Abstract
We propose a pixel-level 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bae, J., Lee, J.H., Kim, S.: PNI: industrial anomaly detection using position and neighborhood information. In: ICCV, pp. 6373–6383, October 2023
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: MVTec AD – a comprehensive real-world dataset for unsupervised anomaly detection. In: CVPR, June 2019
Besnier, V., Bursuc, A., Picard, D., Alexandre, B.: Triggering Failures: out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation. In: ICCV (2021)
Blum, H., Sarlin, P.E., Nieto, J., Siegwart, R., Cadena, C.: The Fishyscapes benchmark: measuring blind spots in semantic segmentation. arXiv:1904.03215 (2019)
Bovcon, B., Muhovič, J., Vranac, D., Mozetič, D., Perš, J., Kristan, M.: MODS-A USV-oriented object detection and obstacle segmentation benchmark. ITS (2021). https://doi.org/10.1109/TITS.2021.3124192
Cai, Y., Liang, D., Luo, D., He, X., Yang, X., Bai, X.: A discrepancy aware framework for robust anomaly detection. IEEE Tran. Indust. Info. (2023). https://doi.org/10.1109/TII.2023.3318302
Chan, R., et al.: SegmentMeIfYouCan: a benchmark for anomaly segmentation. In: NeurIPS Datasets and Bench (2021)
Chan, R., Rottmann, M., Gottschalk, H.: Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation. In: ICCV, October 2021
Cho, M., Alizadeh-Vahid, K., Adya, S., Rastegari, M.: DKM: differentiable k-means clustering layer for neural network compression. In: ICLR (2022). https://openreview.net/forum?id=J_F_qqCE3Z5
Defard, T., Setkov, A., Loesch, A., Audigier, R.: PaDiM: a patch distribution modeling framework for anomaly detection and localization. In: ICPRW (2020)
Di Biase, G., Blum, H., Siegwart, R., Cadena, C.: Pixel-wise anomaly detection in complex driving scenes. In: CVPR, June 2021
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR (2021). https://openreview.net/forum?id=YicbFdNTTy
Galesso, S., Argus, M., Brox, T.: Far away in the deep space: dense nearest-neighbor-based out-of-distribution detection. In: ICCVW, October 2023
Gao, Z., Yan, S., He, X.: ATTA: anomaly-aware test-time adaptation for out-of-distribution detection in segmentation. In: NeurIPS (2023). https://openreview.net/forum?id=bGcdjXrU2w
Grcic, M., Bevandic, P., Segvic, S.: DenseHybrid: hybrid anomaly detection for dense open-set recognition. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13685, pp. 500–517. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19806-9_29
Grcić, M., Šarić, J., Šegvić, S.: On advantages of mask-level recognition for outlier-aware segmentation. In: CVPRW, June 2023
Grcić, M., Bevandić, P., Kalafatić, Z., Šegvić, S.: Dense out-of-distribution detection by robust learning on synthetic negative data. In: arXiv:2112.12833 (2023)
Gu, Z., Zhu, B., Zhu, G., Chen, Y., Tang, M., Wang, J.: AnomalyGPT: detecting industrial anomalies using large vision-language models. arXiv preprint arXiv:2308.15366 (2023)
Gudovskiy, D., Ishizaka, S., Kozuka, K.: CFLOW-AD: real-time unsupervised anomaly detection with localization via conditional normalizing flows. In: WACV, pp. 98–107, January 2022
Hendrycks, D., Gimpel, K.: Gaussian Error Linear Units (GELUs). arXiv:1606.08415 (2023)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. In: ICLR (2018). https://openreview.net/forum?id=H1VGkIxRZ
Lis, K., Honari, S., Fua, P., Salzmann, M.: Detecting road obstacles by erasing them. In: arXiv:2012.13633 (2020)
Lis, K., Nakka, K., Fua, P., Salzmann, M.: Detecting the unexpected via image resynthesis. In: ICCV, October 2019. https://infoscience.epfl.ch/record/269093?ln=en
