论文标题
原型混合模型，用于少量的语义分割
Prototype Mixture Models for Few-shot Semantic Segmentation
论文作者
论文摘要
很少有射击分割具有挑战性，因为支持和查询图像中的对象在外观和姿势上可能会有显着差异。使用直接从支持图像获取的单个原型来分割查询图像会引起语义歧义。在本文中，我们提出了原型混合模型（PMMS），该模型将各种图像区域与多个原型相关联，以实现基于原型的语义表示。通过预期最大化算法估计，PMMS结合了有限的支持图像中丰富的通道和空间语义。 PMM被用作表示和分类器，完全利用语义来激活查询图像中的对象，同时以双面的方式抑制背景区域。对Pascal VOC和MS-Coco数据集进行了广泛的实验表明，PMM可显着改善最先进的方法。特别是，PMMS在MS-Coco上的5-Shot分割性能提高了5.82 \％，仅用于模型尺寸和推理速度的中等成本。
Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose. Using a single prototype acquired directly from the support image to segment the query image causes semantic ambiguity. In this paper, we propose prototype mixture models (PMMs), which correlate diverse image regions with multiple prototypes to enforce the prototype-based semantic representation. Estimated by an Expectation-Maximization algorithm, PMMs incorporate rich channel-wised and spatial semantics from limited support images. Utilized as representations as well as classifiers, PMMs fully leverage the semantics to activate objects in the query image while depressing background regions in a duplex manner. Extensive experiments on Pascal VOC and MS-COCO datasets show that PMMs significantly improve upon state-of-the-arts. Particularly, PMMs improve 5-shot segmentation performance on MS-COCO by up to 5.82\% with only a moderate cost for model size and inference speed.
