Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning

• URL: http://arxiv.org/abs/2208.07039v2
• Date: Tue, 16 Aug 2022 07:48:27 GMT
• Title: Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning
• Authors: Dongwoo Park, Jong-Min Lee
• Abstract summary: Few-shot object detection (FSOD) aims to classify and detect few images of novel categories. We propose a hierarchical attention network with sequentially large receptive fields to fully exploit the query and support images. 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.
• Score: 4.952681349410351
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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