Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation
Qinglong Cao Yuntian Chen Chao Ma Xiaokang Yang
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.