The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
Open accessAbstract
This work proposes a novel attentive graph neural network (AGNN) for zero-shot video object segmentation (ZVOS). The suggested AGNN recasts this task as a process of iterative information fusion over video graphs. Specifically, AGNN builds a fully connected graph to efficiently represent frames as nodes, and relations between arbitrary frame pairs as edges. The underlying pair-wise relations are described by a differentiable attention mechanism. Through parametric message passing, AGNN is able to efficiently capture and mine much richer and higher-order relations between video frames, thus enabling a more complete understanding of video content and more accurate foreground estimation. Experimental results on three video segmentation datasets show that AGNN sets a new state-of-the-art in each case. To further demonstrate the generalizability of our framework, we extend AGNN to an additional task: image object co-segmentation (IOCS). We perform experiments on two famous IOCS datasets and observe again the superiority of our AGNN model. The extensive experiments verify that AGNN is able to learn the underlying semantic/appearance relationships among video frames or related images, and discover the common objects.
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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Fully convolutional networks for semantic segmentation
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
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The graph neural network model
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Cited by
VideoMAC: Video Masked Autoencoders Meet ConvNets
Pei, G., Chen, T., Jiang, X., Liu, H., Sun, Z., & Yao, Y. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02145 · Cited by 23
“Recently, researchers have embarked on exploring MVM approaches [16, 19, 65, 66] to comprehend unsupervised video representations, exhibiting effectiveness across a multitude of downstream tasks [4, 25, 31, 32, 40–44, 60, 61, 71, 74, 75].”
BackgroundA central multimodal fusion framework for outdoor scene image segmentation
Zhang, Y., Morel, O., Seulin, R., Mériaudeau, F., & Sidibé, D. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10357-y · Cited by 5
Metadata
- Authors
- Wang, W., Lu, X., Shen, J., Crandall, D., & Shao, L.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 9235-9244
- OpenAlex
- W2987391422
- Cited by
- 303
- References
- 79
- FWCI
- 16.61
- Type
- conference paper
- Language
- en
- Open access
- green
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- Wang, W., Lu, X., Shen, J., Crandall, D., & Shao, L. (2019). Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2019.00933
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