The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks
Open accessAbstract
We introduce a novel network, called as 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. We will publicly release our implementation and models.
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Metadata
- Authors
- Lu, X., Wang, W., Ma, C., Shen, J., Shao, L., & Porikli, F.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 3618-3627
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2955084925
- Cited by
- 521
- References
- 73
- FWCI
- 27.32
- Type
- conference paper
- Language
- en
- Open access
- green
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- Lu, X., Wang, W., Ma, C., Shen, J., Shao, L., & Porikli, F. (2019). See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00374
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