Odd-One-Out: Anomaly Detection by Comparing with Neighbors
<|reference_start|>Odd-One-Out: Anomaly Detection by Comparing with Neighbors: This paper introduces a novel anomaly detection (AD) problem that focuses on identifying `odd-looking' objects relative to the other instances within a scene. Unlike the traditional AD benchmarks, in our setting, anomalies in this context are scene-specific, defined by the regular instances that make up the majority. Since object instances are often partly visible from a single viewpoint, our setting provides multiple views of each scene as input. To provide a testbed for future research in this task, we introduce two benchmarks, ToysAD-8K and PartsAD-15K. We propose a novel method that generates 3D object-centric representations for each instance and detects the anomalous ones through a cross-examination between the instances. We rigorously analyze our method quantitatively and qualitatively in the presented benchmarks.<|reference_end|>
@article{bhunia2024odd-one-out:,
    title={Odd-One-Out: Anomaly Detection by Comparing with Neighbors},
    author={Ankan Bhunia, Changjian Li, Hakan Bilen},
    journal={arXiv preprint arXiv:2406.20099},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.20099},
    primaryClass={cs.CV}
}