MealySup: A Multi-loss and weakly supervised learning approach for fine-grained object recognition

MealySup: A Multi-loss and weakly supervised learning approach for fine-grained object recognition | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 June 2025 V1 Latest version Share on MealySup: A Multi-loss and weakly supervised learning approach for fine-grained object recognition Authors : Le Hong Trang 0000-0002-6011-2261 [email protected] and Duong Duc Tin Authors Info & Affiliations https://doi.org/10.22541/au.174982173.39813096/v1 209 views 138 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Fine-grained object recognition remains a challenging task due to the subtle visual differences between similar categories. This paper proposes a novel approach to address this challenge by enhancing both feature representation and object localization. Our method introduces a Multi-Classification Module (MCM) and a weakly supervised Multi-Segmentation Module (MSM). The MCM refines feature representations by training each sub-network within the backbone as an independent classifier. The MSM generates object masks from feature maps using a U-Net architecture, providing valuable localization information. These modules can be seamlessly integrated into various backbone networks. Extensive experiments on several benchmark datasets, including CUB, Stanford Cars, and FGVC-Aircraft, demonstrate the superior performance of our method. We also conducted experiments on surface defect datasets including Ball Screw and NEU-DET, to showcase the potential of our approach in machine vision applications. Supplementary Material File (mealysup_.pdf) Download 2.37 MB Information & Authors Information Version history V1 Version 1 13 June 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords defect detection feature discrimination fine-grained classification object recognition representative learning Authors Affiliations Le Hong Trang 0000-0002-6011-2261 [email protected] VNUHCM-Ho Chi Minh City University of Technology View all articles by this author Duong Duc Tin VNUHCM-Ho Chi Minh City University of Technology View all articles by this author Metrics & Citations Metrics Article Usage 209 views 138 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Le Hong Trang, Duong Duc Tin. MealySup: A Multi-loss and weakly supervised learning approach for fine-grained object recognition. Authorea . 13 June 2025. DOI: https://doi.org/10.22541/au.174982173.39813096/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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