PixOOD: Pixel-Level Out-of-Distribution Detection
<|reference_start|>PixOOD: Pixel-Level Out-of-Distribution Detection: We propose a dense image prediction out-of-distribution detection algorithm, called PixOOD, which does not require training on samples of anomalous data and is not designed for a specific application which avoids traditional training biases. In order to model the complex intra-class variability of the in-distribution data at the pixel level, we propose an online data condensation algorithm which is more robust than standard K-means and is easily trainable through SGD. We evaluate PixOOD on a wide range of problems. It achieved state-of-the-art results on four out of seven datasets, while being competitive on the rest. The source code is available at https://github.com/vojirt/PixOOD.<|reference_end|>
@article{vojíř2024pixood:,
    title={PixOOD: Pixel-Level Out-of-Distribution Detection},
    author={Tom'av{s} Voj'iv{r} and Jan v{S}ochman and Jiv{r}'i Matas},
    journal={arXiv preprint arXiv:2405.19882},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19882},
    primaryClass={cs.CV}
}