Learning prototypes from background and latent objects for few-shot semantic segmentation

Few-shot semantic segmentation (FSS) aims to segment target object within a given image supported by few samples with pixel-level annotations. Existing FSS framework primarily focuses on target area for learning a target-object prototype while directly neglecting non-target clues. As such, the target-object prototype has not only to segment the target object but also to filter out non-target area simultaneously, resulting in numerous false positives. In this paper, we propose a background and latent-object prototype learning network (BLPLNet), which learns prototypes from not only the target area but also the non-target counterpart. From our perspective, the non-target area is delineated into background full of repeated textures and salient objects, refer to as latent objects in this paper. Specifically, a background mining module (BMM) is developed to specially learn a background prototype by episodic learning. The learned background prototype replaces the target-object one for background filtering, reducing the false positives. Moreover, a latent object mining module (LOMM), based on self-attention mechanism, works together with the BMM for learning multiple soft-orthogonal prototypes from latent objects. Then, the learned latent-object prototypes, which condense the general knowledge of objects, are used in a target object enhancement module (TOEM) to enhance the target-object prototype with the guidance of affinity-based scores. Extensive experiments on PASCAL-5${}^i$ and COCO-20${}^i$ datasets demonstrate the superiority of the BLPLNet, which outperforms state-of-the-art methods by an average of 0.60% on PASCAL-5${}^i$. Ablation studies validate the effectiveness of each component, and visualization results indicate that the learned latent-object prototypes indeed convey the general knowledge of objects.