While saliency detection on static images has been widely studied, the research on video saliency detection is still in an early stage and requires more efforts due to the challenge to bring both local and global consistency of salient objects into full consideration. In this article, we propose a novel dynamic saliency network based on both local consistency and global discriminations, via which semantic features across video frames are simultaneously extracted and a recurrent feature optimization structure is designed to further enhance its performances. To ensure that the generated dynamic salient map is more concentrated, we design a lightweight discriminator with a local consistency loss LC to identify subtle differences between predicted maps and ground truths. As a result, the proposed network can be further stimulated to produce more realistic saliency maps with smoother boundaries and simpler layer transitions. The added LC loss forces the network to pay more attention to the local consistency between continuous saliency maps. Both qualitative and quantitative experiments are carried out on three large datasets, and the results demonstrate that our proposed network not only achieves improved performances but also shows good robustness.

中文翻译：

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通过联合判别和局部一致性进行视频显着性预测


虽然静态图像的显着性检测已被广泛研究，但视频显着性检测的研究仍处于早期阶段，并且由于充分考虑显着性对象的局部和全局一致性的挑战，需要付出更多的努力。在本文中，我们提出了一种基于局部一致性和全局判别的新型动态显着性网络，通过该网络同时提取视频帧之间的语义特征，并设计了循环特征优化结构以进一步增强其性能。为了确保生成的动态显着图更加集中，我们设计了一个具有局部一致性损失LC的轻量级判别器，以识别预测图和地面实况之间的细微差异。因此，可以进一步刺激所提出的网络产生具有更平滑边界和更简单层过渡的更真实的显着性图。增加的 LC 损失迫使网络更加关注连续显着图之间的局部一致性。在三个大型数据集上进行了定性和定量实验，结果表明我们提出的网络不仅提高了性能，而且表现出良好的鲁棒性。