most citedNVILA: Efficient Frontier Visual Language Models

1 citations · 1 across the 1 of their papers we have counts for

collaborators

12 papers

cs.CV20261 cited

NVILA: Efficient Frontier Visual Language Models

Zhijian Liu, Ligeng Zhu, Baifeng Shi +24

Visual language models (VLMs) have made significant advances in accuracy in recent years. However, their efficiency has received much less attention. This paper introduces NVILA, a…

cs.CV2026

Spatial Orthogonal Refinement for Robust RGB-Event Visual Object Tracking

Dexing Huang, Shiao Wang, Fan Zhang +1

Robust visual object tracking (VOT) remains challenging in high-speed motion scenarios, where conventional RGB sensors suffer from severe motion blur and performance degradation. E…

cs.CV2026

VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding

Shihao Wang, Guo Chen, De-an Huang +6

While Video Large Language Models (Video-LLMs) have shown significant potential in multimodal understanding and reasoning tasks, how to efficiently select the most informative fram…

cs.CV2025

EvRainDrop: HyperGraph-guided Completion for Effective Frame and Event Stream Aggregation

Futian Wang, Fan Zhang, Xiao Wang +3

Event cameras produce asynchronous event streams that are spatially sparse yet temporally dense. Mainstream event representation learning algorithms typically use event frames, vox…

cs.CV2025

STORM: Token-Efficient Long Video Understanding for Multimodal LLMs

Jindong Jiang, Xiuyu Li, Zhijian Liu +13

Recent advances in video-based multimodal large language models (Video-LLMs) have significantly improved video understanding by processing videos as sequences of image frames. Howe…

cs.CV2025

Argus: Vision-Centric Reasoning with Grounded Chain-of-Thought

Yunze Man, De-An Huang, Guilin Liu +6

Recent advances in multimodal large language models (MLLMs) have demonstrated remarkable capabilities in vision-language tasks, yet they often struggle with vision-centric scenario…