Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Xuehao WangHong Qin

Abstract

Existing RGB-D salient object detection methods treat depth information as an independent component to complement RGB and widely follow the bistream parallel network architecture. To selectively fuse the CNN features extracted from both RGB and depth as a final result, the state-of-the-art (SOTA) bistream networks usually consist of two independent subbranches: one subbranch is used for RGB saliency, and the other aims for depth saliency. However, depth saliency is persistently inferior to the RGB saliency because the RGB component is intrinsically more informative than the depth component. The bistream architecture easily biases its subsequent fusion procedure to the RGB subbranch, leading to a performance bottleneck. In this paper, we propose a novel data-level recombination strategy to fuse RGB with D (depth) before deep feature extraction, where we cyclically convert the original 4-dimensional RGB-D into DGB, RDB and RGD. Then, a newly lightweight designed triple-stream network is applied over these novel formulated data to achieve an optimal channel-wise complementary fusion status between the RGB and D, achieving a new SOTA performance.

Citations

Jan 13, 2021·Computational Visual Media·Tao ZhouLing Shao
Sep 9, 2021·Biomedical Signal Processing and Control·Sagar Deep DebPrem S Tripathi

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Methods Mentioned

BETA
Feature Fusion
RGB-D

Software Mentioned

CPFP
Stream
SOTA
Tri
TANet
SSD
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Kinect
NJUDS
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