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Visual Acuity Consistent Foveated Rendering towards Retinal Resolution

IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Zhang, Zhi Gai, Meng Li, Sheng Peking University School of Software and Microelectronics China Peking University School of Computer Science China National Key Laboratory of Intelligent Parallel Technology China

Prior foveated rendering methods often suffer from a limitation where the shading load escalates with increasing display resolution, leading to decreased efficiency, particularly when dealing with retinal-level resolutions. To tackle this challenge, we begin with the essence of the human visual system (HVS) perception and present visual acuity-consistent foveated rendering (VaFR), aiming to achieve exceptional rendering performance at retinal-level resolutions. Specifically, we propose a method with a novel log-polar mapping function derived from the human visual acuity model, which accommodates the natural bandwidth of the visual system. This mapping function and its associated shading rate guarantee a consistent output of rendering information, regardless of variations in the display resolution of the VR HMD. Consequently, our VaFR outperforms alternative methods, improving rendering speed while preserving perceptual visual quality, particularly when operating at retinal resolutions. We validate our approach using both the rasterization and ray-casting rendering pipelines. We also validate our approach using different binocular rendering strategies for HMD devices. In diverse testing scenarios, our approach delivers better perceptual visual quality than prior foveated rendering while achieving an impressive speedup of 6.5×-9.29× for deferred rendering of 3D scenarios and an even more powerful speedup of 10.4×-16.4× for ray-casting at retinal resolution. Additionally, our approach significantly enhances the rendering performance of binocular 8K path tracing, achieving smooth frame rates. © 1995-2012 IEEE.

关键词： Binoculars

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Image Computation for Quantum Transition Systems

arXiv

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arXiv 2025年

作者： Hong, Xin Gao, Dingchao Li, Sanjiang Ying, Shenggang Ying, Mingsheng State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information University of Technology Sydney Sydney Australia

With the rapid progress in quantum hardware and software, the need for verification of quantum systems becomes increasingly crucial. While model checking is a dominant and very successful technique for verifying classical systems, its application to quantum systems is still an underdeveloped research area. This paper advances the development of model checking quantum systems by providing efficient image computation algorithms for quantum transition systems, which play a fundamental role in model checking. In our approach, we represent quantum circuits as tensor networks and design algorithms by leveraging the properties of tensor networks and tensor decision diagrams. Our experiments demonstrate that our contraction partition-based algorithm can greatly improve the efficiency of image computation for quantum transition systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Tensors

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Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding 25

Dynamic Graph Transformer with Correlated Spatial-Temporal P...

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18th ACM International Conference on Web Search and Data Mining, WSDM 2025

作者： Wang, Zhe Zhou, Sheng Chen, Jiawei Zhang, Zhen Hu, Binbin Feng, Yan Chen, Chun Wang, Can Zhejiang University College of Computer Science Hangzhou China Zhejiang University School of Software Technology Ningbo China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China National University of Singapore Singapore Singapore Ant Group Hangzhou China

ISBN: (纸本)9798400713293

Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has garnered significant research interest, largely due to its powerful capabilities in modeling complex interactions between nodes. A fundamental and crucial requirement for representation learning in CTDGs is the appropriate estimation and preservation of proximity. However, due to the sparse and evolving characteristics of CTDGs, the spatial-temporal properties inherent in high-order proximity remain largely unexplored. Despite its importance, this property presents significant challenges due to the computationally intensive nature of personalized interaction intensity estimation and the dynamic attributes of CTDGs. To this end, we propose a novel Correlated Spatial-Temporal Positional encoding that incorporates a parameter-free personalized interaction intensity estimation under the weak assumption of the Poisson Point Process. Building on this, we introduce the Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding (CorDGT), which efficiently retains the evolving spatial-temporal high-order proximity for effective node representation learning in CTDGs. Extensive experiments on seven small and two large-scale datasets demonstrate the superior performance and scalability of the proposed CorDGT. The code is available at: https://***/wangz3066/CorDGT. © 2025 Copyright held by the owner/author(s).

