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Research Article | Open Access

Full-duplex strategy for video object segmentation

School of Computer Science, Wuhan University, Wuhan, China
Computer Vision Lab, ETH Zürich, ETF C113.2, Sternwartstrasse 7, 8092 Zürich, Switzerland
College of Computer Science, Sichuan University, Chengdu, China
Peng Cheng Laboratory, Shenzhen, China
School of Computer Science, Beijing Institute of Technology, Beijing, China
Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates
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Graphical Abstract

Abstract

Previous video object segmentation appro-aches mainly focus on simplex solutions linking appearanceand motion, limiting effective feature collaboration between these two cues. In this work, we study anovel and efficient full-duplex strategy network (FSNet) to address this issue, by considering a better mutual restraint scheme linking motion and appearance allowing exploitation of cross-modal features from the fusion and decoding stage. Specifically, we introduce a relational cross-attention module (RCAM) to achieve bidirectional message propagation across embedding sub-spaces. To improve the model’s robustness and update inconsistent features from the spatiotemporal embeddings, we adopt a bidirectional purification module after the RCAM. Extensive experiments on five popular benchmarks show that our FSNet is robust to various challenging scenarios (e.g., motion blur and occlusion), and compares well to leading methods both for video object segmentation and video salient object detection. The project is publicly available at https://github.com/GewelsJI/FSNet.

Keywords

video object segmentation (VOS)video salient object detection (V-SOD)visual attention

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Computational Visual Media
Volume 9 Issue 1,
March 2023
Pages 155-175
DOI: 10.1007/s41095-021-0262-4
Cite this article:
Ji G-P, Fan D-P, Fu K, et al. Full-duplex strategy for video object segmentation. Computational Visual Media, 2023, 9(1): 155-175. https://doi.org/10.1007/s41095-021-0262-4

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Received: 01 September 2021
Accepted: 16 October 2021
Published: 18 October 2022
© The Author(s) 2022.

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