  Automated salient object detection (SOD) plays an increasingly crucial role
in many computer vision applications. By reformulating the depth information as
supervision rather than as input, depth-supervised convolutional neural
networks (CNN) have achieved promising results on both RGB and RGB-D SOD
scenarios with the merits of no requirements for extra depth networks and depth
inputs in the inference stage. This paper, for the first time, seeks to expand
the applicability of depth supervision to the Transformer architecture.
Specifically, we develop a Depth-supervised Fusion TRansformer (DFTR), to
further improve the accuracy of both RGB and RGB-D SOD. The proposed DFTR
involves three primary features: 1) DFTR, to the best of our knowledge, is the
first pure Transformer-based model for depth-supervised SOD; 2) A multi-scale
feature aggregation (MFA) module is proposed to fully exploit the multi-scale
features encoded by the Swin Transformer in a coarse-to-fine manner; 3) To
enable bidirectional information flow across different streams of features, a
novel multi-stage feature fusion (MFF) module is further integrated into our
DFTR with the emphasis on salient regions at different network learning stages.
We extensively evaluate the proposed DFTR on ten benchmarking datasets.
Experimental results show that our DFTR consistently outperforms the existing
state-of-the-art methods for both RGB and RGB-D SOD tasks. The code and model
will be made publicly available.
