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Article overview
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Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection | Xuehao Wang
; Shuai Li
; Chenglizhao Chen
; Yuming Fang
; Aimin Hao
; Hong Qin
; | Date: |
7 Aug 2020 | Abstract: | Existing RGB-D salient object detection methods treat depth information as an
independent component to complement its RGB part, and widely follow the
bi-stream parallel network architecture. To selectively fuse the CNNs features
extracted from both RGB and depth as a final result, the state-of-the-art
(SOTA) bi-stream networks usually consist of two independent subbranches; i.e.,
one subbranch is used for RGB saliency and the other aims for depth saliency.
However, its depth saliency is persistently inferior to the RGB saliency
because the RGB component is intrinsically more informative than the depth
component. The bi-stream 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 extbf{D}GB, R extbf{D}B and RG extbf{D}.
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. | Source: | arXiv, 2009.05102 | Services: | Forum | Review | PDF | Favorites |
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