  Salient object detection (SOD) aims at finding the most salient objects in
images and outputs pixel-level binary masks. Transformer-based methods achieve
promising performance due to their global semantic understanding, crucial for
identifying salient objects. However, these models tend to be large and require
numerous training parameters. To better harness the potential of transformers
for SOD, we propose a novel parameter-efficient fine-tuning method aimed at
reducing the number of training parameters while enhancing the salient object
detection capability. Our model, termed EXternal Prompt features Enhanced
adapteR Tuning (ExPert), features an encoder-decoder structure with adapters
and injectors interspersed between the layers of a frozen transformer encoder.
The adapter modules adapt the pre-trained backbone to SOD while the injector
modules incorporate external prompt features to enhance the awareness of
salient objects. Comprehensive experiments demonstrate the superiority of our
method. Surpassing former state-of-the-art (SOTA) models across five SOD
datasets, ExPert achieves 0.215 mean absolute error (MAE) in ECSSD dataset with
80.2M trained parameters, 21% better than transformer-based SOTA model and 47%
better than CNN-based SOTA model.
