PANDAS: Prototype-based Novel Class Discovery and Detection
Tyler L. Hayes César R. de Souza Namil Kim Jiwon Kim Riccardo Volpi Diane Larlus
Object detectors are typically trained once and for all on a fixed set of
classes. However, this closed-world assumption is unrealistic in practice, as
new classes will inevitably emerge after the detector is deployed in the wild.
In this work, we look at ways to extend a detector trained for a set of base
classes so it can i) spot the presence of novel classes, and ii) automatically
enrich its repertoire to be able to detect those newly discovered classes
together with the base ones. We propose PANDAS, a method for novel class
discovery and detection. It discovers clusters representing novel classes from
unlabeled data, and represents old and new classes with prototypes. During
inference, a distance-based classifier uses these prototypes to assign a label
to each detected object instance. The simplicity of our method makes it widely
applicable. We experimentally demonstrate the effectiveness of PANDAS on the
VOC 2012 and COCO-to-LVIS benchmarks. It performs favorably against the state
of the art for this task while being computationally more affordable.