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Multi-Stream Attention-Aware Graph Convolution Network for Video Salient Object Detection by Mingzhu Xu; Ping Fu; Bing Liu; Junbao Li is a Computer Science article available to read on EtoBox.

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Recent advances in deep convolution neural networks (CNNs) boost the development of video salient object detection (SOD), and many remarkable deep-CNNs video SOD models have been proposed. However, many existing deep-CNNs video SOD models still suffer from coarse boundaries of the salient object, which may be attributed to the loss of high-frequency information. The traditional graph-based video SOD models can preserve object boundaries well by conducting superpixels/supervoxels segmentation in advance, but they perform weaker in highlighting the whole object than the latest deep-CNNs models, limited by heuristic graph clustering algorithms. To tackle this problem, we find a new way to address this issue under the framework of graph convolution networks (GCNs), taking advantage of graph model and deep neural network. Specifically, a superpixel-level spatiotemporal graph is first constructed among multiple frame-pairs by exploiting the motion cues implied in the frame-pairs. Then the graph data is imported into the devised multi-stream attention-aware GCN, where a novel Edge-Gated graph convolution (GC) operation is proposed to boost the saliency information aggregation on the graph

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Author

Mingzhu Xu; Ping Fu; Bing Liu; Junbao Li

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)