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

arXiv: https://arxiv.org/abs/2010.12176 · HTML: https://mlsanity.com/paper/2010.12176/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Occluded Video Instance Segmentation: A Benchmark](https://mlsanity.com/paper/2102.01558/) (2021)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [Hierarchical Memory Matching Network for Video Object Segmentation](https://mlsanity.com/paper/2109.11404/) (2021)
- [Breaking the "Object" in Video Object Segmentation](https://mlsanity.com/paper/2212.06200/) (2022)
- [Reliable Propagation-Correction Modulation for Video Object Segmentation](https://mlsanity.com/paper/2112.02853/) (2021)
- [End-to-end video instance segmentation via spatial-temporal graph neural networks](https://mlsanity.com/paper/2203.03145/) (2022)
- [Tackling Background Distraction in Video Object Segmentation](https://mlsanity.com/paper/2207.06953/) (2022)
- [TransVOS: Video Object Segmentation with Transformers](https://mlsanity.com/paper/2106.00588/) (2021)
- [AIParsing: Anchor-free Instance-level Human Parsing](https://mlsanity.com/paper/2207.06854/) (2022)
- [MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation](https://mlsanity.com/paper/2111.14646/) (2021)
- [Exploring Cycle Consistency Learning in Interactive Volume Segmentation](https://mlsanity.com/paper/2303.06493/) (2023)
- [Hybrid Tracker with Pixel and Instance for Video Panoptic Segmentation](https://mlsanity.com/paper/2203.01217/) (2022)
- [Exploring the Semi-supervised Video Object Segmentation Problem from a Cyclic Perspective](https://mlsanity.com/paper/2111.01323/) (2021)
- [FlowVOS: Weakly-Supervised Visual Warping for Detail-Preserving and Temporally Consistent Single-Shot Video Object Segmentation](https://mlsanity.com/paper/2111.10621/) (2021)
- [Pixel-Level Equalized Matching for Video Object Segmentation](https://mlsanity.com/paper/2209.03139/) (2022)

## Abstract (arXiv)

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.