  Existing salient object detection methods often adopt deeper and wider
networks for better performance, resulting in heavy computational burden and
slow inference speed. This inspires us to rethink saliency detection to achieve
a favorable balance between efficiency and accuracy. To this end, we design a
lightweight framework while maintaining satisfying competitive accuracy.
Specifically, we propose a novel trilateral decoder framework by decoupling the
U-shape structure into three complementary branches, which are devised to
confront the dilution of semantic context, loss of spatial structure and
absence of boundary detail, respectively. Along with the fusion of three
branches, the coarse segmentation results are gradually refined in structure
details and boundary quality. Without adding additional learnable parameters,
we further propose Scale-Adaptive Pooling Module to obtain multi-scale
receptive filed. In particular, on the premise of inheriting this framework, we
rethink the relationship among accuracy, parameters and speed via network
depth-width tradeoff. With these insightful considerations, we comprehensively
design shallower and narrower models to explore the maximum potential of
lightweight SOD. Our models are purposed for different application
environments: 1) a tiny version CTD-S (1.7M, 125FPS) for resource constrained
devices, 2) a fast version CTD-M (12.6M, 158FPS) for speed-demanding scenarios,
3) a standard version CTD-L (26.5M, 84FPS) for high-performance platforms.
Extensive experiments validate the superiority of our method, which achieves
better efficiency-accuracy balance across five benchmarks.