Liu, Y., et al.: Residual pattern learning for pixel-wise out-of-distribution detection in semantic segmentation. In: ICCV, October 2023
Lu, R., et al.: Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection. In: NeurIPS (2023). https://openreview.net/forum?id=clJTNssgn6
Nayal, N., Yavuz, M., Henriques, J.F., Güney, F.: RbA: segmenting unknown regions rejected by all. In: ICCV (2023)
Neyman, J., Pearson, E.S.: On the use and interpretation of certain test criteria for purposes of statistical inference. Biometrika (1928)
Neyman, J., Pearson, E.S.: Ix. on the problem of the most efficient tests of statistical hypotheses. Philos. Trans. Royal Soc. Lond. Ser. A Containing Papers of a Mathematical or Physical Character 231 (1933)
Oquab, M., et al.: DINOv2: learning robust visual features without supervision (2023)
Pinggera, P., Ramos, S., Gehrig, S., Franke, U., Rother, C., Mester, R.: Lost and found: detecting small road hazards for self-driving vehicles. In: IROS (2016)
R. H. Byrd, P.L., Nocedal, J.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Stat. Comput. 16 (1995)
Radford, A., et al.: Learning transferable visual models from natural language supervision. In: ICML, pp. 8748–8763 (2021)
Rai, S.N., Cermelli, F., Fontanel, D., Masone, C., Caputo, B.: Unmasking anomalies in road-scene segmentation. In: ICCV, October 2023
Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: CVPR, pp. 14318–14328 (2022)
Schlesinger, M.I., Hlavac, V.: Ten Lectures on Statistical and Structural Pattern Recognition. Computational Imaging and Vision, Springer, Dordrecht (2002). https://doi.org/10.1007/978-94-017-3217-8
Tian, Y., Liu, Y., Pang, G., Liu, F., Chen, Y., Carneiro, G.: Pixel-wise energy-biased abstention learning for anomaly segmentation on complex urban driving scenes. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13699, pp. 246–263. Springer, Cham (2021). https://doi.org/10.1007/978-3-031-19842-7_15
Vojir, T., Šipka, T., Aljundi, R., Chumerin, N., Reino, D.O., Matas, J.: Road anomaly detection by partial image reconstruction with segmentation coupling. In: ICCV (2021)
Vojíř, T., Matas, J.: Image-consistent detection of road anomalies as unpredictable patches. In: WACV (2023)
Vojíř, T., Šochman, J., Aljundi, R., Matas, J.: Calibrated out-of-distribution detection with a generic representation. In: ICCVW, October 2023
Wang, H., Li, Y., Yao, H., Li, X.: CLIPN for zero-shot OOD detection: teaching CLIP to say no. In: ICCV, October 2023
You, Z., et al.: A unified model for multi-class anomaly detection. In: NeurIPS (2022)
Zavrtanik, V., Kristan, M., Skocaj, D.: DRAEM - a discriminatively trained reconstruction embedding for surface anomaly detection. In: ICCV (2021)
Zhang, H., Li, F., Qi, L., Yang, M.H., Ahuja, N.: CSL: class-agnostic structure-constrained learning for segmentation including the unseen. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 7 (2024). https://doi.org/10.1609/aaai.v38i7.28535
Žust, L., Perš, J., Kristan, M.: LaRS: a diverse panoptic maritime obstacle detection dataset and benchmark. In: ICCV (2023)
Acknowledgement
This work was supported by Toyota Motor Europe and Czech Technical University in Prague institutional support Future Fund.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Vojíř, T., Šochman, J., Matas, J. (2025). PixOOD: Pixel-Level Out-of-Distribution Detection. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15118. Springer, Cham. https://doi.org/10.1007/978-3-031-73027-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-73027-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-73026-9
Online ISBN: 978-3-031-73027-6
eBook Packages: Computer ScienceComputer Science (R0)