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

Science China(Information Sciences) 2025年第1期68卷 189-203页

作者： Xiaoqin ZHANG Zhenni YU Li ZHAO deng-ping fan Guobao XIAO Zhejiang Province Key Laboratory of Intelligent Informatics for Safety and Emergency Wenzhou University Nankai International Advanced Research Institute (SHENZHEN FUTIAN) College of Computer Science Nankai University School of Computer Science and Technology Tongji University

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 ***, 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://***/guobaoxiao/COMPrompter.

关键词： segment anything model camouflaged object detection boundary prompt

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Specificity-preserving RGB-D saliency detection

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Computational Visual Media 2023年第2期9卷 297-317页

作者： Tao Zhou deng-ping fan Geng Chen Yi Zhou Huazhu Fu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina Computer Vision Lab ETH Z¨urichZ¨urichSwitzerland School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’anChina School of Computer Science and Engineering Southeast UniversityNanjingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Salient object detection(SOD)in RGB and depth images has attracted increasing research *** RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities,while few methods explicitly consider how to preserve modality-specific *** this study,we propose a novel framework,the specificity-preserving network(SPNet),which improves SOD performance by exploring both the shared information and modality-specific ***,we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction *** effectively fuse cross-modal features in the shared learning network,we propose a cross-enhanced integration module(CIM)and propagate the fused feature to the next layer to integrate cross-level ***,to capture rich complementary multi-modal information to boost SOD performance,we use a multi-modal feature aggregation(MFA)module to integrate the modalityspecific features from each individual decoder into the shared *** using skip connections between encoder and decoder layers,hierarchical features can be fully *** experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection *** project is publicly available at https://***/taozh2017/SPNet.

关键词： salient object detection(SOD) RGB-D cross-enhanced integration module(CIM) multi-modal feature aggregation(MFA)

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RGB-D salient object detection:A survey

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Computational Visual Media 2021年第1期7卷 37-69页

作者： Tao Zhou deng-ping fan Ming-Ming Cheng Jianbing Shen Ling Shao Inception Institute of Artificial Intelligence(IIAI) Abu DhabiUnited Arab Emirates

Salient object detection,which simulates human visual perception in locating the most significant object(s)in a scene,has been widely applied to various computer vision ***,the advent of depth sensors means that depth maps can easily be captured;this additional spatial information can boost the performance of salient object *** various RGB-D based salient object detection models with promising performance have been proposed over the past several years,an in-depth understanding of these models and the challenges in this field remains *** this paper,we provide a comprehensive survey of RGBD based salient object detection models from various perspectives,and review related benchmark datasets in ***,as light fields can also provide depth maps,we review salient object detection models and popular benchmark datasets from this domain ***,to investigate the ability of existing models to detect salient objects,we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection ***,we discuss several challenges and open directions of RGB-D based salient object detection for future *** collected models,benchmark datasets,datasets constructed for attribute-based evaluation,and related code are publicly available at https://***/taozh2017/RGBD-SODsurvey.

关键词： RGB-D saliency light fields benchmarks

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Sequential interactive image segmentation

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Computational Visual Media 2023年第4期9卷 753-765页

作者： Zheng Lin Zhao Zhang Zi-Yue Zhu deng-ping fan Xia-Lei Liu TKLNDST College of Computer ScienceNankai UniversityTianjinChina Computer Vision Lab ETH ZurichZurichSwitzerland

Interactive image segmentation(IIS)is an important technique for obtaining pixel-level *** many cases,target objects share similar ***,IIS methods neglect this connection and in particular the cues provided by representations of previously segmented objects,previous user interaction,and previous prediction masks,which can all provide suitable priors for the current *** this paper,we formulate a sequential interactive image segmentation(SIIS)task for minimizing user interaction when segmenting sequences of related images,and we provide a practical approach to this task using two pertinent *** first is a novel interaction *** annotating a new sample,our method can automatically propose an initial click proposal based on previous *** dramatically helps to reduce the interaction burden on the *** second is an online optimization strategy,with the goal of providing semantic information when annotating specific targets,optimizing the model with dense supervision from previously labeled *** demonstrate the effectiveness of regarding SIIS as a particular task,and our methods for addressing it.

