mlsanityData to 14 Oct 2026

Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videos

arXiv 2302.06670 ↗First posted 13 Feb 2023cs.LG cs.AIcited by 7

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Abstract (arXiv)

Anomaly detection and localization in visual data, including images and videos, are crucial in machine learning and real-world applications. Despite rapid advancements in visual anomaly detection (VAD), interpreting these often black-box models and explaining why specific instances are flagged as anomalous remains challenging. This paper provides the first comprehensive survey focused specifically on explainable 2D visual anomaly detection (X-VAD), covering methods for both images (IAD) and videos (VAD). We first introduce the background of IAD and VAD. Then, as the core contribution, we present a thorough literature review of explainable methods, categorized by their underlying techniques (e.g., attention-based, generative model-based, reasoning-based, foundation model-based). We analyze the commonalities and differences in applying these methods across image and video modalities, highlighting modality-specific challenges and opportunities for explainability. Additionally, we summarize relevant datasets and evaluation metrics, discussing both standard performance metrics and emerging approaches for assessing explanation quality (e.g., faithfulness, stability). Finally, we discuss promising future directions and open problems, including quantifying explanation quality, explaining diverse AD paradigms (SSL, zero-shot), enhancing context-awareness, leveraging foundation models responsibly, and addressing real-world constraints like efficiency and robustness. A curated collection of related resources is available at https://github.com/wyzjack/Awesome-XAD.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 87 of its 172 references that are in the map. First posted on arXiv 13 Feb 2023.

Builds on

87 of 172 references.

References · 87

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 2; 2026: 3.

7 papers.

  1. 2023VaQuitA: Enhancing Alignment in LLM-Assisted Video Understandingcited by 18
  2. 2023Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network?cited by 8
  3. 2025BalancEdit: Dynamically Balancing the Generality-Locality Trade-off in Multi-modal Model Editingcited by 7
  4. 2026FoMo X: Modular Explainability Signals for Outlier Detection Foundation Modelscited by 2
  5. 2026Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervisioncited by 1
  6. 2025Research on Anomaly Detection Methods Based on Diffusion Modelscited by 0
  7. 2026Augmenting Visual Anomaly Detection with Automated Interpretabilitycited by 0