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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2410.09797 |
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| _version_ | 1866912122207731712 |
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| author | Li, Ping Wang, Hongbo Lu, Lei |
| author_facet | Li, Ping Wang, Hongbo Lu, Lei |
| contents | Metric-based few-shot fine-grained classification has shown promise due to its simplicity and efficiency. However, existing methods often overlook task-level special cases and struggle with accurate category description and irrelevant sample information. To tackle these, we propose TAFD-Net: a task adaptive feature distribution network. It features a task-adaptive component for embedding to capture task-level nuances, an asymmetric metric for calculating feature distribution similarities between query samples and support categories, and a contrastive measure strategy to boost performance. Extensive experiments have been conducted on three datasets and the experimental results show that our proposed algorithm outperforms recent incremental learning algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09797 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Task Adaptive Feature Distribution Based Network for Few-shot Fine-grained Target Classification Li, Ping Wang, Hongbo Lu, Lei Computer Vision and Pattern Recognition Metric-based few-shot fine-grained classification has shown promise due to its simplicity and efficiency. However, existing methods often overlook task-level special cases and struggle with accurate category description and irrelevant sample information. To tackle these, we propose TAFD-Net: a task adaptive feature distribution network. It features a task-adaptive component for embedding to capture task-level nuances, an asymmetric metric for calculating feature distribution similarities between query samples and support categories, and a contrastive measure strategy to boost performance. Extensive experiments have been conducted on three datasets and the experimental results show that our proposed algorithm outperforms recent incremental learning algorithms. |
| title | Task Adaptive Feature Distribution Based Network for Few-shot Fine-grained Target Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.09797 |