Anomalous video understanding aims to identify abnormal events in videos and interpret their semantic meanings beyond simple anomaly detection. Recent video large language models (Video-LLMs) have demonstrated promising zero-shot capabilities for this task, yet their performance remains limited due to insufficient adap...
Hui-Ning Li, Yu-Xiang Duan, Ji-Yang Tan et al.· 0 citations
Video anomaly understanding (VAU) focuses on comprehensively interpreting abnormal events in videos, requiring models to identify anomalous occurrences, discover their supporting evidence, and explain the underlying causes beyond simple anomaly detection. Existing VAU methods often rely on specialized training or limit...
Yu-Xiang Duan, Hui-Ning Li, Aonian Li et al.· 0 citations
By unifying four hallucination dimensions with paired question design, KnowHal addresses an important gap in existing evaluation frameworks and enables a more comprehensive assessment of hallucinations in MLLMs.
Ruihan Li, Ji-Yang Tan, Kai-Lin Jiang et al.· 1 citation
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