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Distortion-adaptive salient object detection in 360◦ omnidirectional images

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arXiv 2019年

作者： Li, Jia Su, Jinming Xia, Changqun Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Adavanced Innovation Center for Big Data and Brain Computing Beihang University China National Engineering Laboratory for Video Technology School of Electronics Engineering and Computer Science Peking University Beijing China Peng Cheng Laboratory Shenzhen518000 China

Image-based salient object detection (SOD) has been extensively explored in the past decades. However, SOD on 360◦ omnidirectional images is less studied owing to the lack of datasets with pixel-level annotations. Toward this end, this paper proposes a 360◦ image-based SOD dataset that contains 500 high-resolution equirectangular images. We collect the representative equirectangular images from five mainstream 360◦ video datasets and manually annotate all objects and regions over these images with precise masks with a free-viewpoint way. To the best of our knowledge, it is the first public available dataset for salient object detection on 360◦ scenes. By observing this dataset, we find that distortion from projection, large-scale complex scene and small salient objects are the most prominent characteristics. Inspired by these foundings, this paper proposes a baseline model for SOD on equirectangular images. In the proposed approach, we construct a distortion-adaptive module to deal with the distortion caused by the equirectangular projection. In addition, a multiscale contextual integration block is introduced to perceive and distinguish the rich scenes and objects in omnidirectional scenes. The whole network is organized in a progressively manner with deep supervision. Experimental results show the proposed baseline approach outperforms the top-performanced state-of-the-art methods on 360◦ SOD dataset. Moreover, benchmarking results of the proposed baseline approach and other methods on 360◦ SOD dataset show the proposed dataset is very challenging, which also validate the usefulness of the proposed dataset and approach to boost the development of SOD on 360◦ omnidirectional scenes. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

Surface Modification of (001) Facets Dominated TiO2 with Ozone for Adsorption and Photocatalytic Degradation of Gaseous Toluene

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Chinese Journal of Chemical Physics 2019年第5期32卷 611-619页

作者： Yue Yang Zhi-yu Wang Fan Zhang Yi Fan Jing-jing Dong Song Sun Chen Gao Jun Bao National Synchrotron Radiation Laboratory Collaborative Innovation Center of Chemistry for Energy Materials Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei 230029 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China School of Chemistry and Chemical Engineering Anhui University Hefei 230601 China Beijing Advanced Sciences and Innovation center Chinese Academy of Sciences Beijing 101407 China

This study investigated the positive effect of surface modification with ozone on the photocatalytic performance of anatase TiO2 with dominated（001） facets for toluene *** performance of photocatalyst was tested on a home-made volatile organic compounds degradation system. The ozone modification, toluene adsorption and degradation mechanism were established by a combination of various characterization methods, in situ diffuse reflectance infrared fourier transform spectroscopy, and density functional theory *** surface modification with ozone can significantly enhance the photocatalytic degradation performance for toluene. The abundant unsaturated coordinated 5 c-Ti sites on（001）facets act as the adsorption sites for ozone. The formed Ti–O bonds reacted with H2O to generate a large amount of isolated Ti5 c-OH which act as the adsorption sites for toluene,and thus significantly increase the adsorption capacity for toluene. The outstanding photocatalytic performance of ozone-modified TiO2 is due to its high adsorption ability for toluene and the abundant surface hydroxyl groups, which produce very reactive OH·radicals under irradiation. Furthermore, the O2 generated via ozone dissociation could combine with the photogenerated electrons to form superoxide radicals which are also conductive to the toluene degradation.

关键词： Ozone modification (001) Facets Toluene degradation TiO2 In situ diffuse reflectance infrared fourier transform spectroscopy

Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer

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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

Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

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arXiv 2024年

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

Dynamic Searchable Scheme with Forward Privacy for Encrypted Document Similarity

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Dynamic Searchable Scheme with Forward Privacy for Encrypted...

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

作者： Mustafa A. Al Sibahee Chengwen Luo Jin Zhang Yijing Huang Zaid Ameen Abduljabbar National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen PR China College of Computer Science and Software Engineering Shenzhen University Shenzhen PR China Computer Technology Engineering Department Iraq University College Basrah Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen PR China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections.

