Related papers: Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation

Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation

URL: http://arxiv.org/abs/2306.11734v1
Date: Mon, 29 May 2023 09:28:34 GMT
Title: Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation
Authors: Qinglong Cao, Yuntian Chen, Chao Ma and Xiaokang Yang
Abstract summary: Few-shot aerial image segmentation is a challenging task that involves precisely parsing objects in query aerial images. The authors propose a novel few-shot rotation-invariant aerial semantic segmentation network (FRINet) Experiments demonstrate that FRINet achieves state-of-the-art performance in few-shot aerial semantic segmentation benchmark.
Score: 53.47160182465171
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Few-shot aerial image segmentation is a challenging task that involves precisely parsing objects in query aerial images with limited annotated support. Conventional matching methods without consideration of varying object orientations can fail to activate same-category objects with different orientations. Moreover, conventional algorithms can lead to false recognition of lower-scored rotated semantic objects. In response to these challenges, the authors propose a novel few-shot rotation-invariant aerial semantic segmentation network (FRINet). FRINet matches each query feature rotation-adaptively with orientation-varying yet category-consistent support information. The segmentation predictions from different orientations are supervised by the same label, and the backbones are pre-trained in the base category to boost segmentation performance. Experimental results demonstrate that FRINet achieves state-of-the-art performance in few-shot aerial semantic segmentation benchmark.

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