Per-Clip Video Object Segmentation

• URL: http://arxiv.org/abs/2208.01924v1
• Date: Wed, 3 Aug 2022 09:02:29 GMT
• Title: Per-Clip Video Object Segmentation
• Authors: Kwanyong Park, Sanghyun Woo, Seoung Wug Oh, In So Kweon, Joon-Young Lee
• Abstract summary: Recently, memory-based approaches show promising results on semisupervised video object segmentation. We treat video object segmentation as clip-wise mask-wise propagation. We propose a new method tailored for the per-clip inference.
• Score: 110.08925274049409
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, memory-based approaches show promising results on semi-supervised video object segmentation. These methods predict object masks frame-by-frame with the help of frequently updated memory of the previous mask. Different from this per-frame inference, we investigate an alternative perspective by treating video object segmentation as clip-wise mask propagation. In this per-clip inference scheme, we update the memory with an interval and simultaneously process a set of consecutive frames (i.e. clip) between the memory updates. The scheme provides two potential benefits: accuracy gain by clip-level optimization and efficiency gain by parallel computation of multiple frames. To this end, we propose a new method tailored for the per-clip inference. Specifically, we first introduce a clip-wise operation to refine the features based on intra-clip correlation. In addition, we employ a progressive matching mechanism for efficient information-passing within a clip. With the synergy of two modules and a newly proposed per-clip based training, our network achieves state-of-the-art performance on Youtube-VOS 2018/2019 val (84.6% and 84.6%) and DAVIS 2016/2017 val (91.9% and 86.1%). Furthermore, our model shows a great speed-accuracy trade-off with varying memory update intervals, which leads to huge flexibility.

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