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PromptVAD: Abnormal Prompt via Vision-Language Model.

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    This study introduces PromptVAD, a novel method for weakly supervised video anomaly detection (WSVAD). PromptVAD effectively utilizes category names to reduce the semantic gap, achieving state-of-the-art performance on benchmark datasets.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Weakly supervised video anomaly detection (WSVAD) models training videos using video-level annotations.
    • Abnormal event category names offer valuable human-abstracted knowledge for identifying anomalies.

    Purpose of the Study:

    • To leverage implicit knowledge in category names for improved WSVAD.
    • To reduce the semantic gap between visual data and anomaly categories in WSVAD.

    Main Methods:

    • Introduced a learnable abnormal prompt, incorporating domain, category, and definition prompts, based on visual-language pretraining.
    • Proposed PromptVAD, a fine-grained WSVAD method utilizing the learnable abnormal prompt.
    • Implemented a coarse-grained two-class prompt module for joint coarse- and fine-grained VAD learning.

    Main Results:

    • PromptVAD achieved state-of-the-art performance on ShanghaiTech, UCF-Crime, and XD-Violence datasets.
    • Demonstrated superior performance with an 88.62% AUC on the UCF-Crime dataset.
    • Effectively reduced the semantic gap between visual representations and anomaly categories.

    Conclusions:

    • The proposed learnable abnormal prompt effectively utilizes category name knowledge for WSVAD.
    • PromptVAD offers a robust framework for both coarse- and fine-grained video anomaly detection.
    • The method shows significant advancements in weakly supervised video anomaly detection tasks.