Harmonic Feature Activation for Few-Shot Semantic Segmentation

Few-shot semantic segmentation remains an open problem because limited support (training) images are insufficient to represent the diverse semantics within target categories. Conventional methods typically model a target category solely using information from the support image(s), resulting in incomplete semantic activation. In this paper, we propose a novel few-shot segmentation approach, termed harmonic feature activation (HFA), with the aim to implement dense support-to-query semantic transform by incorporating the features of both query and support images. HFA is formulated as a bilinear model, which takes charge of the pixel-wise dense correlation (bilinear feature activation) between query and support images in a systematic way. HFA incorporates a low-rank decomposition procedure, which speeds up bilinear feature activation with negligible performance cost. In addition, a semantic diffusion procedure is fused with HFA, which further improves the global harmony and local consistency of the feature activation. Extensive experiments on commonly used datasets (PASCAL VOC and MS COCO) show that HFA improves the state-of-the-arts with significant margins. Code is available at https://github.com/Bibikiller/HFA.