Bi-focus cosine complement network for few-shot fine-grained image classification

IF 3.9 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Recognition Letters Pub Date : 2025-03-11 DOI:10.1016/j.patrec.2025.03.002
Penghao Jia, Guanglei Gou, Yu Cheng, Aoxiang Ning
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Abstract

Few-shot fine-grained image classification presents significant challenges due to the need to discern subtle differences with few samples. And the image backgrounds often contain substantial redundant information. Existing methods utilize features containing redundant information directly after extraction to compute distances for classification, which diminishes the model’s object-level distinguishing capability. Additionally, these methods struggle to integrate discriminative and non-discriminative features within hierarchical architectures. To address this, we propose a Bi-focus Cosine Complement Network (BfCCN). In BfCCN, the Bi-focus Module (BFM) harnesses both spatial and channel dimension information to precisely localize targets within images by mitigating the impact of background noise on the model. Moreover, the Cosine Complement Network (CCN) leverages multi-level and multi-type features to capture the fine-grained details typically present in shallow layers but absent in deeper layers, thereby enhancing the model’s ability to detect subtle distinctions by utilizing complementary cosine distance for classification. Experiments on three benchmark fine-grained datasets demonstrate the efficacy of our method. Codes will be available at https://github.com/naivejph/BFCCN.git.
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来源期刊
Pattern Recognition Letters
Pattern Recognition Letters 工程技术-计算机:人工智能
CiteScore
12.40
自引率
5.90%
发文量
287
审稿时长
9.1 months
期刊介绍: Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition. Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition.
期刊最新文献
Editorial Board Bi-focus cosine complement network for few-shot fine-grained image classification Enhancing visual adversarial transferability via affine transformation of intermediate-level perturbations DAUNet: A deformable aggregation UNet for multi-organ 3D medical image segmentation ObjMST: Object-focused multimodal style transfer
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