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SwiftNet: Real-time Video Object Segmentation

2021-02-09 02:22:48
Haochen Wang, Xiaolong Jiang, Haibing Ren, Yao Hu, Song Bai
arXiv_CV
     
Abstract

In this work we present SwiftNet for real-time semi-supervised video object segmentation (one-shot VOS), which reports 77.8% J&F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance. We achieve this by elaborately compressing spatiotemporal redundancy in matching-based VOS via Pixel-Adaptive Memory (PAM). Temporally, PAM adaptively triggers memory updates on frames where objects display noteworthy inter-frame variations. Spatially, PAM selectively performs memory update and match on dynamic pixels while ignoring the static ones, significantly reducing redundant computations wasted on segmentation-irrelevant pixels. To promote efficient reference encoding, light-aggregation encoder is also introduced in SwiftNet deploying reversed sub-pixel. We hope SwiftNet could set a strong and efficient baseline for real-time VOS and facilitate its application in mobile vision.

Abstract (translated)

URL

https://arxiv.org/abs/2102.04604

PDF

https://arxiv.org/pdf/2102.04604.pdf

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