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Learning effective feature representation for video object segmentation via memory
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AI
DOI：10.1016/j.knosys.2024.112020.png)
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Abstract
En 中文
To solve the problem of target discrimination being ignored in developing the feature of the current frame, this paper proposes the effective feature representation via memory (EFRM) method to form the effective and discriminative feature representation of the current frame from global and local perspectives by fully benefiting from the rich information contained in the memorized frames. First, the global discriminative feature, representing the differences between the foreground and background, is generated through the space- time memory read (STMR) with the nonlocal matching scheme. Second, the local discriminative feature, which has feature differences among targets that appear in the current frame, is generated by the designed per -object memory enhancement (PoME) by relying only on the diverse representations of the targets shown in the memorized frames. Finally, the segmentation of the current frame is generated based on the effective feature representation formed by the concatenation of global and local discriminative features. Evaluations on DAVIS 16, 17 and YouTube-VOS 18, 19 demonstrate the competitive performance of the proposed method.
Keywords:
Video object segmentation
Effective feature representation
Per-object memory enhancement
Quality-aware weight assessment
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W