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%).

本文提出了一种名为 HST 的半监督视频对象分割框架，使用 Swin Transformer 和 Video Swin Transformer 提取图像和视频特征，并使用内存读取操作产生层次特征以精确重构对象蒙版，该框架在处理复杂场景下具有有效性和鲁棒性，尤其是 HST-B 在多个流行基准测试中优于现有技术。