关键词： interactive segmentation user interaction object segmentation

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Full-duplex strategy for video object segmentation

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Computational Visual Media 2023年第1期9卷 155-175页

作者： Ge-Peng Ji deng-ping fan Keren Fu Zhe Wu Jianbing Shen Ling Shao School of Computer Science Wuhan UniversityWuhanChina Computer Vision Lab ETH ZurichETF C113.2Sternwartstrasse 78092 ZurichSwitzerland College of Computer Science Sichuan UniversityChengduChina Peng Cheng Laboratory ShenzhenChina School of Computer Science Beijing Institute of TechnologyBeijingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Previous video object segmentation approachesmainly focus on simplex solutions linking appearance and motion,limiting effective feature collaboration between these two *** this work,we study a novel and efficient full-duplex strategy network(FSNet)to address this issue,by considering a better mutual restraint scheme linking motion and appearance allowing exploitation of cross-modal features from the fusion and decoding ***,we introduce a relational cross-attention module(RCAM)to achieve bidirectional message propagation across embedding *** improve the model’s robustness and update inconsistent features from the spatiotemporal embeddings,we adopt a bidirectional purification module after the *** experiments on five popular benchmarks show that our FSNet is robust to various challenging scenarios(e.g.,motion blur and occlusion),and compares well to leading methods both for video object segmentation and video salient object *** project is publicly available at https://***/GewelsJI/FSNet.

关键词： video object segmentation(VOS) video salient object detection(V-SOD) visual attention

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Light field salient object detection:A review and benchmark

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Computational Visual Media 2022年第4期8卷 509-534页

作者： Keren Fu Yao Jiang Ge-Peng Ji Tao Zhou Qijun Zhao deng-ping fan College of Computer Science Sichuan Universityand National Key Laboratory of Fundamental Science on Synthetic VisionSichuan UniversityChengdu 610065China School of Computer Science Wuhan UniversityWuhan 430072China PCA Lab School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing 210094China Computer Vision Lab ETH ZürichZürichSwitzerland

Salient object detection(SOD)is a long-standing research topic in computer vision with increasing interest in the past *** light fields record comprehensive information of natural scenes that benefit SOD in a number of ways,using light field inputs to improve saliency detection over conventional RGB inputs is an emerging *** paper provides the first comprehensive review and a benchmark for light field SOD,which has long been lacking in the saliency ***,we introduce light fields,including theory and data forms,and then review existing studies on light field SOD,covering ten traditional models,seven deep learning-based models,a comparative study,and a brief *** datasets for light field SOD are also ***,we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets,providing insightful discussions and analyses,including a comparison between light field SOD and RGB-D SOD *** to the inconsistency of current datasets,we further generate complete data and supplement focal stacks,depth maps,and multi-view images for them,making them consistent and *** supplemental data make a universal benchmark ***,light field SOD is a specialised problem,because of its diverse data representations and high dependency on acquisition hardware,so it differs greatly from other saliency detection *** provide nine observations on challenges and future directions,and outline several open *** the materials including models,datasets,benchmarking results,and supplemented light field datasets are publicly available at https://***/kerenfu/LFSOD-Survey.

关键词： light field salient object detection(SOD) deep learning benchmarking

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Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data

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Machine Intelligence Research 2024年第2期21卷 318-330页

作者： Yu-Cheng Chou Bowen Li deng-ping fan Alan Yuille Zongwei Zhou Department of Computer Science Johns Hopkins UniversityBaltimore 21218USA Computer Vision Lab ETH ZurichZurich 8001Switzerland

Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and ***,with limited resources,it is challenging to determine the best type of annotations when annotating massive amounts of unlabeled *** address this issue,we focus on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans;Both applications require significant effort and time for pixel-wise annotation due to the high dimensional nature of the data,involving either temporary or spatial *** this paper,we develop a new annotation strategy,termed Drag&Drop,which simplifies the annotation process to drag and *** annotation strategy is more efficient,particularly for temporal and volumetric imaging,than other types of weak annotations,such as per-pixel,bounding boxes,scribbles,ellipses and ***,to exploit our Drag&Drop annotations,we develop a novel weakly supervised learning method based on the watershed *** results show that our method achieves better detection and localization performance than alternative weak annotations and,more importantly,achieves similar performance to that trained on detailed per-pixel ***,we find that,with limited resources,allocating weak annotations from a diverse patient population can foster models more robust to unseen images than allocating per-pixel annotations for a small set of *** summary,this research proposes an efficient annotation strategy for tumor detection and localization that is less accurate than per-pixel annotations but useful for creating large-scale datasets for screening tumors in various medical modalities.

