PARTICUL: Part Identification with Confidence measure using Unsupervised Learning

• URL: http://arxiv.org/abs/2206.13304v1
• Date: Mon, 27 Jun 2022 13:44:49 GMT
• Title: PARTICUL: Part Identification with Confidence measure using Unsupervised Learning
• Authors: Romain Xu-Darme (LSL, MRIM ), Georges Qu\'enot (MRIM ), Zakaria Chihani (LSL), Marie-Christine Rousset (SLIDE )
• Abstract summary: PARTICUL is a novel algorithm for unsupervised learning of part detectors from datasets used in fine-grained recognition. It exploits the macro-similarities of all images in the training set in order to mine for recurring patterns in the feature space of a pre-trained convolutional neural network. We show that our detectors can consistently highlight parts of the object while providing a good measure of the confidence in their prediction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present PARTICUL, a novel algorithm for unsupervised learning of part detectors from datasets used in fine-grained recognition. It exploits the macro-similarities of all images in the training set in order to mine for recurring patterns in the feature space of a pre-trained convolutional neural network. We propose new objective functions enforcing the locality and unicity of the detected parts. Additionally, we embed our detectors with a confidence measure based on correlation scores, allowing the system to estimate the visibility of each part. We apply our method on two public fine-grained datasets (Caltech-UCSD Bird 200 and Stanford Cars) and show that our detectors can consistently highlight parts of the object while providing a good measure of the confidence in their prediction. We also demonstrate that these detectors can be directly used to build part-based fine-grained classifiers that provide a good compromise between the transparency of prototype-based approaches and the performance of non-interpretable methods.

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