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

By Yuxi Li, Ning Xu, Jinlong Peng, John See, Weiyao Lin. 2020

32 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

Occluded Video Instance Segmentation: A Benchmark (2021, background): “According to whether to provide the mask for the first frame, VOS can be divided into semi-supervised and unsupervised scenarios.”
A Survey on Deep Learning Technique for Video Segmentation (2021, unknown): “EGMN [100] ECCV Memory Network Matching-based MSRA10K [84] + COCO [123] + DAVIS17 [81] + YouTube-VOS [95] STM-Cycle [166] NeurIPS Memory Network Matching-based DAVIS17 [81] + YouTube-VOS [95]”