  With the rapid development of deep learning techniques, image saliency deep
models trained solely by spatial information have occasionally achieved
detection performance for video data comparable to that of the models trained
by both spatial and temporal information. However, due to the lesser
consideration of temporal information, the image saliency deep models may
become fragile in the video sequences dominated by temporal information. Thus,
the most recent video saliency detection approaches have adopted the network
architecture starting with a spatial deep model that is followed by an
elaborately designed temporal deep model. However, such methods easily
encounter the performance bottleneck arising from the single stream learning
methodology, so the overall detection performance is largely determined by the
spatial deep model. In sharp contrast to the current mainstream methods, this
paper proposes a novel plug-and-play scheme to weakly retrain a pretrained
image saliency deep model for video data by using the newly sensed and coded
temporal information. Thus, the retrained image saliency deep model will be
able to maintain temporal saliency awareness, achieving much improved detection
performance. Moreover, our method is simple yet effective for adapting any
off-the-shelf pre-trained image saliency deep model to obtain high-quality
video saliency detection. Additionally, both the data and source code of our
method are publicly available.
