论文标题
自助少量射击语义分段
Self-Support Few-Shot Semantic Segmentation
论文作者
论文摘要
现有的少数射击分段方法基于支持 - 引物匹配框架取得了巨大进展。但是，他们仍然受到了所提供的几杆支撑的覆盖率有限的覆盖率。由简单的格式塔原理激励，即属于同一对象的像素比同一类的不同对象的像素更相似，我们提出了一种新颖的自支撑匹配策略来减轻此问题，该策略使用查询原型匹配查询特征，其中查询原型是从高连接质量查询查询疑问的预测中收集的。该策略可以有效地捕获查询对象的一致性基本特性，从而符合查询功能。我们还提出了一个自适应的自支持背景原型生成模块和自支撑损失，以进一步促进自支撑匹配程序。我们的自支撑网络大大提高了原型质量，从更强的骨架和更多的支持中提高了更多的改善，并在多个数据集上实现了SOTA。代码位于\ url {https://github.com/fanq15/ssp}。
Existing few-shot segmentation methods have achieved great progress based on the support-query matching framework. But they still heavily suffer from the limited coverage of intra-class variations from the few-shot supports provided. Motivated by the simple Gestalt principle that pixels belonging to the same object are more similar than those to different objects of same class, we propose a novel self-support matching strategy to alleviate this problem, which uses query prototypes to match query features, where the query prototypes are collected from high-confidence query predictions. This strategy can effectively capture the consistent underlying characteristics of the query objects, and thus fittingly match query features. We also propose an adaptive self-support background prototype generation module and self-support loss to further facilitate the self-support matching procedure. Our self-support network substantially improves the prototype quality, benefits more improvement from stronger backbones and more supports, and achieves SOTA on multiple datasets. Codes are at \url{https://github.com/fanq15/SSP}.
