  Video salient object detection aims to find the most visually distinctive
objects in a video. To explore the temporal dependencies, existing methods
usually resort to recurrent neural networks or optical flow. However, these
approaches require high computational cost, and tend to accumulate inaccuracies
over time. In this paper, we propose a network with attention modules to learn
contrastive features for video salient object detection without the high
computational temporal modeling techniques. We develop a non-local
self-attention scheme to capture the global information in the video frame. A
co-attention formulation is utilized to combine the low-level and high-level
features. We further apply the contrastive learning to improve the feature
representations, where foreground region pairs from the same video are pulled
together, and foreground-background region pairs are pushed away in the latent
space. The intra-frame contrastive loss helps separate the foreground and
background features, and the inter-frame contrastive loss improves the temporal
consistency. We conduct extensive experiments on several benchmark datasets for
video salient object detection and unsupervised video object segmentation, and
show that the proposed method requires less computation, and performs favorably
against the state-of-the-art approaches.