关键词： Encoding (symbols)

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Optimizing Monitoring Utility of Unmanned Aerial Vehicles Considering Adverse Effects

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Haihan Dai, Haipeng Qiu, Yu Yu, Enze Zhou, Ruiben Wang, Weijun Wang, Jingwu Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China South China University of Technology School of Computer Science and Engineering Guangzhou China Beijing China

For Unmanned Aerial Vehicles (UAVs) monitoring tasks, capturing high quality images of target objects is important for subsequent recognition. Concerning the problem, many prior works study placement/trajectory planning for UAVs to maximize the quality of captured images. However, all of them overlook a fact that UAV monitoring may cause a huge risk/annoyance on living objects. In this paper, we investigate the novel problem of oPtimizing unmanned aErial vehicles plAcement by Considering both monitoring utility and adverse Effects (PEACE). We propose an approach to solve PEACE, which is proved to be NP-hard. Overall, our approach achieves a 1-1/e- approximation ratio. First, we approximate the original problem of PEACE as a classical problem of Monotone Submodular function Maximization under a Uniform Matroid constraint (MSMUM) with a controlled gap. Then, for MSMUM, we propose a combination of algorithms achieving a 1-1/e approximation and O(n\log n) time complexity considering the correlation among the UAV monitoring strategies. The proposed algorithms outperform existing algorithms for MSMUM through theoretical analysis and experimental results. Extensive simulations and field experiments demonstrate the effectiveness of our approach, achieving performance gains of 9.0% to 1434.5% compared to existing methods. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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UNI-IQA: A Unified Approach for Mutual Promotion of Natural and Screen Content Image Quality Assessment

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Song, Mengke Chen, Chenglizhao Song, Wenfeng Fang, Yuming Qingdao Institute of Software School of Computer Science and Technology China University of Petroleum China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China Computer School Beijing Information Science and Technology University China School of Information Management and Mathematics Jiangxi University of Finance and Economics China

To date, the image quality assessment (IQA) research field has mainly focused on natural images (NIs)-based IQA and screen content images (SCIs)-based IQA. Usually, these two research branches are quite independent due to the large differences between NIs and SCIs, where NIs, captured by cameras directly, contain pictorial information solely, yet, SCIs, synthesized or GPU-rendered, have pictures and textures. Moreover, the distortion types are also different, and subjective scores of different datasets assigned by participants are usually not well aligned. So, due to the above-mentioned "domain shifts" and "dataset misalignments", our research community has widely believed that it could be very difficult to achieve joint mutual promotions between NIs- and SCIs-based IQA. In this paper, we argue that despite the "differences", there still are some "common characteristics" — our human visual system perceives the "pictures" in both SCIs and NIs almost the same way. Thus, we can still achieve mutual performance promotion if we can appropriately use the "common characteristics" between SCIs and NIs. Our key idea is to devise a "content-aware" data switch, which, from the perspective of input’s contents (i.e., pictures or textures), aims at letting the model automatically enhance the commonness and compress the discrepancies between the two tasks. Notice that none of the existing fusion schemes can reach this goal since they are actually content-unaware, degenerating the "mutual interactions" into "mutual interferences". This paper is the first attempt to achieve full end-to-end "mutual interactions" between NIs- and SCIs-based IQA. Using the proposed switch, we are also the first to achieve solid mutual promotions for the two tasks, reaching new SOTA results. © 1991-2012 IEEE All rights reserved.

