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- W4292199858 abstract "Few-shot object detection (FSOD) aims to classify and detect few images of novel categories. Existing meta-learning methods insufficiently exploit features between support and query images owing to structural limitations. We propose a hierarchical attention network with sequentially large receptive fields to fully exploit the query and support images. In addition, meta-learning does not distinguish the categories well because it determines whether the support and query images match. In other words, metric-based learning for classification is ineffective because it does not work directly. Thus, we propose a contrastive learning method called meta-contrastive learning, which directly helps achieve the purpose of the meta-learning strategy. Finally, we establish a new state-of-the-art network, by realizing significant margins. Our method brings 2.3, 1.0, 1.3, 3.4 and 2.4% AP improvements for 1-30 shots object detection on COCO dataset. Our code is available at: https://github.com/infinity7428/hANMCL" @default.
- W4292199858 created "2022-08-18" @default.
- W4292199858 creator A5015283188 @default.
- W4292199858 creator A5081702341 @default.
- W4292199858 date "2022-08-15" @default.
- W4292199858 modified "2023-10-16" @default.
- W4292199858 title "Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning" @default.
- W4292199858 doi "https://doi.org/10.48550/arxiv.2208.07039" @default.
- W4292199858 hasPublicationYear "2022" @default.
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