  Existing studies on salient object detection (SOD) focus on extracting
distinct objects with edge information and aggregating multi-level features to
improve SOD performance. To achieve satisfactory performance, the methods
employ refined edge information and low multi-level discrepancy. However, both
performance gain and computational efficiency cannot be attained, which has
motivated us to study the inefficiencies in existing encoder-decoder structures
to avoid this trade-off. We propose TRACER, which detects salient objects with
explicit edges by incorporating attention guided tracing modules. We employ a
masked edge attention module at the end of the first encoder using a fast
Fourier transform to propagate the refined edge information to the downstream
feature extraction. In the multi-level aggregation phase, the union attention
module identifies the complementary channel and important spatial information.
To improve the decoder performance and computational efficiency, we minimize
the decoder block usage with object attention module. This module extracts
undetected objects and edge information from refined channels and spatial
representations. Subsequently, we propose an adaptive pixel intensity loss
function to deal with the relatively important pixels unlike conventional loss
functions which treat all pixels equally. A comparison with 13 existing methods
reveals that TRACER achieves state-of-the-art performance on five benchmark
datasets. We have released TRACER at https://github.com/Karel911/TRACER.

Computer Vision and Pattern Recognition