  In this paper, we propose a patch-based architecture for multi-label
classification problems where only a single positive label is observed in
images of the dataset. Our contributions are twofold. First, we introduce a
light patch architecture based on the attention mechanism. Next, leveraging on
patch embedding self-similarities, we provide a novel strategy for estimating
negative examples and deal with positive and unlabeled learning problems.
Experiments demonstrate that our architecture can be trained from scratch,
whereas pre-training on similar databases is required for related methods from
the literature.

Computer Vision and Pattern Recognition