Semi-supervised video object segmentation (VOS) aims to track the designated
objects present in the initial frame of a video at the pixel level. To fully
exploit the appearance information of an object, pixel-level feature matching
is widely used in VOS. Conventional feature matching runs in a surjective
manner, i.e., only the best matches from the query frame to the reference frame
are considered. Each location in the query frame refers to the optimal location
in the reference frame regardless of how often each reference frame location is
referenced. This works well in most cases and is robust against rapid
appearance variations, but may cause critical errors when the query frame
contains background distractors that look similar to the target object. To
mitigate this concern, we introduce a bijective matching mechanism to find the
best matches from the query frame to the reference frame and vice versa. Before
finding the best matches for the query frame pixels, the optimal matches for
the reference frame pixels are first considered to prevent each reference frame
pixel from being overly referenced. As this mechanism operates in a strict
manner, i.e., pixels are connected if and only if they are the sure matches for
each other, it can effectively eliminate background distractors. In addition,
we propose a mask embedding module to improve the existing mask propagation
method. By embedding multiple historic masks with coordinate information, it
can effectively capture the position information of a target object.

该研究提出了一种双射匹配机制，可用于解决像素级特征匹配中可能出现的关键问题，同时还提出了一种面向历史掩模的嵌入模块，以提高现有掩模传播方法的效果。