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Complementary Learning System Theory-based Active Learning for Audio Classification

Complementary Learning System Theory-based Active Learning f...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hui Geng Zijian Gao Tianjiao Wan Dawei Feng Changjian Wang Kele Xu College of Computer Science and Technology National University of Defense Technology Changsha China State Key Laboratory of Complex & Critical Software Environment Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep learning has significantly advanced the audio classification, achieving remarkable results. However, these successes often rely on extensive manual annotation of audio, a labor-intensive and costly process. Active Learning (AL) presents a promising solution by minimizing the required amount of annotation through the iterative selection of the most informative audio samples. Current AL methods for audio classification typically depend solely on the latest model checkpoint, overlooking the dynamics of the entire training process. The Complementary Learning Systems (CLS) theory posits that the interplay between short-term and long-term memory systems can effectively measure sample uncertainty, offering a means to capture training dynamics. In this work, we introduce a novel AL framework for audio classification, termed CLS-AL, which addresses the limitations of existing methods by simultaneously maintaining both short-term and long-term memory models. This dual-memory approach allows for a more comprehensive consideration of training dynamics. The divergence in predictions between these memory models provides a new metric for evaluating the uncertainty of unlabeled samples, enhancing the effectiveness of the AL sample selection process. We demonstrate the effectiveness and generalizability of CLS-AL through extensive experiments on a diverse set of audio datasets, showing that CLS-AL obviously outperforms existing state-of-the-art methods.

关键词： Training Uncertainty Annotations Active learning Measurement uncertainty Manuals Signal processing Predictive models Iterative methods Speech processing

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Probabilistic-Search and Neighbor-Density Based Doppler-Shift Acquisition in Space Communications

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IEEE Transactions on Communications 2025年

作者： Zhang, Zhaowei Du, Shuai Liu, Lin Liu, Hui Zuo, Jiakuo Miao, Wang Zhao, Haitao Nanjing University of Posts and Telecommunications School of Internet of Things China Nanjing University State Key Laboratory for Novel Software Technology China Jinling Institute of Technology School of Software Engineering China University of Exeter Department of Computer Science United Kingdom

In space communications, the signal's long-distance transmission between a flying transmitter-receiver pair encounters two obstacles: a huge path-loss and a high-speed movement. The huge path-loss results in a low signal-to-noise ratio (SNR) and the high-speed movement brings a dynamic Doppler-shift, thus posing a great challenge for Doppler-shift acquisition. Under the low SNR, the Doppler-shift acquisition has to accumulate many symbols in a long-time period. However, during this period, the dynamic Doppler-shift disperses all these symbols' total energy over a wide range, thus causing the energy dispersion problem. To address this problem, we propose a probabilistic-search and neighbor-density (PSND) scheme, where the probabilistic-search considers the element's amplitude and sacrifices some redundant signal elements for a narrower search-range, while the neighbordensity sums the signal element's all neighbors for a larger signal energy. The PSND scheme includes two algorithms: the Generic PSND and Iterative PSND. The Generic PSND algorithm first selects some search-elements with their probabilities to construct a probabilistic-search-range, then selects some density-elements with their neighbor-elements to derive the neighbor-densities, and finally searches the largest neighbor-density to obtain the acquisition result. Built upon the Generic PSND algorithm above, the Iterative PSND algorithm iteratively updates the searchelements to further strengthen the neighbor-density in a narrower probabilistic-search-range. Moreover, the simulations results have demonstrated our PSND scheme's higher acquisition probability, as compared with the existing schemes. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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Enhanced ID recommendation method utilizing semantic patterns of interactive behaviors

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Qinghua Daxue Xuebao/Journal of Tsinghua University 2025年第5期65卷 844-853页

作者： Wang, Yuanlai Bai, Yu Lian, Peng School of Computer Science Shenyang Aerospace University 110136 China National & Local Joint Engineering Laboratory of Multi-Language Collaborative Translation Technology Shenyang110136 China Shenyang Northern Software College of Information Technology Shenyang110136 China

