Few-shot Oriented Object Detection with Memorable Contrastive Learning in Remote Sensing Images

arXiv:2403.13375v1 Announce Type: new
Abstract: Few-shot object detection (FSOD) has garnered significant research attention in the field of remote sensing due to its ability to reduce the dependency on large amounts of annotated data. However, two challenges persist in this area: (1) axis-aligned proposals, which can result in misalignment for arbitrarily oriented objects, and (2) the scarcity of annotated data still limits the performance for unseen object categories. To address these issues, we propose a novel FSOD method for remote …

abstract annotated data arxiv attention challenges cs.cv data detection few-shot however images object proposals reduce research sensing type