained vision-language models (VLMs). By inte…

cs.CV2025

VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction

Chaoyou Fu, Haojia Lin, Xiong Wang +13

Recent Multimodal Large Language Models (MLLMs) have typically focused on integrating visual and textual modalities, with less emphasis placed on the role of speech in enhancing in…

cs.CV20244 cited

MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs

Chaoyou Fu, Yi-Fan Zhang, Shukang Yin +9

As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. Build…

cs.CV2024

Sparrow: Data-Efficient Video-LLM with Text-to-Image Augmentation

Shukang Yin, Chaoyou Fu, Sirui Zhao +7

Recent years have seen the success of Multimodal Large Language Models (MLLMs) in the domain of vision understanding. The success of these models can largely be attributed to the d…