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arXiv · 2209.06530

A patch-based architecture for multi-label classification from single label annotations

Warren Jouanneau·Aurélie Bugeau·Marc Palyart·Nicolas Papadakis·Laurent Vézard
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Abstract

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.

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Warren Jouanneau, Aurélie Bugeau, Marc Palyart, Nicolas Papadakis, Laurent Vézard. 2022-09-14. A patch-based architecture for multi-label classification from single label annotations. https://arxiv.org/abs/2209.06530

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