Significant performance improvement has been achieved for fully-supervised
video salient object detection with the pixel-wise labeled training datasets,
which are time-consuming and expensive to obtain. To relieve the burden of data
annotation, we present the first weakly supervised video salient object
detection model based on relabeled "fixation guided scribble annotations".
Specifically, an "Appearance-motion fusion module" and bidirectional ConvLSTM
based framework are proposed to achieve effective multi-modal learning and
long-term temporal context modeling based on our new weak annotations. Further,
we design a novel foreground-background similarity loss to further explore the
labeling similarity across frames. A weak annotation boosting strategy is also
introduced to boost our model performance with a new pseudo-label generation
technique. Extensive experimental results on six benchmark video saliency
detection datasets illustrate the effectiveness of our solution.

本文提出了基于新型弱标注的视频显著目标检测模型，并使用外观运动融合模块和双向 ConvLSTM 框架来实现有效的多模态学习和长期时序建模，还设计了一种前景背景相似度损失和一种弱标注增强策略，以提高模型性能和伪标签生成技术。在六个基准视频显著性检测数据集上的实验结果证明了我们方案的有效性。