Explainable Anomaly Detection in Images and Videos: A Comprehensive Survey

Core Concepts

Anomaly detection methods for images and videos are crucial, but the lack of explainability hinders their adoption. This survey focuses on providing insights into explainable anomaly detection techniques.

Abstract

Anomaly detection in visual data is essential for various applications, but the interpretability of black-box models remains a challenge. This survey delves into explainable anomaly detection methods for both images and videos. Techniques like attention-based, input-perturbation-based, generative-model-based, and foundation-model-based approaches are explored. Attention-based methods highlight important regions in images for anomaly detection. Input-perturbation-based methods perturb input data to detect anomalies based on score changes. Generative-model-based methods use models like GANs and VAEs to reconstruct normal patterns and identify anomalies. Foundation-model-based methods leverage large language models for enhanced anomaly detection and explanations. The survey also discusses the importance of explainability in anomaly detection tasks across different modalities.

Stats

Despite rapid advancements in visual anomaly detection techniques, interpretations of these black-box models remain scarce.
Deep learning-based methods like Autoencoders and VAEs are extensively employed in 2D anomaly detection.
Explainability is crucial for AI systems, especially in safety-critical domains like biomedical image analysis.
Various explainable methods have been proposed for 2D anomaly detection tasks, each with unique approaches.
Intrinsically interpretable video anomaly detection methods show strong empirical performance on benchmark datasets.

Quotes

Key Insights Distilled From

Explainable Anomaly Detection in Images and Videos

by Yizhou Wang,... at arxiv.org 03-14-2024

https://arxiv.org/pdf/2302.06670.pdf

Deeper Inquiries

How can the lack of interpretability hinder the adoption of anomaly detection systems in real-world scenarios

The lack of interpretability in anomaly detection systems can hinder their adoption in real-world scenarios for several reasons. Firstly, without clear explanations of why anomalies are detected, users may not trust the system's decisions. This lack of trust can lead to reluctance in relying on the system for critical tasks or decision-making processes. In fields where human safety is a concern, such as medical imaging or security surveillance, this lack of trust can be particularly problematic.
Secondly, without interpretability, it becomes challenging to troubleshoot and understand false positives or false negatives generated by the anomaly detection system. When anomalies are flagged incorrectly or missed altogether, it is crucial to have insights into why these errors occurred to improve the system's performance over time. Without this understanding, optimizing and refining the anomaly detection model becomes a daunting task.
Furthermore, in regulated industries where compliance with standards and regulations is mandatory, explainable anomaly detection systems are essential. Regulatory bodies often require transparent decision-making processes that can be audited and understood by stakeholders. An opaque anomaly detection system may fail to meet these regulatory requirements, leading to potential legal issues or non-compliance penalties.
Overall, lacking interpretability in anomaly detection systems not only undermines user trust but also hampers performance improvement efforts and regulatory compliance.

What challenges might arise when applying image-specific explainable methods to video anomaly detection

Applying image-specific explainable methods to video anomaly detection poses several challenges due to the inherent differences between images and videos. One primary challenge is related to temporal information: videos contain sequential frames that capture motion dynamics over time which cannot be adequately captured by static image-based methods designed for individual frames.
Another challenge lies in contextual cues: videos provide additional context through object interactions and scene changes across frames that contribute significantly to understanding anomalies within a video sequence. Image-specific explainable methods may struggle with capturing these dynamic relationships present in videos.
Moreover, scalability could be an issue when translating image-based techniques into video settings since processing multiple frames requires more computational resources than analyzing single images individually. The complexity increases when considering long video sequences with high frame rates compared to single images.
Additionally, interpreting anomalies across different modalities (images vs videos) might introduce inconsistencies if the explanation models do not account for variations specific to each modality correctly.

How can the integration of large language models enhance the interpretability of anomaly detection results

The integration of large language models (LLMs) enhances the interpretability of anomaly detection results by leveraging their exceptional reasoning abilities trained on vast amounts of data from various domains.

Semantic Understanding: LLMs like CLIP enable semantic understanding through text-image alignment capabilities that allow them to comprehend visual content based on textual prompts.
Explanatory Prompts: By providing detailed descriptions based on prompt inputs related explicitly to anomalous patterns within images/videos,
LLMs offer intuitive explanations accessible even without prior examples.
Multi-turn Dialogue: Facilitating interaction between users and models enables detailed localization & comprehension
of anomalies via fine-grained semantic capture & prompt learner integration.
Domain-Specific Representation: Expert perception modules embedded within LMMs enhance domain-specific vision-language representation,
incorporating expert-generated annotations/mappings directly into learning processes for improved accuracy & comprehensibility.
By combining visual features with extracted text embeddings fed into LLMs' input token space,
users gain access not only accurate predictions but also meaningful textual explanations enhancing overall transparency &
interpretation capabilities essential for effective deployment & utilization of advanced AI-driven solutions like anomaly detectors