关键词： Privacy data privacy Cloud computing Memory Search problems Cryptography Servers

Digital Mahjong system: Towards Precise Cognitive Assessment with IoT Technologies 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Hu, Enze Guo, Yanrui Yang, Jiaoyun Au, Rhoda Ding, Huitong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shenzhen Corecloud Innovation Technology Co. Ltd. Shenzhen China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Anatomy and Neurobiology Neurology and Framingham Heart Study Boston University School of Medicine Boston United States Department of Epidemiology Boston University School of Public Health Boston United States

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

To the best of our knowledge, this paper is the first to apply IoT technologies to transform the popular Mahjong game into a Digital Mahjong system (DMS) for digitally performing cognitive assessments. People have started exploring digital cognitive assessment tools for better objectivity and simplification. However, most of these tools are not friendly for older adults. This paper aims to address this issue by integrating IoT technologies with Mahjong to make cognitive assessment simpler and more engaging for older adults. DMS has the following features: 1) integrating motion tracking devices into Mahjong tiles and transferring them into Digital Mahjong Tiles (DMTs), which can precisely capture their moving trajectory during the assessment, 2) using a Near Field Communication reader to configure DMT’s primary data, 3) supporting Over-The-Air Device Firmware Update that can be done remotely via Bluetooth, and 4) developing a charging base that can charge 16 DMTs simultaneously using Qi wireless charging standard. With further testing, the DMS has the potential to become a digital platform to implement and test various cognitive assessment tools integrating Mahjong elements. © 2022, Springer Nature Switzerland AG.

关键词： Internet of things

ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT data

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arXiv 2022年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Wu, Peixi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China The School of Dentistry Shenzhen University Shenzhen China

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

Clustering with adaptive graph learning and spectral rotation

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Clustering with adaptive graph learning and spectral rotatio...

7th International Conference on Vision, Image and Signal Processing (ICVISP 2023)

作者： G. Yuan J. Zhou C. Huang C. Gao Y. Wang X. Shen National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 518060. Guangdong People's Republic of China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 Guangdong People's Republic of China College of Mathematics and Statistics Shenzhen University Shenzhen 518060 Guangdong People's Republic of China

Prototype-based clustering algorithms have garnered considerable attention in the field of machine learning due to their efficiency and interpretability. Nonetheless, these algorithms often face performance degradation when confronted with high-dimensional or non-ellipsoidal data distributions. To surmount these challenges, this study introduces a novel clustering approach, dubbed Clustering with Adaptive Graph learning and Spectral Rotation (CAGSR). In CAGSR, the imposed spectral rotation operation mitigates the discrepancy between the membership matrix, which adheres to the notion of fuzzy clustering, and the spectral representations derived from an adaptive graph rather than a predefined one. This enables the generation of a comprehensive representation of the data across multiple spaces. Furthermore, the clustering and graph learning tasks are jointly optimized in a projected subspace, which can effectively reduce the adverse impact caused by irrelevant features in the original space. The proposed method seamlessly integrates fuzzy clustering, graph structure learning, and spectral rotation into a unified model, facilitating the detection of intrinsic structures. Experimental evaluations conducted on benchmark data sets substantiate the effectiveness of CAGSR when compared to related clustering approaches.

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SDGFormer: An Efficient Convolution Network Structurally Similar to Transformer

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SDGFormer: An Efficient Convolution Network Structurally Sim...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Chaohao Wen Xun Gong School of Computing and Artificial Intelligence SWJTU Chengdu China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education China National Engineering Laboratory of Integrated Transportation Big Data Application Technology SWJTU Chengdu China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province SWJTU Chengdu China

Deep Neural Networks (DNN) have achieved extraordinary success in many visual recognition tasks. Visual Transformer (ViT), which is derived from Natural Language Processing (NLP), has achieved state-of-the-art (SOTA) results on many tasks due to its capability of capturing long-range dependencies in visual data. However, Existing ViT models are challenging to deploy on devices due to their massive computational consumption, huge memory overhead, and reliance on large datasets. In this work, we address these issues by replacing some computationally expensive and memory-intensive modules in ViT with standard Convolutional Neural Network (CNN) modules. Firstly, we propose an efficient Self-Attention module called SDG-Attention (SDGA) with linear space and time complexity, and an economical FeedForward Network (FFN) composed of group convolution and shuffle channel (SFFN). Then, we develop a lightweight CNN model with SDGA and SFFN, SDGFormer, which embraces several priors of ViT and is LayerNorm-Free. We evaluate SDGFormer on ImageNet-1K and Mini-ImageNet, and the SDGFormer-S achieves a comparable top-1 accuracy of 77.6% on ImageNet-1K with 9.1M parameters and 1.6 GFlops regimes. Moreover, our SDGFormer-T achieves SOTA performance on Mini-ImageNet with 83.3% accuracy, demonstrating good generalization on small datasets without extra data.

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