Related papers: Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation

Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation

URL: http://arxiv.org/abs/2010.12176v1
Date: Fri, 23 Oct 2020 05:40:53 GMT
Title: Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
Authors: Yuxi Li, Ning Xu, Jinlong Peng, John See, Weiyao Lin
Abstract summary: A cyclic mechanism is incorporated to the standard semi-supervised process to produce more robust representations. We introduce a simple gradient correction module, which extends the offline pipeline to an online method. Finally, we develop cycle effective receptive field (cycle-ERF) based on gradient correction to provide a new perspective into analyzing object-specific regions of interests.
Score: 37.3336313567187
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In this paper, we address several inadequacies of current video object segmentation pipelines. Firstly, a cyclic mechanism is incorporated to the standard semi-supervised process to produce more robust representations. By relying on the accurate reference mask in the starting frame, we show that the error propagation problem can be mitigated. Next, we introduce a simple gradient correction module, which extends the offline pipeline to an online method while maintaining the efficiency of the former. Finally we develop cycle effective receptive field (cycle-ERF) based on gradient correction to provide a new perspective into analyzing object-specific regions of interests. We conduct comprehensive experiments on challenging benchmarks of DAVIS17 and Youtube-VOS, demonstrating that the cyclic mechanism is beneficial to segmentation quality.

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