This paper presents a novel framework called HST for semi-supervised video object segmentation (VOS). HST extracts image and video features using the latest Swin Transformer and Video Swin Transformer to inherit their inductive bias for the spatiotemporal locality, which is essential for temporally coherent VOS. To take full advantage of the image and video features, HST casts image and video features as a query and memory, respectively. By applying efficient memory read operations at multiple scales, HST produces hierarchical features for the precise reconstruction of object masks. HST shows effectiveness and robustness in handling challenging scenarios with occluded and fast-moving objects under cluttered backgrounds. In particular, HST-B outperforms the state-of-the-art competitors on multiple popular benchmarks, i.e., YouTube-VOS (85.0%), DAVIS 2017 (85.9%), and DAVIS 2016 (94.0%).

翻译：本文提出了一种名为HST的新型框架，用于半监督视频对象分割（VOS）。HST采用最新的Swin Transformer和Video Swin Transformer提取图像和视频特征，以继承其针对时空局部性的归纳偏置，这对时间一致性VOS至关重要。为充分利用图像和视频特征，HST分别将图像特征和视频特征视为查询和记忆。通过在多尺度上执行高效记忆读取操作，HST生成层级特征用于对象掩码的精确重建。HST在应对杂波背景下遮挡和快速移动物体的挑战性场景中展现出有效性和鲁棒性。特别地，HST-B在多个主流基准数据集上超越了现有最佳方法，即YouTube-VOS（85.0%）、DAVIS 2017（85.9%）和DAVIS 2016（94.0%）。