Patch Impact Factor Module for Fine-grained Image Recognition

Patch Impact Factor Module for Fine-grained Image 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 19 June 2025 V1 Latest version Share on Patch Impact Factor Module for Fine-grained Image Recognition Authors : Zujun Liu , Fei Gao [email protected] , Ying Xing , and Tianyu Lu 0009-0008-1743-4530 Authors Info & Affiliations https://doi.org/10.22541/au.175031126.63971053/v1 146 views 114 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Fine-grained image recognition aims to achieve classification of subclasses by processing detailed features, which is still a critical problem to be solved in computing due to the small differences between subclasses. Most of the work extracts the detected features by reusing the backbone network or by using RPN (RegionProposal Network), these operations undoubtedly increase the complexity of the work. In recent years, Transformer has shown satisfactory performance in vision tasks. Transformer decomposes input images into patch of the same size, and classifies the input images by computing the attention scores between patches multiple times and weighting them. In this paper, we propose the PIFM (Patch Impact Factor Module) with reference to the SENet. Specifically, the weight calculation is performed on the patch obtained from each transformer layer calculation, then the patch is fused using the calculated weights. The result of the weight calculation represents the importance of the patch and indicates the factor by which the network should fuse the patch. To verify the effectiveness of our method, we conducted experiments on the CUB-200-2011 and stanford-dog datasets. Supplementary Material File (patch impact factor module for fine-grained image recognition.pdf) Download 1.57 MB Information & Authors Information Version history V1 Version 1 19 June 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords feature extraction fine-grained image recognition patch impact factor module transformer Authors Affiliations Zujun Liu Smart Steps Digital Technology Co Ltd View all articles by this author Fei Gao [email protected] Tibet University View all articles by this author Ying Xing Beijing University of Posts and Telecommunications View all articles by this author Tianyu Lu 0009-0008-1743-4530 Beijing University of Posts and Telecommunications View all articles by this author Metrics & Citations Metrics Article Usage 146 views 114 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Zujun Liu, Fei Gao, Ying Xing, et al. Patch Impact Factor Module for Fine-grained Image Recognition. Authorea . 19 June 2025. DOI: https://doi.org/10.22541/au.175031126.63971053/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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