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• W2955084925 abstract "We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view. We emphasize the importance of inherent correlation among video frames and incorporate a global co-attention mechanism to improve further the state-of-the-art deep learning based solutions that primarily focus on learning discriminative foreground representations over appearance and motion in short-term temporal segments. The co-attention layers in our network provide efficient and competent stages for capturing global correlations and scene context by jointly computing and appending co-attention responses into a joint feature space. We train COSNet with pairs of video frames, which naturally augments training data and allows increased learning capacity. During the segmentation stage, the co-attention model encodes useful information by processing multiple reference frames together, which is leveraged to infer the frequently reappearing and salient foreground objects better. We propose a unified and end-to-end trainable framework where different co-attention variants can be derived for mining the rich context within videos. Our extensive experiments over three large benchmarks manifest that COSNet outperforms the current alternatives by a large margin." @default.
• W2955084925 created "2019-07-12" @default.
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• W2955084925 date "2019-06-01" @default.
• W2955084925 modified "2023-10-18" @default.
• W2955084925 title "See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks" @default.
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• W2955084925 doi "https://doi.org/10.1109/cvpr.2019.00374" @default.
• W2955084925 hasPublicationYear "2019" @default.
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