  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.