[Objective] ID-based recommendation methods in recommender systems utilize unique identifiers of users or items to generate suggestions. However, these methods often encounter challenges such as data sparsity and cold-start problems, especially when using single-domain data. Cross-domain ID-based recommendations can help mitigate cold-start issues by relying on overlapping users or items across different domains. However, cross-domain ID information often lacks overlapped users or items. To address this, latent semantic patterns in behavioral networks across various recommender domains can be leveraged. This method aims to extract user preferences for items from discrete ID data, thereby tackling the limited shared information between these domains. [Methods] Based on the study of interaction behaviors, this paper assumes the existence of latent pattern correlations between user-item interactions across different domains. A potential factor connects users across domains, leading some users to exhibit similar interaction behaviors in different contexts. These shared characteristics are referred to as interaction behavior semantic patterns. The proposed pattern-enhanced ID recommendation method enhances ID-based recommendations by leveraging these semantic patterns. In the target domain recommendation task, auxiliary domain information is introduced, and information from both auxiliary and target domains is jointly encoded using a graph neural network. By incorporating interaction behavior semantic patterns, user-item interaction and item description information from the auxiliary domain are transferred to the target domain. This process enhances the semantics of interaction behaviors in ID-based recommendations within the target domain. [Results] This study conducts experiments on nine public datasets. User-item ID interaction data from datasets such as Yelp2018, Amazon-Kindle, Alibaba-iFashion, Amazon-Electronic, Book Crossing, MovieLens10M, MovieLens20M, and MovieL

关键词： Graph neural networks

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Image Tampering Detection With Frequency-Aware Attention and Multiview Fusion

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 614-625页

作者： Xu, Xu Chen, Junxin Lv, Wenrui Wang, Wei Zhang, Yushu Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Jiangxi University of Finance and Economics School of Computing and Artificial Intelligence Nanchang330013 China

Manipulated images are flooding our daily lives, which poses a threat to social security. Recently, many studies have focused on image tampering detection. However, they have poor performance on independent validation due to differences in image scenes and tampering methods. The key question is how to design a network that is able to adaptively enhance the tampering information and suppress the generalization features during training. To this end, we propose a dual-branch network with a frequency adaptation paradigm and a feature fusion module for robust tampering image detection. First, this paradigm is designed to adaptively highlight tampering features through frequency conversion and learnable weight. Second, a feature fusion module is developed to filter redundant features and dynamically fuse two-branch features. Experiments on eight typical datasets demonstrate that our model has advantages over state-of-the-art algorithms, and our paradigm can well empower semantic segmentation networks for tampering detection. © 2024 IEEE.

关键词： Semantic Segmentation

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Robust intrusion detection based on personalized federated learning for IoT environment

引用

computers and Security 2025年 154卷

作者： Sun, Shimin Zhou, Le Wang, Ze Han, Li Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong University Tianjin300387 China School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

In the dynamic and complex realm of the Internet of Things (IoT) and artificial intelligence (AI), it is a significant challenge to design a network intrusion detection system that balances accuracy, efficiency, and data privacy. Federated learning offers a solution by enabling the sharing of high-quality attack samples to enhance local models’ intrusion detection capabilities without compromising local data privacy. However, most existing research on federated learning for intrusion detection assumes homogeneity among local models, which can reduce detection accuracy in real-world scenarios where local datasets are often non-independent and identically distributed (Non-IID). The Non-IID characteristic, marked by varied distributional properties and correlations, impacts model convergence and stability. To address this challenge, we propose a personalized federated cross learning framework (pFedCross) for intrusion detection, to manage imbalanced and heterogeneous data distributions. First, we present a collaborative model cross aggregation algorithm for personalized local model update, to solve the problem that one global model cannot always accommodate all the incompatible convergence directions of local models. Then, we introduce a gradient approximation α-fairness algorithm for global model generation to achieve a well-generalization. Finally, the experiments show that pFedCross outperforms baseline methods in improving model accuracy and reducing loss, highlighting its promise for enhancing IoT security. © 2025 Elsevier Ltd

关键词： Federated learning

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