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Multi-branch and multi-loss learning for fine-grained image retrieval.

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Bibliographic Details
Title: Multi-branch and multi-loss learning for fine-grained image retrieval.
Authors: Lu, Hongchun1,2 (AUTHOR), Han, Min1,2 (AUTHOR) hanmin@swjtu.edu.cn, He, Songlin1,2 (AUTHOR), Li, Xue3 (AUTHOR), Wu, Chase4 (AUTHOR)
Source: Applied Soft Computing. Dec2025:Part B, Vol. 185, pN.PAG-N.PAG. 1p.
Subject Terms: Image retrieval, Deep learning, Binary codes, Loss functions (Statistics)
Abstract: To effectively address the problem of low accuracy of fine-grained image retrieval due to significant intra-class differences and small inter-class differences, we propose a novel and highly reliable fine-grained deep hashing learning framework dubbed MBLNet to accurately retrieve fine-grained images. Specifically, we propose (i) a dual-selected significant region erasure method for generating compact binary codes for fine-grained images; (ii) a dual filtering object location method for mining discriminative local features; and (iii) a new multi-stage loss function for optimizing network training. We conducted extensive experiments on three fine-grained datasets, Stanford Cars, FGVC-Aircraft, and CUB-200-2011, and achieved mAP results of 89.3%, 87.2%, and 80.6%, respectively. Additionally, the ablation study demonstrates that both the dual-selected significant region erasure method and the dual filtering object location method contribute to the improved accuracy of fine-grained image retrieval, further validating the effectiveness of the proposed method. Code can be found at https://github.com/luhongchun/MBLNet.git. • Build a novel and highly reliable fine-grained deep hash learning framework for more accurate retrieval of fine-grained images. • Propose a dual-selected significant region erasure method for generating compact binary codes for fine-grained images. • Introduce a dual filtering object location method for mining discriminative local features. • Design a new multi-stage loss function for optimizing network training. [ABSTRACT FROM AUTHOR]
Database: Supplemental Index
Description
Abstract:To effectively address the problem of low accuracy of fine-grained image retrieval due to significant intra-class differences and small inter-class differences, we propose a novel and highly reliable fine-grained deep hashing learning framework dubbed MBLNet to accurately retrieve fine-grained images. Specifically, we propose (i) a dual-selected significant region erasure method for generating compact binary codes for fine-grained images; (ii) a dual filtering object location method for mining discriminative local features; and (iii) a new multi-stage loss function for optimizing network training. We conducted extensive experiments on three fine-grained datasets, Stanford Cars, FGVC-Aircraft, and CUB-200-2011, and achieved mAP results of 89.3%, 87.2%, and 80.6%, respectively. Additionally, the ablation study demonstrates that both the dual-selected significant region erasure method and the dual filtering object location method contribute to the improved accuracy of fine-grained image retrieval, further validating the effectiveness of the proposed method. Code can be found at https://github.com/luhongchun/MBLNet.git. • Build a novel and highly reliable fine-grained deep hash learning framework for more accurate retrieval of fine-grained images. • Propose a dual-selected significant region erasure method for generating compact binary codes for fine-grained images. • Introduce a dual filtering object location method for mining discriminative local features. • Design a new multi-stage loss function for optimizing network training. [ABSTRACT FROM AUTHOR]
ISSN:15684946
DOI:10.1016/j.asoc.2025.113833