DP-Net: Learning Discriminative Parts for image recognition

• URL: http://arxiv.org/abs/2404.15037v1
• Date: Tue, 23 Apr 2024 13:42:12 GMT
• Title: DP-Net: Learning Discriminative Parts for image recognition
• Authors: Ronan Sicre, Hanwei Zhang, Julien Dejasmin, Chiheb Daaloul, Stéphane Ayache, Thierry Artières,
• Abstract summary: DP-Net is a deep architecture with strong interpretation capabilities. It exploits a pretrained Convolutional Neural Network (CNN) combined with a part-based recognition module.
• Score: 4.480595534587716
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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