mlsanityData to 7 Oct 2026

Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference

arXiv 2403.14213 ↗First posted 21 Mar 2024cs.CVcited by 7

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Abstract (arXiv)

In the context of high usability in single-class anomaly detection models, recent academic research has become concerned about the more complex multi-class anomaly detection. Although several papers have designed unified models for this task, they often overlook the utility of class labels, a potent tool for mitigating inter-class interference. To address this issue, we introduce a Multi-class Implicit Neural representation Transformer for unified Anomaly Detection (MINT-AD), which leverages the fine-grained category information in the training stage. By learning the multi-class distributions, the model generates class-aware query embeddings for the transformer decoder, mitigating inter-class interference within the reconstruction model. Utilizing such an implicit neural representation network, MINT-AD can project category and position information into a feature embedding space, further supervised by classification and prior probability loss functions. Experimental results on multiple datasets demonstrate that MINT-AD outperforms existing unified training models.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 41 of its 54 references that are in the map. First posted on arXiv 21 Mar 2024.

Builds on

41 of 54 references.

References · 41

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29 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 4.

7 papers.

  1. 2024MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detectioncited by 46
  2. 2026AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparisoncited by 5
  3. 2026EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Modelscited by 2
  4. 2025Multi-class Image Anomaly Detection for Practical Applications: Requirements and Robust Solutionscited by 1
  5. 2025Generalist Multi-Class Anomaly Detection via Distillation to Two Heterogeneous Student Networkscited by 0
  6. 2026Modulate-and-Map: Crossmodal Feature Mapping with Cross-View Modulation for 3D Anomaly Detectioncited by 0
  7. 2026AnomalyClaw: A Universal Visual Anomaly Detection Agent via Tool-Grounded Refutationcited by 0