Click to Correction: Interactive Bidirectional Dynamic Propagation Video Object Segmentation Network

High-quality video object segmentation is a challenging visual computing task. Interactive segmentation can improve segmentation results. This paper proposes a multi-round interactive dynamic propagation instance-level video object segmentation network based on click interaction. The network consists of two parts: a user interaction segmentation module and a bidirectional dynamic propagation module. A prior segmentation network was designed in the user interaction segmentation module to better segment objects of different scales that users click on. The dynamic propagation network achieves high-precision video object segmentation through the bidirectional propagation and fusion of segmentation masks obtained from multiple rounds of interaction. Experiments on interactive segmentation datasets and video object segmentation datasets show that our method achieves state-of-the-art segmentation results with fewer click interactions.

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Publication Details

Journal
Sensors
Published
2024-10-02
DOI
https://doi.org/10.3390/s24196405
Primary Topic
Visual Attention and Saliency Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Click to Correction: Interactive Bidirectional Dynamic Propagation Video Object Segmentation Network

Shuting Yang, Xia Yuan, Sihan Luo
Sensors
Visual Attention and Saliency Detection
article

Click to Correction: Interactive Bidirectional Dynamic Propagation Video Object Segmentation Network

Shuting Yang, Xia Yuan, Sihan Luo
article en

Abstract

High-quality video object segmentation is a challenging visual computing task. Interactive segmentation can improve segmentation results. This paper proposes a multi-round interactive dynamic propagation instance-level video object segmentation network based on click interaction. The network consists of two parts: a user interaction segmentation module and a bidirectional dynamic propagation module. A prior segmentation network was designed in the user interaction segmentation module to better segment objects of different scales that users click on. The dynamic propagation network achieves high-precision video object segmentation through the bidirectional propagation and fusion of segmentation masks obtained from multiple rounds of interaction. Experiments on interactive segmentation datasets and video object segmentation datasets show that our method achieves state-of-the-art segmentation results with fewer click interactions.

SensorsVol. 24(19)
Nanjing University of Science and Technology (CN), Ningxia Academy of Agriculture and Forestry Sciences (CN)
Openalex Percentile: Top 83%
Visual Attention and Saliency Detection
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