Odd-One-Out: Anomaly Detection by Comparing with Neighbors
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909547630690304 |
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| author | Bhunia, Ankan Li, Changjian Bilen, Hakan |
| author_facet | Bhunia, Ankan Li, Changjian Bilen, Hakan |
| contents | This paper introduces a novel anomaly detection (AD) problem aimed at identifying `odd-looking' objects within a scene by comparing them to other objects present. Unlike traditional AD benchmarks with fixed anomaly criteria, our task detects anomalies specific to each scene by inferring a reference group of regular objects. To address occlusions, we use multiple views of each scene as input, construct 3D object-centric models for each instance from 2D views, enhancing these models with geometrically consistent part-aware representations. Anomalous objects are then detected through cross-instance comparison. We also introduce two new benchmarks, ToysAD-8K and PartsAD-15K as testbeds for future research in this task. We provide a comprehensive analysis of our method quantitatively and qualitatively on these benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_20099 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Odd-One-Out: Anomaly Detection by Comparing with Neighbors Bhunia, Ankan Li, Changjian Bilen, Hakan Computer Vision and Pattern Recognition This paper introduces a novel anomaly detection (AD) problem aimed at identifying `odd-looking' objects within a scene by comparing them to other objects present. Unlike traditional AD benchmarks with fixed anomaly criteria, our task detects anomalies specific to each scene by inferring a reference group of regular objects. To address occlusions, we use multiple views of each scene as input, construct 3D object-centric models for each instance from 2D views, enhancing these models with geometrically consistent part-aware representations. Anomalous objects are then detected through cross-instance comparison. We also introduce two new benchmarks, ToysAD-8K and PartsAD-15K as testbeds for future research in this task. We provide a comprehensive analysis of our method quantitatively and qualitatively on these benchmarks. |
| title | Odd-One-Out: Anomaly Detection by Comparing with Neighbors |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.20099 |