Boosting Few-Shot Semantic Segmentation With Prior-Driven Edge Feature Enhancement Network

Few-shot semantic segmentation (FSS) focuses on segmenting objects of novel classes with only a small number of annotated samples and has achieved great development. However, compared with general semantic segmentation, inaccurate boundary predictions remain a serious problem in FSS. This is because, in scenarios with few samples, the extracted query features by the model struggle to contain sufficient detailed information to focus on the boundary of the target. To address this issue, we propose a prior-driven edge feature enhancement network (PDEFE) that utilizes the prior information of the object edges to enhance the query feature, thereby promoting the accurate segmentation of the target. Specifically, we first design an edge feature enhancement module (EFEM) that can utilize object edges to enhance the feature of the query object's boundaries. Furthermore, we also propose an edge prior mask generator (EPMG) to generate prior masks for edges based on the gradient information of the image, which can guide the model to pay more attention to the boundaries of the target in the query image. Extensive experiments on PASCAL-$5^{i}$ and COCO-$20^{i}$ demonstrate that PDEFE significantly improves upon two baseline detectors (up to 2.7$\sim$4.2% mIoU in average), achieving state-of-the-art performance.