Handle object segmentation in long videos using working and long-term memories with XMem

Handle object segmentation in long videos using working and long-term memories with XMem

XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model

arXiv paper abstract https://arxiv.org/abs/2207.07115

arXiv PDF paper https://arxiv.org/pdf/2207.07115.pdf

GitHub https://github.com/hkchengrex/XMem

Project page https://hkchengrex.github.io/XMem

... present XMem, a video object segmentation architecture for long videos with unified feature memory stores inspired by the Atkinson-Shiffrin memory model.

Prior work on video object segmentation typically only uses one type of feature memory.

For videos longer than a minute, a single feature memory model tightly links memory consumption and accuracy.

... develop an architecture that incorporates multiple independent yet deeply-connected feature memory stores: a rapidly updated sensory memory, a high-resolution working memory, and a compact thus sustained long-term memory.

... consolidates actively used working memory elements into the long-term memory, which avoids memory explosion and minimizes performance decay for long-term prediction.

... XMem greatly exceeds state-of-the-art performance on long-video datasets while being on par with state-of-the-art methods (that do not work on long videos) on short-video datasets ...

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