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- W4308238268 abstract "Co-saliency detection is a task to segment the occurring salient objects in a group of images. The biggest challenges are distracting objects in the background and ambiguity between the foreground and background. To handle these issues, we propose a novel superpixel group-correlation network (SGCN) architecture that uses a superpixel algorithm to obtain various component features from a group of images and creates a group-correlation matrix to detect the common components of those images. In this way, non-common objects can be effectively excluded from consideration, enabling a clear distinction between foreground and background. Our method outperforms current state-of-the-art methods on three popular benchmark datasets for co-saliency detection, and our extensive experiments thoroughly validate our claimed contributions." @default.
- W4308238268 created "2022-11-09" @default.
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- W4308238268 date "2022-10-16" @default.
- W4308238268 modified "2023-09-30" @default.
- W4308238268 title "Superpixel Group-Correlation Network for Co-Saliency Detection" @default.
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- W4308238268 doi "https://doi.org/10.1109/icip46576.2022.9897408" @default.
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