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  1. Home
  3. Benchmarking Compact Vlms For Clip-level Surveillance Anomaly Detection Under Weak Supervision.
  1. Home
  3. Benchmarking Compact Vlms For Clip-level Surveillance Anomaly Detection Under Weak Supervision.

Related Experiment Video

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
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Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

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Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervision.

Kirill Borodin1, Kirill Kondrashov1, Nikita Vasiliev1

  • 1Faculty of Information Technology, Moscow Technical University of Communication and Informatics, Moscow 111024, Russia.

Journal of Imaging
|November 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Compact vision-language models (VLMs) offer a practical solution for CCTV anomaly detection, balancing accuracy and speed. Parameter-efficient fine-tuning enhances their reliability and consistency for real-time safety monitoring.

Keywords:
CCTV video analyticsclip-level anomaly detectioncompact vision–language modelsparameter-efficient fine-tuningprompt robustness and design

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • CCTV safety monitoring requires anomaly detection with high accuracy and low latency.
  • Weak supervision presents challenges for traditional anomaly detection methods.
  • Compact vision-language models (VLMs) are explored for their potential in this domain.

Purpose of the Study:

  • To investigate the effectiveness of parameter-efficiently adapted compact VLMs for CCTV anomaly detection.
  • To establish a unified evaluation protocol for comparing different VLM approaches and baselines.
  • To assess the trade-off between detection accuracy and per-clip latency.

Main Methods:

  • A unified evaluation protocol was developed, standardizing preprocessing, prompting, dataset splits, metrics, and runtime settings.
  • Compact VLMs were adapted using parameter-efficient fine-tuning.
  • Performance was compared against training-free VLM pipelines and weakly supervised baselines.
  • Metrics included accuracy, precision, recall, F1, ROC-AUC, and average per-clip latency.

    • Main Results:

    • Parameter-efficiently adapted compact VLMs achieved performance comparable to or exceeding established methods.
    • These models maintained competitive per-clip latency, crucial for real-time monitoring.
    • Adaptation reduced prompt sensitivity, leading to more consistent behavior.
    • A favorable accuracy-efficiency trade-off was demonstrated.

    Conclusions:

    • Parameter-efficient fine-tuning enables compact VLMs to function as reliable clip-level anomaly detectors.
    • Compact VLMs offer a practical and efficient solution for CCTV safety monitoring under weak supervision.
    • The proposed evaluation protocol ensures transparency and consistency in assessing anomaly detection methods.