DCPNet: Distribution Calibration Prototypical Network for Few-Shot Image Classification
Deep learning has witnessed significant advancements in various tasks and has displayed exceptional performance. However, traditional deep learning techniques often necessitate the utilization of extensive labeled data for training, a requirement that is challenging to fulfill in many real-world sce...
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| Format: | Article |
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10522678/ |
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| author | Ranhui Xu Kaizhong Jiang Lulu Qi Shaojie Zhao Mingming Zheng |
| author_facet | Ranhui Xu Kaizhong Jiang Lulu Qi Shaojie Zhao Mingming Zheng |
| author_sort | Ranhui Xu |
| collection | DOAJ |
| description | Deep learning has witnessed significant advancements in various tasks and has displayed exceptional performance. However, traditional deep learning techniques often necessitate the utilization of extensive labeled data for training, a requirement that is challenging to fulfill in many real-world scenarios. This limitation has given rise to the field of few-shot learning (FSL). In this paper, we introduce a Distribution Calibration Prototypical Network (DCPNet), aiming to address the limitations of prototypical networks in terms of their weak feature extraction capabilities and the inability of their classifier boundaries to align with the dataset. DCPNet incorporates a parallel hierarchical feature extraction module and a few-shot differentiation loss function to fine-tune the metric learning for better feature representation. This approach employs a parallel approach to extract features based on the semantic depth of image hierarchical extraction and incorporates contrastive learning to achieve feature vector fusion. Furthermore, DCPNet incorporates an improved distribution calibration method that leverages information from the base class dataset to align classifier boundaries with the dataset. To validate our approach, we conducted comparative experiments on datasets such as Mini-Imagenet, Omniglot, and CUB using classical baseline methods. In additional, we conducted ablation experiments on the Mini-Imagenet to assess the performance effectiveness of each component of the model. The results demonstrate that the proposed method presented in this paper outperforms other approaches and offer new insights into the field of few-shot image classification. |
| format | Article |
| id | doaj-art-d15dd45018f6484284604c847c5a7828 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d15dd45018f6484284604c847c5a78282025-08-20T02:33:55ZengIEEEIEEE Access2169-35362024-01-0112670366704510.1109/ACCESS.2024.339813410522678DCPNet: Distribution Calibration Prototypical Network for Few-Shot Image ClassificationRanhui Xu0https://orcid.org/0009-0007-6463-1855Kaizhong Jiang1Lulu Qi2Shaojie Zhao3https://orcid.org/0000-0003-3836-7759Mingming Zheng4https://orcid.org/0009-0002-9084-0603School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, ChinaDeep learning has witnessed significant advancements in various tasks and has displayed exceptional performance. However, traditional deep learning techniques often necessitate the utilization of extensive labeled data for training, a requirement that is challenging to fulfill in many real-world scenarios. This limitation has given rise to the field of few-shot learning (FSL). In this paper, we introduce a Distribution Calibration Prototypical Network (DCPNet), aiming to address the limitations of prototypical networks in terms of their weak feature extraction capabilities and the inability of their classifier boundaries to align with the dataset. DCPNet incorporates a parallel hierarchical feature extraction module and a few-shot differentiation loss function to fine-tune the metric learning for better feature representation. This approach employs a parallel approach to extract features based on the semantic depth of image hierarchical extraction and incorporates contrastive learning to achieve feature vector fusion. Furthermore, DCPNet incorporates an improved distribution calibration method that leverages information from the base class dataset to align classifier boundaries with the dataset. To validate our approach, we conducted comparative experiments on datasets such as Mini-Imagenet, Omniglot, and CUB using classical baseline methods. In additional, we conducted ablation experiments on the Mini-Imagenet to assess the performance effectiveness of each component of the model. The results demonstrate that the proposed method presented in this paper outperforms other approaches and offer new insights into the field of few-shot image classification.https://ieeexplore.ieee.org/document/10522678/Improved distribution calibrationfew-shot learningprototypical networkimage classificationcomputer vision |
| spellingShingle | Ranhui Xu Kaizhong Jiang Lulu Qi Shaojie Zhao Mingming Zheng DCPNet: Distribution Calibration Prototypical Network for Few-Shot Image Classification IEEE Access Improved distribution calibration few-shot learning prototypical network image classification computer vision |
| title | DCPNet: Distribution Calibration Prototypical Network for Few-Shot Image Classification |
| title_full | DCPNet: Distribution Calibration Prototypical Network for Few-Shot Image Classification |
| title_fullStr | DCPNet: Distribution Calibration Prototypical Network for Few-Shot Image Classification |
| title_full_unstemmed | DCPNet: Distribution Calibration Prototypical Network for Few-Shot Image Classification |
| title_short | DCPNet: Distribution Calibration Prototypical Network for Few-Shot Image Classification |
| title_sort | dcpnet distribution calibration prototypical network for few shot image classification |
| topic | Improved distribution calibration few-shot learning prototypical network image classification computer vision |
| url | https://ieeexplore.ieee.org/document/10522678/ |
| work_keys_str_mv | AT ranhuixu dcpnetdistributioncalibrationprototypicalnetworkforfewshotimageclassification AT kaizhongjiang dcpnetdistributioncalibrationprototypicalnetworkforfewshotimageclassification AT luluqi dcpnetdistributioncalibrationprototypicalnetworkforfewshotimageclassification AT shaojiezhao dcpnetdistributioncalibrationprototypicalnetworkforfewshotimageclassification AT mingmingzheng dcpnetdistributioncalibrationprototypicalnetworkforfewshotimageclassification |