  We consider the generic problem of detecting low-level structures in images,
which includes segmenting the manipulated parts, identifying out-of-focus
pixels, separating shadow regions, and detecting concealed objects. Whereas
each such topic has been typically addressed with a domain-specific solution,
we show that a unified approach performs well across all of them. 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 the input's
high-frequency components. The proposed EVP significantly outperforms other
parameter-efficient tuning protocols under the same amount of tunable
parameters (5.7% extra trainable parameters of each task). EVP also achieves
state-of-the-art performances on diverse low-level structure segmentation tasks
compared to task-specific solutions. Our code is available at:
https://github.com/NiFangBaAGe/Explicit-Visual-Prompt.
