mlsanityData to 7 Oct 2026

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects

arXiv 2412.04867 ↗First posted 6 Dec 2024cs.CVcited by 26

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Abstract (arXiv)

We present MANTA, a visual-text anomaly detection dataset for tiny objects. The visual component comprises over 137.3K images across 38 object categories spanning five typical domains, of which 8.6K images are labeled as anomalous with pixel-level annotations. Each image is captured from five distinct viewpoints to ensure comprehensive object coverage. The text component consists of two subsets: Declarative Knowledge, including 875 words that describe common anomalies across various domains and specific categories, with detailed explanations for < what, why, how>, including causes and visual characteristics; and Constructivist Learning, providing 2K multiple-choice questions with varying levels of difficulty, each paired with images and corresponded answer explanations. We also propose a baseline for visual-text tasks and conduct extensive benchmarking experiments to evaluate advanced methods across different settings, highlighting the challenges and efficacy of our dataset.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 49 of its 73 references that are in the map. First posted on arXiv 6 Dec 2024.

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49 of 73 references.

References · 49

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37 more
  1. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,417
  2. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  3. 2024Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMscited by 719
  4. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  5. 2024LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Modelscited by 571
  6. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  7. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  8. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  9. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  10. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
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  12. 2022A Unified Model for Multi-class Anomaly Detectioncited by 267
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  14. 2022ADBench: Anomaly Detection Benchmarkcited by 230
  15. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 222
  16. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  17. 2019Segmentation-Based Deep-Learning Approach for Surface-Defect Detectioncited by 145
  18. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
  19. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  20. 2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
  21. 2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 97
  22. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
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Citing papers per year: 2025: 11; 2026: 15.

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  2. 2025LLaPa: A Vision-Language Model Framework for Counterfactual-Aware Procedural Planningcited by 6
  3. 2025Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detectioncited by 6
  4. 2026AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparisoncited by 5
  5. 2025SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignmentcited by 4
  6. 2025"Stones from Other Hills can Polish Jade": Zero-shot Anomaly Image Synthesis via Cross-domain Anomaly Injectioncited by 3
  7. 2025Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmarkcited by 3
  8. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  9. 2025On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detectioncited by 2
  10. 2025Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Eracited by 2
  11. 2025ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categoriescited by 2
  12. 2026Visual Prompt-Agnostic Evolutioncited by 2
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  1. 2025Unsupervised Multi-View Visual Anomaly Detection via Progressive Homography-Guided Alignmentcited by 1
  2. 2025GAOT: Generating Articulated Objects Through Text-Guided Diffusion Modelscited by 1
  3. 2026M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detectioncited by 1
  4. 2026MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Modelscited by 1
  5. 2026IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusioncited by 1
  6. 2026Semantic-Deviation-Anchored Multi-Branch Fusion for Unsupervised Anomaly Detection and Localization in Unstructured Conveyor-Belt Coal Scenescited by 0
  7. 2026VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distractioncited by 0
  8. 2026Large-Scale Universal Defect Generation: Foundation Models and Datasetscited by 0
  9. 2026MATCH: Flow Matching for Multi-View Anomaly Detectioncited by 0
  10. 2026ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detectioncited by 0
  11. 2026See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detectioncited by 0
  12. 2026GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignmentcited by 0
  13. 2026DPA: Decoupling Product-Agnostic Anomaly Representations for Zero-shot Anomaly Generationcited by 0
  14. 2026VD-DeepStack: Bridging Visual Comparison and Language Reasoning for Few-Shot Anomaly Detectioncited by 0