Learning to Explore Saliency for Stereoscopic Videos Via Component-Based Interaction
Learning to Explore Saliency for Stereoscopic Videos Via Component-Based Interaction
复制标题
学习通过基于组件的交互探索立体视频的显着性
DOI：
10.1109/tip.2020.2985531
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发表时间：
2020-04
影响因子：
10.6
通讯作者：
Jiang Jianmin
中科院分区：
文献类型：
--
作者：
Zhang Qiudan;Wang Xu;Wang Shiqi;Sun Zhenhao;Kwong Sam;Jiang Jianmin
In this paper, we devise a saliency prediction model for stereoscopic videos that learns to explore saliency inspired by the component-based interactions including spatial, temporal, as well as depth cues. The model first takes advantage of specific structure of 3D residual network (3D-ResNet) to model the saliency driven by spatio-temporal coherence from consecutive frames. Subsequently, the saliency inferred by implicit-depth is automatically derived based on the displacement correlation between left and right views by leveraging a deep convolutional network (ConvNet). Finally, a component-wise refinement network is devised to produce final saliency maps over time by aggregating saliency distributions obtained from multiple components. In order to further facilitate research towards stereoscopic video saliency, we create a new dataset including 175 stereoscopic video sequences with diverse content, as well as their dense eye fixation annotations. Extensive experiments support that our proposed model can achieve superior performance compared to the state-of-the-art methods on all publicly available eye fixation datasets.
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DOI：
10.1109/crv.2005.13
发表时间：
2005-05
期刊：
The 2nd Canadian Conference on Computer and Robot Vision (CRV'05)
影响因子：
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作者：
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通讯作者：
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影响因子：
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通讯作者：
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影响因子：
10.6
作者：
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通讯作者：
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发表时间：
2010-01
期刊：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1109/tpami.2018.2815601
发表时间：
2019-03-01
影响因子：
23.6
作者：
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通讯作者：
Durand, Fredo