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Guided Slot Attention for Unsupervised Video Object Segmentation

Minhyeok Lee · Suhwan Cho · Dogyoon Lee · Chaewon Park · Jungho Lee · Sangyoun Lee

Arch 4A-E Poster #352
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Wed 19 Jun 10:30 a.m. PDT — noon PDT


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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