论文标题
带有自适应功能库和不确定区域改进的视频对象细分
Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
论文作者
论文摘要
我们为半监督视频对象细分（VOS）提出了一个新的基于匹配的框架。最近，通过基于匹配的算法来实现最先进的VOS性能，其中创建了功能库来存储用于区域匹配和分类的功能。但是，如何有效地组织不断增长的功能银行中的信息仍然不足，这导致银行的设计效率低下。我们引入了一种自适应功能银行更新方案，以动态吸收新功能并丢弃过时功能。我们还设计了新的置信度损失和细粒细分模块，以提高不确定区域的分割精度。在公共基准上，我们的算法胜过现有的最先进的。
We propose a new matching-based framework for semi-supervised video object segmentation (VOS). Recently, state-of-the-art VOS performance has been achieved by matching-based algorithms, in which feature banks are created to store features for region matching and classification. However, how to effectively organize information in the continuously growing feature bank remains under-explored, and this leads to inefficient design of the bank. We introduce an adaptive feature bank update scheme to dynamically absorb new features and discard obsolete features. We also design a new confidence loss and a fine-grained segmentation module to enhance the segmentation accuracy in uncertain regions. On public benchmarks, our algorithm outperforms existing state-of-the-arts.
