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arXiv 2023年

作者： Luo, Anwei Cai, Rizhao Kong, Chenqi Ju, Yakun Kang, Xiangui Huang, Jiwu Kot, Alex C. The School of Information Technology Jiangxi University of Finance and Economics Nanchang330013 China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Lab. School of Electrical and Electronic Engineering Nanyang Technology University Singapore The Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The China-Singapore International Joint Research Institute Singapore

With the rapid progress of generative models, the current challenge in face forgery detection is how to effectively detect realistic manipulated faces from different unseen domains. Though previous studies show that pre-trained Vision Transformer (ViT) based models can achieve some promising results after fully fine-tuning on the Deepfake dataset, their generalization performances are still unsatisfactory. One possible reason is that fully fine-tuned ViT-based models may disrupt the pre-trained features [1], [2] and overfit to some data-specific patterns [3]. To alleviate this issue, we present a Forgery-aware Adaptive Vision Transformer (FA-ViT) under the adaptive learning paradigm, where the parameters in the pre-trained ViT are kept fixed while the designed adaptive modules are optimized to capture forgery features. Specifically, a global adaptive module is designed to model long-range interactions among input tokens, which takes advantage of self-attention mechanism to mine global forgery clues. To further explore essential local forgery clues, a local adaptive module is proposed to expose local inconsistencies by enhancing the local contextual association. In addition, we introduce a fine-grained adaptive learning module that emphasizes the common compact representation of genuine faces through relationship learning in fine-grained pairs, driving these proposed adaptive modules to be aware of fine-grained forgery-aware information. Extensive experiments demonstrate that our FA-ViT achieves state-of-the-arts results in the cross-dataset evaluation, and enhances the robustness against unseen perturbations. Particularly, FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation. The code and trained model have been released at: https://***/LoveSiameseCat/FAViT. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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Dual-Source CBCT for Larger Longitudinal Coverage: System Design and Image Reconstruction

Dual-Source CBCT for Larger Longitudinal Coverage: System De...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xusheng Zhang Tianling Lyu Yijie Shi Xinyun Zhong Zhan Wu Yan Xi Shijie Wang Yang Chen Wentao Zhu School of Software Southeast University Nanjing China Zhejiang Lab Hangzhou China Laboratory of Image Science and Technology the School of Computer Science and Engineering Southeast University Nanjing China First-Imaging Tech. Shanghai China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts. The system design and reconstruction algorithm are tested on both simulated and real-scanned data. Results show that the DS-CBCT can expand the effective imaging volume by 37.1% compared to single-source CBCT systems. The reconstructed images by DT-FDK also show improved image quality compared to traditional reconstruction algorithms.

关键词： Image quality Orthopedic surgery Imaging Reconstruction algorithms Engineering in medicine and biology Image reconstruction System analysis and design

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Efficient spatialtemporal context modeling for action recognition?

arXiv

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arXiv 2021年

作者： Cao, Congqi Lu, Yue Zhang, Yifan Jiang, Dongmei Zhang, Yanning Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Lab on Speech and Image Information Processing School of Computer Science Northwestern Poly-technical University Xi'an710129 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China

Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. However, directly modeling the contextual information between any two points brings huge cost in computation and memory, especially for action recognition, where there is an additional temporal dimension. Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition. The global context is factorized into sparse relation maps. We model the relationship between points in the same line along the direction of horizon, vertical and depth at each time, which forms a 3D criss-cross structure, and duplicate the same operation with recurrent mechanism to transmit the relation between points in a line to a plane finally to the whole spatiotemporal space. Compared with the non-local method, the proposed RCCA-3D module reduces the number of parameters and FLOPs by 25% and 30% for video context modeling. We evaluate the performance of RCCA-3D with two latest action recognition networks on three datasets and make a thorough analysis of the architecture, obtaining the optimal way to factorize and fuse the relation maps. Comparisons with other state-of-the-art methods demonstrate the effectiveness and efficiency of our model. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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A hierarchical image matting model for blood vessel segmentation in fundus images

arXiv

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arXiv 2017年

作者： Fan, Zhun Lu, Jiewei Li, Wenji Wei, Caimin Huang, Han Cai, Xinye Chen, Xinjian Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shan'tou515063 China Department of Mathematics Shantou University Shan'tou515063 China School of Software Engineering South China University of Technology Guang'zhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiang'su210016 China Medical Image Processing and Analysis Lab School of Electronics and Information Engineering Soochow University Su'zhou215006 China

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and extendibility of retinal blood vessels is integrated into the image matting model for blood vessel segmentation. Normally the matting models require the user specified trimap, which separates the input image into three regions manually: the foreground, background and unknown regions. However, since creating a user specified trimap is a tedious and time-consuming task, region features of blood vessels are used to generate the trimap automatically in this paper. The proposed model has low computational complexity and outperforms many other state-of-art supervised and unsupervised methods in terms of accuracy, which achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly availab.e fundus image datasets DRIVE, STARE, and CHASE_DB1, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Blood vessels

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AIDE: Attack Inference Based on Heterogeneous Dependency Graphs with MITRE ATT&CK

