Finding Dino: A plug-and-play framework for unsupervised detection of
  out-of-distribution objects using prototypes
Poulami Sinhamahapatra Franziska Schwaiger Shirsha Bose Huiyu Wang Karsten Roscher Stephan Guennemann
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.