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Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detection by Zhang, Wenbo; Fu, Keren; Wang, Zhuo; Ji, Ge-Peng; Zhao, Qijun is a scholarly article available to read on EtoBox.

What is Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detection about?

Recently CNN-based RGB-D salient object detection (SOD) has obtained significant improvement on detection accuracy. However, existing models often fail to perform well in terms of efficiency and accuracy simultaneously. This hinders their potential applications on mobile devices as well as many real-world problems. To bridge the accuracy gap between lightweight and large models for RGB-D SOD, in this paper, an efficient module that can greatly improve the accuracy but adds little computation is proposed. Inspired by the fact that depth quality is a key factor influencing the accuracy, we propose an efficient depth quality-inspired feature manipulation (DQFM) process, which can dynamically filter depth features according to depth quality. The proposed DQFM resorts to the alignment of low-level RGB and depth features, as well as holistic attention of the depth stream to explicitly control and enhance cross-modal fusion. We embed DQFM to obtain an efficient lightweight RGB-D SOD model called DFM-Net, where we in addition design a tailored depth backbone and a two-stage decoder as basic parts. Extensive experimental results on nine RGB-D datasets demonstrate that our DFM-Net outperform

Author

Zhang, Wenbo; Fu, Keren; Wang, Zhuo; Ji, Ge-Peng; Zhao, Qijun

Published

2022

Language

EN