AIDE: Attack Inference Based on Heterogeneous Dependency Gra...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Chunhe Xia Nan Feng Xinyi Pan Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Software & Microelectronics Peking University Beijing China School of Cyber Science and Technology Beihang University Beijing China School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The MITRE ATT&CK Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures (TTPs). By leveraging the associations among these techniques, we can infer unobserved techniques based on those have been observed. Current machine learning inference methods based on small-scale data face the following problems: "logical dependencies missing" and "complex associations missing." The former means that current association methods rely solely on the statistical dependencies between attack techniques while ignoring the logical dependencies involved in executing those techniques. The latter indicates that current inference methods simplify the complex associations between technologies based on statistical assumptions, failing to adequately capture the long-range and intricate real-world relationships between techniques. To address these problems, we propose an Attack Inference model based on heterogeneous DEpendency graphs (AIDE). First, we extract three types of logical dependencies: CVE, Pattern, and Conditional, which allows us to construct a heterogeneous dependency graph among the techniques, thus addressing the "logical dependencies missing" problem. Second, we employ Relation Graph Convolution Neural Networks (RGCN) to aggregate heterogeneous dependencies into representation vectors of the techniques. Finally, we utilize the sequence inference model Decoder, which excels at capturing implicit contextual associations, to infer unknown attacks, thus addressing the "complex associations missing" problem. Experiments conducted on real datasets demonstrate that our model significantly outperforms state-of-the-art baseline models in both hits@k and map@k metrics.

关键词： Adaptation models Aggregates Refining Machine learning Vectors Threat assessment Real-time systems Decoding Security Context modeling

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Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features

arXiv

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arXiv 2024年

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is availab.e at this url. © 2024, CC BY.

关键词： Semantics

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Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques

arXiv

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arXiv 2024年

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Yang, Zhiyong Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is availab.e at this url. © 2024, CC BY.

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Denoising of 3D magnetic resonance images using a residual encoder-decoder wasserstein generative adversarial network

arXiv

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arXiv 2018年

作者： Ran, Maosong Hu, Jinrong Chen, Yang Chen, Hu Sun, Huaiqiang Zhou, Jiliu Zhang, Yi College of Computer Science Sichuan University Chengdu610065 China Department of Computer Science Chengdu University of Information Technology Chengdu610225 China Lab of Image Science and Technology School of Computer Science and Engineering Southeast University Nanjing210096 China School of Cyber Science and Engineering Southeast University Nanjing210096 China Ministry of Education Nanjing210096 China Department of Radiology West China Hospital of Sichuan University Chengdu610041 China Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou510515 China

Structure-preserved denoising of 3D magnetic resonance imaging (MRI) images is a critical step in medical image analysis. Over the past few years, many algorithms with impressive performances have been proposed. In this paper, inspired by the idea of deep learning, we introduce an MRI denoising method based on the residual encoder-decoder Wasserstein generative adversarial network (RED-WGAN). Specifically, to explore the structure similarity between neighboring slices, a 3D configuration is utilized as the basic processing unit. Residual autoencoders combined with deconvolution operations are introduced into the generator network. Furthermore, to alleviate the oversmoothing shortcoming of the traditional mean squared error (MSE) loss function, the perceptual similarity, which is implemented by calculating the distances in the feature space extracted by a pretrained VGG-19 network, is incorporated with the MSE and adversarial losses to form the new loss function. Extensive experiments are implemented to assess the performance of the proposed method. The experimental results show that the proposed RED-WGAN achieves performance superior to several state-of-the-art methods in both simulated and real clinical data. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Facilities collab.ration in cloud manufacturing based on generalized collab.ration network

Facilities collaboration in cloud manufacturing based on gen...

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International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks (QSHINE)

作者： Wenxiang Li Chunsheng Zhu Edith C.-H. Ngai Laurence T. Yang Lei Shu Yuxia Sheng Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China Department of Electrical and Computer Engineering The University of British Columbia Vancouver BC Canada Department of Information Technology Uppsala University 751 05 Uppsala Sweden Department of Computer Science St. Francis Xavier University Antigonish NS B2G 2W5 Canada Guangdong Provincial Key Lab. of Petrochemical Equipment Fault Diagnosis Guangdong University of Petrochemical Technology Maoming Guangdong China

In cloud manufacturing for regional industrial cluster, there is increasing necessity of collab.ration among enterprises or facilities. It is valuable to explore the characteristics of these collab.ration behaviors for effectively scheduling dispersed manufacturing facilities and organizing their collab.ration. The collab.rative relation of manufacturing in regional industrial cluster can be described as a generalized social collab.ration network. In this paper, we introduce the relevant entities and relations of facilities collab.ration, and propose the method for building Facility Collab.ration Network (FCN). We further design the dynamically growing process of FCN for different facility selection strategies, including random selection, balanced selection, random selection with preference and balanced selection with preference. Based on the metrics such as network scale, node degree distribution, act degree distribution, average shortest distance and number of cliques, we present the statistical characteristics of FCN, and analyze relevant characteristics and laws for efficient facilities selection in cloud manufacturing.

关键词： Strips Acceleration

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A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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