Explicit Visual Prompting for Low-Level Structure Segmentations

1 University of Macau     2 Tencent AI Lab    
CVPR 2023


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.


We propose a unified method for four low-level structure segmentation tasks: camouflaged object, forgery, shadow and defocus blur detection (left). Our approach relies on a pre-trained frozen transformer backbone that leverages explicit extracted features, e.g., the frozen embedded features and high-frequency components, to prompt knowledge (right).


We remodulate the features via the Embedding Tune and the HFC Tune. The Adaptor is designed to efficiently obtain prompts.



	  title={Explicit Visual Prompting for Low-Level Structure Segmentations},
	  author={Liu, Weihuang and Shen, Xi and Pun, Chi-Man and Cun, Xiaodong},