Showing cs.CVShow all
2 papers · 1 filter
cs.CV2026
AgenticVAU: Multi-Agent Explore-Verify Reasoning for Video Anomaly Understanding
Yuxiang Duan, Huining Li, Ao Li +6
Video anomaly understanding (VAU) focuses on comprehensively interpreting abnormal events in videos, requiring models to identify anomalous occurrences, discover their supporting e…
cs.CV2024
SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets
Shenghua Wan, Ziyuan Chen, Le Gan +2
Model-based offline reinforcement Learning (RL) is a promising approach that leverages existing data effectively in many real-world applications, especially those involving high-di…