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arXiv · 2404.15037

DP-Net: Learning Discriminative Parts for image recognition

Ronan Sicre·Hanwei Zhang·Julien Dejasmin·Chiheb Daaloul·Stéphane Ayache·Thierry Artières
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Abstract

This paper presents Discriminative Part Network (DP-Net), a deep architecture with strong interpretation capabilities, which exploits a pretrained Convolutional Neural Network (CNN) combined with a part-based recognition module. This system learns and detects parts in the images that are discriminative among categories, without the need for fine-tuning the CNN, making it more scalable than other part-based models. While part-based approaches naturally offer interpretable representations, we propose explanations at image and category levels and introduce specific constraints on the part learning process to make them more discrimative.

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Ronan Sicre, Hanwei Zhang, Julien Dejasmin, Chiheb Daaloul, Stéphane Ayache, Thierry Artières. 2024-04-23. DP-Net: Learning Discriminative Parts for image recognition. https://doi.org/10.1109/icip49359.2023.10222053

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