UAL-Bench: The First Comprehensive Unusual Activity Localization Benchmark
Hasnat Md Abdullah, Tian Liu, Kangda Wei, Shu Kong, Ruihong Huang

TL;DR
This paper introduces UAL-Bench, a comprehensive benchmark for localizing unusual activities in videos, evaluating foundation models' capabilities and proposing new metrics and methods to improve localization accuracy.
Contribution
It presents the first extensive benchmark for unusual activity localization, evaluates multiple models including a novel VLM-LLM approach, and introduces a new evaluation metric R@1, TD <= p.
Findings
VLM-LLM outperforms Vid-LLMs in localizing short-span events.
The new metric R@1, TD <= p better captures localization accuracy.
Challenges remain in long-duration video localization, especially in autism diagnosis.
Abstract
Localizing unusual activities, such as human errors or surveillance incidents, in videos holds practical significance. However, current video understanding models struggle with localizing these unusual events likely because of their insufficient representation in models' pretraining datasets. To explore foundation models' capability in localizing unusual activity, we introduce UAL-Bench, a comprehensive benchmark for unusual activity localization, featuring three video datasets: UAG-OOPS, UAG-SSBD, UAG-FunQA, and an instruction-tune dataset: OOPS-UAG-Instruct, to improve model capabilities. UAL-Bench evaluates three approaches: Video-Language Models (Vid-LLMs), instruction-tuned Vid-LLMs, and a novel integration of Vision-Language Models and Large Language Models (VLM-LLM). Our results show the VLM-LLM approach excels in localizing short-span unusual events and predicting their onset…
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Taxonomy
TopicsMedical Imaging Techniques and Applications · Advanced Radiotherapy Techniques
