Exploring the Semi-supervised Video Object Segmentation Problem from a Cyclic Perspective

TL;DR

This paper introduces a cyclic workflow for semi-supervised video object segmentation that enhances consistency, mitigates error propagation, and provides interpretability, leading to improved robustness and segmentation quality.

Contribution

It proposes a cyclic mechanism and gradient correction module for semi-supervised VOS, offering a novel approach to improve accuracy and robustness while enabling better interpretability.

Findings

Enhanced segmentation consistency through cyclic mechanism

Reduced error propagation with reference mask reliance

Improved robustness and interpretability demonstrated on benchmarks

Abstract

Modern video object segmentation (VOS) algorithms have achieved remarkably high performance in a sequential processing order, while most of currently prevailing pipelines still show some obvious inadequacy like accumulative error, unknown robustness or lack of proper interpretation tools. In this paper, we place the semi-supervised video object segmentation problem into a cyclic workflow and find the defects above can be collectively addressed via the inherent cyclic property of semi-supervised VOS systems. Firstly, a cyclic mechanism incorporated to the standard sequential flow can produce more consistent representations for pixel-wise correspondance. Relying on the accurate reference mask in the starting frame, we show that the error propagation problem can be mitigated. Next, a simple gradient correction module, which naturally extends the offline cyclic pipeline to an online manner, can highlight the high-frequent and detailed part of results to further improve the segmentation quality while keeping feasible computation cost. Meanwhile such correction can protect the network from severe performance degration resulted from interference signals. Finally we develop cycle effective receptive field (cycle-ERF) based on gradient correction process to provide a new perspective into analyzing object-specific regions of interests. We conduct comprehensive comparison and detailed analysis on challenging benchmarks of DAVIS16, DAVIS17 and Youtube-VOS, demonstrating that the cyclic mechanism is helpful to enhance segmentation quality, improve the robustness of VOS systems, and further provide qualitative comparison and interpretation on how different VOS algorithms work. The code of this project can be found at https://github.com/lyxok1/STM-Training