关键词： Weak annotation detection localization segmentation colonoscopy abdomen

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PVT v2:Improved baselines with Pyramid Vision Transformer

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Computational Visual Media 2022年第3期8卷 415-424页

作者： Wenhai Wang Enze Xie Xiang Li deng-ping fan Kaitao Song Ding Liang Tong Lu ping Luo Ling Shao Shanghai AI Laboratory Shanghai 200232China Department of Computer Science and Technology NanjingUniversityNanjing 210023China Department of Computer Science the University ofHong KongHong Kong 999077China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210014China Computer Vision Lab ETH ZurichZurich 8092Switzerland SenseTime Beijing 100080China Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Transformers have recently lead to encouraging progress in computer *** this work,we present new baselines by improving the original Pyramid Vision Transformer(PVT v1)by adding three designs:(i)a linear complexity attention layer,(ii)an overlapping patch embedding,and(iii)a convolutional feed-forward *** these modifications,PVT v2 reduces the computational complexity of PVT v1 to linearity and provides significant improvements on fundamental vision tasks such as classification,detection,and *** particular,PVT v2 achieves comparable or better performance than recent work such as the Swin *** hope this work will facilitate state-ofthe-art transformer research in computer *** is available at https://***/whai362/PVT.

关键词： transformers dense prediction image classification object detection semantic segmentation

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Facial-sketch Synthesis:A New Challenge

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Machine Intelligence Research 2022年第4期19卷 257-287页

作者： deng-ping fan Ziling Huang Peng Zheng Hong Liu Xuebin Qin Luc Van Gool Computer Vision Laboratory ETH ZürichZürich 8092Switzerland Information and Communication Engineering University of TokyoTokyo 113-8654Japan Computer Vision Mohamed bin Zayed University of Artificial IntelligenceAbu DhabiUAE Digital Content and Media Sciences Research Division National Institute of InformaticsTokyo 101-8430Japan

This paper aims to conduct a comprehensive study on facial-sketch synthesis(FSS).However,due to the high cost of obtaining hand-drawn sketch datasets,there is a lack of a complete benchmark for assessing the development of FSS algorithms over the last *** first introduce a high-quality dataset for FSS,named FS2K,which consists of 2104 image-sketch pairs spanning three types of sketch styles,image backgrounds,lighting conditions,skin colors,and facial ***2K differs from previous FSS datasets in difficulty,diversity,and scalability and should thus facilitate the progress of FSS ***,we present the largest-scale FSS investigation by reviewing 89 classic methods,including 25 handcrafted feature-based facial-sketch synthesis approaches,29 general translation methods,and 35 image-to-sketch *** addition,we elaborate comprehensive experiments on the existing 19 cutting-edge ***,we present a simple baseline for FSS,named *** only two straightforward components,i.e.,facialaware masking and style-vector expansion,our FSGAN surpasses the performance of all previous state-of-the-art models on the proposed FS2K dataset by a large ***,we conclude with lessons learned over the past years and point out several unsolved *** code is available at https://***/dengpingfan/FSGAN.

关键词： Facial sketch synthesis(FSS) facial sketch dataset benchmark attribute style transfer

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Editorial for Special Issue on Multi-modal Representation Learning

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Machine Intelligence Research 2024年第4期21卷 615-616页

作者： deng-ping fan Nick Barnes Ming-Ming Cheng Luc Van Gool Nankai University China&ETH ZürichSwitzerland Australian National University Australia Nankai University China ETH Zürich Switzerland

The past decade has witnessed the impressive and steady development of single-modal AI technologies in several fields,thanks to the emergence of deep *** studied,however,is multi-modal AI-commonly considered the next generation of AI-which utilizes complementary context concealed in different-modality inputs to improve *** naturally learn to form a global concept from multiple modalities(i.e.,sight,hearing,touch,smell,and taste),even when some are incomplete or ***,in addition to the two popular modalities(vision and language),other types of data such as depth,infrared information,and events are also important for multi-modal learning in real-world scenes.

关键词： modal utilize incomplete

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