  Foreground segmentation is a fundamental problem in computer vision, which
includes salient object detection, forgery detection, defocus blur detection,
shadow detection, and camouflage object detection. Previous works have
typically relied on domain-specific solutions to address accuracy and
robustness issues in those applications. In this paper, we present a unified
framework for a number of foreground segmentation tasks without any
task-specific designs. We take inspiration from the widely-used pre-training
and then prompt tuning protocols in NLP and propose a new visual prompting
model, named Explicit Visual Prompting (EVP). Different from the previous
visual prompting which is typically a dataset-level implicit embedding, our key
insight is to enforce the tunable parameters focusing on the explicit visual
content from each individual image, i.e., the features from frozen patch
embeddings and high-frequency components. Our method freezes a pre-trained
model and then learns task-specific knowledge using a few extra parameters.
Despite introducing only a small number of tunable parameters, EVP achieves
superior performance than full fine-tuning and other parameter-efficient
fine-tuning methods. Experiments in fourteen datasets across five tasks show
the proposed method outperforms other task-specific methods while being
considerably simple. The proposed method demonstrates the scalability in
different architectures, pre-trained weights, and tasks. The code is available
at: https://github.com/NiFangBaAGe/Explicit-Visual-Prompt.
