Finding Dino: A plug-and-play framework for unsupervised detection of out-of-distribution objects using prototypes

• URL: http://arxiv.org/abs/2404.07664v1
• Date: Thu, 11 Apr 2024 11:55:42 GMT
• Title: Finding Dino: A plug-and-play framework for unsupervised detection of out-of-distribution objects using prototypes
• Authors: Poulami Sinhamahapatra, Franziska Schwaiger, Shirsha Bose, Huiyu Wang, Karsten Roscher, Stephan Guennemann,
• Abstract summary: PRototype-based zero-shot OOD detection Without Labels (PROWL) We present PRototype-based zero-shot OOD detection Without Labels (PROWL) It is an inference-based method that does not require training on the domain dataset. We also demonstrate its suitability for other domains such as rail and maritime scenes.
• Score: 12.82756672393553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting and localising unknown or Out-of-distribution (OOD) objects in any scene can be a challenging task in vision. Particularly, in safety-critical cases involving autonomous systems like automated vehicles or trains. Supervised anomaly segmentation or open-world object detection models depend on training on exhaustively annotated datasets for every domain and still struggle in distinguishing between background and OOD objects. In this work, we present a plug-and-play generalised framework - PRototype-based zero-shot OOD detection Without Labels (PROWL). It is an inference-based method that does not require training on the domain dataset and relies on extracting relevant features from self-supervised pre-trained models. PROWL can be easily adapted to detect OOD objects in any operational design domain by specifying a list of known classes from this domain. PROWL, as an unsupervised method, outperforms other supervised methods trained without auxiliary OOD data on the RoadAnomaly and RoadObstacle datasets provided in SegmentMeIfYouCan (SMIYC) benchmark. We also demonstrate its suitability for other domains such as rail and maritime scenes.

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