Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance

TL;DR

This paper introduces an efficient unsupervised video object segmentation network that leverages motion guidance and multi-scale fusion to improve accuracy and reduce computational costs, validated on multiple datasets.

Contribution

The paper proposes a novel dual-stream network with motion guidance and multi-scale fusion for unsupervised video object segmentation, enhancing accuracy and efficiency.

Findings

Achieves superior accuracy on DAVIS 16, FBMS, and ViSal datasets.

Reduces computational complexity compared to existing methods.

Demonstrates robustness and effectiveness of the proposed approach.

Abstract

Due to the problem of performance constraints of unsupervised video object detection, its large-scale application is limited. In response to this pain point, we propose another excellent method to solve this problematic point. By incorporating motion characterization in unsupervised video object detection, detection accuracy is improved while reducing the computational amount of the network. The whole network structure consists of dual-stream network, motion guidance module, and multi-scale progressive fusion module. The appearance and motion representations of the detection target are obtained through a dual-stream network. Then, the semantic features of the motion representation are obtained through the local attention mechanism in the motion guidance module to obtain the high-level semantic features of the appearance representation. The multi-scale progressive fusion module then fuses the features of different deep semantic features in the dual-stream network further to improve the detection effect of the overall network. We have conducted numerous experiments on the three datasets of DAVIS 16, FBMS, and ViSal. The verification results show that the proposed method achieves superior accuracy and performance and proves the superiority and robustness of the algorithm.