3D fully convolutional networks (FCN), which jointly leverage the spatial and temporal cues, have achieved great success in video saliency prediction. However, they still have limitations in some challenging cases, e.g. fixation shift. To address this issue, we propose a SpatioTemporally Attentive 3D Network (STA3D) to selectively propagate the significant temporal features and refine the spatial features in 3D FCN for video saliency prediction. Extensive experiments on three standard datasets demonstrate the superiority of the proposed model against the state-of-the-art.

中文翻译：

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STA3D：时空专注的3D网络，用于视频显着性预测

共同利用时空线索的3D全卷积网络（FCN）在视频显着性预测中取得了巨大的成功。但是，它们在某些具有挑战性的情况下仍具有局限性，例如注视移位。为解决此问题，我们提出了一个时空时空专注3D网络（STA3D），以选择性地传播重要的时间特征并完善3D FCN中的空间特征，以进行视频显着性预测。在三个标准数据集上的大量实验证明了所提出的模型相对于最新技术的优越性。