COMPrompter:reconceptualized segment anything model with multiprompt network for camouflaged object detection  

作　　者：Xiaoqin ZHANG Zhenni YU Li ZHAO Deng-Ping FAN Guobao XIAO 

机构地区：[1]Zhejiang Province Key Laboratory of Intelligent Informatics for Safety and Emergency,Wenzhou University,Wenzhou 325035,China [2]Nankai International Advanced Research Institute(SHENZHEN FUTIAN),Shenzhen 518045,China [3]College of Computer Science,Nankai University,Tianjin 300071,China [4]School of Computer Science and Technology,Tongji University,Shanghai 201804,China

出　　处：《Science China(Information Sciences)》2025年第1期186-200,共15页中国科学(信息科学)(英文版)

基　　金：supported in part by National Natural Science Foundation of China(Grant Nos.U2033210,U24A20242,62101387,62475241);Zhejiang Provincial Natural Science Foundation(Grant No.LDT23F02024F02).

摘　　要：We rethink the segment anything model(SAM)and propose a novel multiprompt network called COMPrompter for camouflaged object detection(COD).SAM has zero-shot generalization ability beyond other models and can provide an ideal framework for COD.Our network aims to enhance the single prompt strategy in SAM to a multiprompt strategy.To achieve this,we propose an edge gradient extraction module,which generates a mask containing gradient information regarding the boundaries of camouflaged objects.This gradient mask is then used as a novel boundary prompt,enhancing the segmentation process.Thereafter,we design a box-boundary mutual guidance module,which fosters more precise and comprehensive feature extraction via mutual guidance between a boundary prompt and a box prompt.This collaboration enhances the model’s ability to accurately detect camouflaged objects.Moreover,we employ the discrete wavelet transform to extract high-frequency features from image embeddings.The high-frequency features serve as a supplementary component to the multiprompt system.Finally,our COMPrompter guides the network to achieve enhanced segmentation results,thereby advancing the development of SAM in terms of COD.Experimental results across COD benchmarks demonstrate that COMPrompter achieves a cutting-edge performance,surpassing the current leading model by an average positive metric of 2.2%in COD10K.In the specific application of COD,the experimental results in polyp segmentation show that our model is superior to top-tier methods as well.The code will be made available at https://github.com/guobaoxiao/COMPrompter.

关 键 词：segment anything model camouflaged object detection BOUNDARY PROMPT 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]