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Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning. (arXiv:2208.07039v2 [cs.CV] UPDATED)
cs.CV updates on arXiv.org arxiv.org
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 …
arxiv attention cv detection hierarchical learning meta network