Explicit visual prompting for universal foreground segmentations

W Liu, X Shen, CM Pun, X Cun - arXiv preprint arXiv:2305.18476, 2023 - arxiv.org
arXiv preprint arXiv:2305.18476, 2023arxiv.org
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 …
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.
arxiv.org
以上显示的是最相近的搜索结果。 查看全部搜索结果

Google学术搜索按钮

example.edu/paper.pdf
搜索
获取 PDF 文件
引用
References