  Existing salient object detection (SOD) methods mainly rely on U-shaped
convolution neural networks (CNNs) with skip connections to combine the global
contexts and local spatial details that are crucial for locating salient
objects and refining object details, respectively. Despite great successes, the
ability of CNNs in learning global contexts is limited. Recently, the vision
transformer has achieved revolutionary progress in computer vision owing to its
powerful modeling of global dependencies. However, directly applying the
transformer to SOD is suboptimal because the transformer lacks the ability to
learn local spatial representations. To this end, this paper explores the
combination of transformers and CNNs to learn both global and local
representations for SOD. We propose a transformer-based Asymmetric Bilateral
U-Net (ABiU-Net). The asymmetric bilateral encoder has a transformer path and a
lightweight CNN path, where the two paths communicate at each encoder stage to
learn complementary global contexts and local spatial details, respectively.
The asymmetric bilateral decoder also consists of two paths to process features
from the transformer and CNN encoder paths, with communication at each decoder
stage for decoding coarse salient object locations and fine-grained object
details, respectively. Such communication between the two encoder/decoder paths
enables AbiU-Net to learn complementary global and local representations,
taking advantage of the natural merits of transformers and CNNs, respectively.
Hence, ABiU-Net provides a new perspective for transformer-based SOD. Extensive
experiments demonstrate that ABiU-Net performs favorably against previous
state-of-the-art SOD methods. The code is available at
https://github.com/yuqiuyuqiu/ABiU-Net.
