Related papers: Guided Slot Attention for Unsupervised Video Object Segmentation

Guided Slot Attention for Unsupervised Video Object Segmentation

URL: http://arxiv.org/abs/2303.08314v3
Date: Sun, 31 Mar 2024 04:11:30 GMT
Title: Guided Slot Attention for Unsupervised Video Object Segmentation
Authors: Minhyeok Lee, Suhwan Cho, Dogyoon Lee, Chaewon Park, Jungho Lee, Sangyoun Lee,
Abstract summary: We propose a guided slot attention network to reinforce spatial structural information and obtain better foreground--background separation. The proposed model achieves state-of-the-art performance on two popular datasets.
Score: 16.69412563413671
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However, the existence of complex backgrounds and multiple foreground objects make this task challenging. To address this issue, we propose a guided slot attention network to reinforce spatial structural information and obtain better foreground--background separation. The foreground and background slots, which are initialized with query guidance, are iteratively refined based on interactions with template information. Furthermore, to improve slot--template interaction and effectively fuse global and local features in the target and reference frames, K-nearest neighbors filtering and a feature aggregation transformer are introduced. The proposed model achieves state-of-the-art performance on two popular datasets. Additionally, we demonstrate the robustness of the proposed model in challenging scenes through various comparative experiments.

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