  Computer vision methods that explicitly detect object parts and reason on
them are a step towards inherently interpretable models. Existing approaches
that perform part discovery driven by a fine-grained classification task make
very restrictive assumptions on the geometric properties of the discovered
parts; they should be small and compact. Although this prior is useful in some
cases, in this paper we show that pre-trained transformer-based vision models,
such as self-supervised DINOv2 ViT, enable the relaxation of these constraints.
In particular, we find that a total variation (TV) prior, which allows for
multiple connected components of any size, substantially outperforms previous
work. We test our approach on three fine-grained classification benchmarks:
CUB, PartImageNet and Oxford Flowers, and compare our results to previously
published methods as well as a re-implementation of the state-of-the-art method
PDiscoNet with a transformer-based backbone. We consistently obtain substantial
improvements across the board, both on part discovery metrics and the
downstream classification task, showing that the strong inductive biases in
self-supervised ViT models require to rethink the geometric priors that can be
used for unsupervised part discovery.
