1 citations · 1 across the 5 of their papers we have counts for
6 papers · 1 filter
ProBench: Judging Multimodal Foundation Models on Open-ended Multi-domain Expert Tasks
Yan Yang, Dongxu Li, Haoning Wu +4
Solving expert-level multimodal tasks is a key milestone towards general intelligence. As the capabilities of multimodal large language models (MLLMs) continue to improve, evaluati…
DynFocus: Dynamic Cooperative Network Empowers LLMs with Video Understanding
Yudong Han, Qingpei Guo, Liyuan Pan +3
The challenge in LLM-based video understanding lies in preserving visual and semantic information in long videos while maintaining a memory-affordable token count. However, redunda…
Storyboard guided Alignment for Fine-grained Video Action Recognition
Enqi Liu, Liyuan Pan, Yan Yang +4
Fine-grained video action recognition can be conceptualized as a video-text matching problem. Previous approaches often rely on global video semantics to consolidate video embeddin…
EZSR: Event-based Zero-Shot Recognition
Yan Yang, Liyuan Pan, Dongxu Li +1
This paper studies zero-shot object recognition using event camera data. Guided by CLIP, which is pre-trained on RGB images, existing approaches achieve zero-shot object recognitio…
Event Camera Data Dense Pre-training
Yan Yang, Liyuan Pan, Liu Liu
This paper introduces a self-supervised learning framework designed for pre-training neural networks tailored to dense prediction tasks using event camera data. Our approach utiliz…
Event Camera Data Pre-training
Yan Yang, Liyuan Pan, Liu Liu
This paper proposes a pre-trained neural network for handling event camera data. Our model is a self-supervised learning framework, and uses paired event camera data and natural RG…