  In this paper, we propose a simple yet effective approach, named Triple
Excitation Network, to reinforce the training of video salient object detection
(VSOD) from three aspects, spatial, temporal, and online excitations. These
excitation mechanisms are designed following the spirit of curriculum learning
and aim to reduce learning ambiguities at the beginning of training by
selectively exciting feature activations using ground truth. Then we gradually
reduce the weight of ground truth excitations by a curriculum rate and replace
it by a curriculum complementary map for better and faster convergence. In
particular, the spatial excitation strengthens feature activations for clear
object boundaries, while the temporal excitation imposes motions to emphasize
spatio-temporal salient regions. Spatial and temporal excitations can combat
the saliency shifting problem and conflict between spatial and temporal
features of VSOD. Furthermore, our semi-curriculum learning design enables the
first online refinement strategy for VSOD, which allows exciting and boosting
saliency responses during testing without re-training. The proposed triple
excitations can easily plug in different VSOD methods. Extensive experiments
show the effectiveness of all three excitation methods and the proposed method
outperforms state-of-the-art image and video salient object detection methods.
