  While previous CNN-based models have exhibited promising results for salient
object detection (SOD), their ability to explore global long-range dependencies
is restricted. Our previous work, the Visual Saliency Transformer (VST),
addressed this constraint from a transformer-based sequence-to-sequence
perspective, to unify RGB and RGB-D SOD. In VST, we developed a multi-task
transformer decoder that concurrently predicts saliency and boundary outcomes
in a pure transformer architecture. Moreover, we introduced a novel token
upsampling method called reverse T2T for predicting a high-resolution saliency
map effortlessly within transformer-based structures. Building upon the VST
model, we further propose an efficient and stronger VST version in this work,
i.e. VST++. To mitigate the computational costs of the VST model, we propose a
Select-Integrate Attention (SIA) module, partitioning foreground into
fine-grained segments and aggregating background information into a single
coarse-grained token. To incorporate 3D depth information with low cost, we
design a novel depth position encoding method tailored for depth maps.
Furthermore, we introduce a token-supervised prediction loss to provide
straightforward guidance for the task-related tokens. We evaluate our VST++
model across various transformer-based backbones on RGB, RGB-D, and RGB-T SOD
benchmark datasets. Experimental results show that our model outperforms
existing methods while achieving a 25% reduction in computational costs without
significant performance compromise. The demonstrated strong ability for
generalization, enhanced performance, and heightened efficiency of our VST++
model highlight its potential.
