5 papers · 1 filter
ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding
Chia-Hui Chen, Shih-Ying Yeh, Fu-En Yang +2
In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely on offline inference with g…
FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding
Gueter Josmy Faure, Min-Hung Chen, Jia-Fong Yeh +2
Vision-Language Models (VLMs) have demonstrated remarkable capabilities in general video understanding, yet they often struggle with the fine-grained comprehension crucial for real…
VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models
Ying Cheng, Yu-Ho Lin, Min-Hung Chen +2
Video anomaly understanding (VAU) aims to provide detailed interpretation and semantic comprehension of anomalous events within videos, addressing limitations of traditional method…
HERMES: temporal-coHERent long-forM understanding with Episodes and Semantics
Gueter Josmy Faure, Jia-Fong Yeh, Min-Hung Chen +3
Long-form video understanding presents unique challenges that extend beyond traditional short-video analysis approaches, particularly in capturing long-range dependencies, processi…
Spatio-Temporal Context Prompting for Zero-Shot Action Detection
Wei-Jhe Huang, Min-Hung Chen, Shang-Hong Lai
Spatio-temporal action detection encompasses the tasks of localizing and classifying individual actions within a video. Recent works aim to enhance this process by incorporating in…