.png){kind=icon}
.png){kind=icon}
Return
Local Memory Read-and-Comparator for Video Object Segmentation
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3201245.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recently, the memory-based approach, which performs non-local matching between previously segmented frames and a query frame, has led to significant improvement in video object segmentation. However, the positional proximity of the target objects between the query and the local memory (previous frame), i.e. temporal smoothness, is often neglected. There are some attempts to solve the problem, but they are vulnerable and sensitive to large movements of target objects. In this paper, we propose local memory read-and-compare operations to address the problem. First, we propose local memory read and sequential local memory read modules to explore temporal smoothness between neighboring frames. Second, we propose the memory comparator to read the global memory and local memory adaptively by comparing the affinities of the global and local memories. Experimental results demonstrate that the proposed algorithm yields more strict segmentation results than the recent state-of-the-art algorithms. For example, the proposed algorithm improves the video object segmentation performance by 0.4% and 0.5% in terms of J&F on the most commonly used datasets, DAVIS2016 and DAVIS2017, respectively.
Keywords:
Memory network
semi-supervised video object segmentation
video object segmentation
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W