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- W2974188250 abstract "Recently, video object segmentation (VOS) is a new challenging research direction from DAVIS competition. Carrying on with these researches, we propose a visual attention guided framework in video object segmentation, which includes four main components: segmentation network, visual encoder, spatial encoder and guide. The segmentation network predicts the object mask in the current video frame, and the visual guide force segmentation network to focus on the annotated object by visual information from visual encoder, and the spatial guide provide spatial location by spatial encoder from previous frame. Visual attention mechanism plays an important role in the model on capturing annotated object without online fine-tuning as previous models. This approach has an advantage over previous methods on accuracy and efficiency, especially avoid the online fine-tuning in those one-shot learning approaches." @default.
- W2974188250 created "2019-09-26" @default.
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- W2974188250 date "2019-06-01" @default.
- W2974188250 modified "2023-09-25" @default.
- W2974188250 title "Visual Attention Guided Video Object Segmentation" @default.
- W2974188250 doi "https://doi.org/10.1109/iciea.2019.8834292" @default.
- W2974188250 hasPublicationYear "2019" @default.
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