PDiscoNet: Semantically consistent part discovery for fine-grained
  recognition
Robert van der Klis Stephan Alaniz Massimiliano Mancini Cassio F. Dantas Dino Ienco Zeynep Akata Diego Marcos
Fine-grained classification often requires recognizing specific object parts,
such as beak shape and wing patterns for birds. Encouraging a fine-grained
classification model to first detect such parts and then using them to infer
the class could help us gauge whether the model is indeed looking at the right
details better than with interpretability methods that provide a single
attribution map. We propose PDiscoNet to discover object parts by using only
image-level class labels along with priors encouraging the parts to be:
discriminative, compact, distinct from each other, equivariant to rigid
transforms, and active in at least some of the images. In addition to using the
appropriate losses to encode these priors, we propose to use part-dropout,
where full part feature vectors are dropped at once to prevent a single part
from dominating in the classification, and part feature vector modulation,
which makes the information coming from each part distinct from the perspective
of the classifier. Our results on CUB, CelebA, and PartImageNet show that the
proposed method provides substantially better part discovery performance than
previous methods while not requiring any additional hyper-parameter tuning and
without penalizing the classification performance. The code is available at
https://github.com/robertdvdk/part_detection.