Better object segmentation in video by using only high quality memorized frames with QDMN
Better object segmentation in video by using only high quality memorized frames with QDMN
Learning Quality-aware Dynamic Memory for Video Object Segmentation
arXiv paper abstract https://arxiv.org/abs/2207.07922v1
arXiv PDF paper https://arxiv.org/pdf/2207.07922v1.pdf
... several spatial-temporal memory-based methods have verified that storing intermediate frames and their masks as memory are helpful to segment target objects in videos.
However, they ... focus on ... matching between the current ... and ... memory frames without ... paying attention to the quality of the memory.
Therefore, frames with poor segmentation masks are prone to be memorized, which leads to a segmentation mask error accumulation problem
... propose a Quality-aware Dynamic Memory Network (QDMN) to evaluate the segmentation quality of each frame, allowing the memory bank to selectively store accurately segmented frames to prevent the error accumulation problem.
... QDMN achieves new state-of-the-art performance on both DAVIS and YouTube-VOS benchmarks ...
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