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A survey for light field super-resolution

High-Confidence Computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Model Checking for Probabilistic Multiagent Systems

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Journal of computer Science & technology 2023年第5期38卷 1162-1186页

作者：付辰 Andrea Turrini 黄小炜宋磊冯元张立军 State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Institute of Intelligent Software Guangzhou 511455China Department of Computer Science University of LiverpoolLiverpool L693BXU.K Microsoft Research Beijing 100190China Centre for Quantum Software and Information University of Technology SydneySydney 2007Australia

In multiagent systems,agents usually do not have complete information of the whole system,which makes the analysis of such systems *** incompleteness of information is normally modelled by means of accessibility relations,and the schedulers consistent with such relations are called *** this paper,we consider probabilistic multiagent systems with accessibility relations and focus on the model checking problem with respect to the probabilistic epistemic temporal logic,which can specify both temporal and epistemic ***,the problem is undecidable in *** show that it becomes decidable when restricted to memoryless uniform ***,we present two algorithms for this case:one reduces the model checking problem into a mixed integer non-linear programming(MINLP)problem,which can then be solved by Satisfiability Modulo Theories(SMT)solvers,and the other is an approximate algorithm based on the upper confidence bounds applied to trees(UCT)algorithm,which can return a result whenever *** algorithms have been implemented in an existing model checker and then validated on *** experimental results show the efficiency and extendability of these algorithms,and the algorithm based on UCT outperforms the one based on MINLP in most cases.

关键词： probabilistic multiagent system model checking uniform scheduler probabilistic epistemic temporal logic

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SCARE: A Novel Framework to Enhance Chinese Harmful Memes Detection

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IEEE Transactions on Affective Computing 2025年第2期16卷 933-945页

作者： Gu, Tianlong Feng, Mingfeng Feng, Xuan Wang, Xuemin Jinan University College of Information Science and Technology College of Cyber Security Guangzhou510632 China Guilin University of Electronic Science and Technology College of Computer and Information Security Guilin541004 China Guilin University of Electronic Science and Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China

Harmful meme detection presents a significant multimodal challenge that necessitates contextual background knowledge and comprehensive inference. Although some research studies have been related to harmful meme detection in English, detecting harmful memes in Chinese is also an unresolved issue. In this paper, to bridge this gap, we constructed a Chinese harmful meme detection dataset, named CHMEMES. Furthermore, existing multimodal alignment methods have shown poor performance in tasks involving harmful meme detection, where there is a mismatch between the image and text components. To improve the task, we propose a multimodal framework Semantic Contrastive Alignment fRamEwork (SCARE), which enables fully representing both cross-modal and intra-modal information. For cross-modal information, we introduce a cross-modal contrast alignment objective to maximize the mutual information between image and text. For intra-modal information, we design a new intra-modal contrast objective to achieve more robust visual and textual representation learning. Moreover, we present a simple yet efficient vision prompt tuning paradigm for parameter-efficient harmful meme detection. We conduct extensive experiments on the constructed Chinese dataset and the existing English dataset. Experimental results show that our method outperforms state-of-the-art baselines in harmful meme detection. © 2010-2012 IEEE.

关键词： Semantics

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Autonomous Aspect-Image Instruction A2II: Q-Former Guided Multimodal Sentiment Classification 30

Autonomous Aspect-Image Instruction A2II: Q-Former Guided Mu...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Feng, Junjia Lin, Mingqian Shang, Lin Gao, Xiaoying State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Engineering and Computer Science Victoria University of Wellington New Zealand

ISBN: (纸本)9782493814104

Multimodal aspect-oriented sentiment classification (MABSC) task has garnered significant attention, which aims to identify the sentiment polarities of aspects by combining both language and vision information. However, the limited multimodal data in this task has become a big gap for the vision-language multimodal fusion. While large-scale vision-language pretrained models have been adapted to multiple tasks, their use for MABSC task is still in a nascent stage. In this work, we present an attempt to use the instruction tuning paradigm to MABSC task and leverage the ability of large vision-language models to alleviate the limitation in the fusion of textual and image modalities. To tackle the problem of potential irrelevance between aspects and images, we propose a plug-and-play selector to autonomously choose the most appropriate instruction from the instruction pool, thereby reducing the impact of irrelevant image noise on the final sentiment classification results. We conduct extensive experiments in various scenarios and our model achieves state-of-the-art performance on benchmark datasets, as well as in few-shot settings. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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ISpliter:an intelligent and automatic surface mesh generator using neural networks and splitting lines

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Advances in Aerodynamics 2023年第1期5卷 362-386页

作者： Zengsheng Liu Shizhao Chen Xiang Gao Xiang Zhang Chunye Gong Chuanfu Xu Jie Liu State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410073China College of Computer National University of Defense TechnologyChangsha 410073China Laboratory of Digitizing Software for Frontier Equipment National University of Defense TechnologyChangsha 410073China

In this paper,we present a novel surface mesh generation approach that splits B-rep geometry models into isotropic triangular meshes based on neural networks and splitting *** the first stage,a recursive method is designed to generate plentiful data to train the neural network model *** the second stage,the implemented mesh generator,ISpliter,maps each surface patch into the parameter plane,and then the trained neural network model is applied to select the optimal splitting line to divide the patch into subdomains continuously until they are all *** the third stage,ISpliter remaps the 2D mesh back to the physical space and further optimizes *** typical cases are evaluated to compare the mesh quality generated by ISpliter and two baselines,Gmsh and *** results show that ISpliter can generate isotropic triangular meshes with high average quality,and the generated meshes are comparable to those generated by the other two software under the same configuration.

关键词： Surface mesh generation Artificial neural network Splitting line Triangular element Feature extraction

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MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning 39

MOS: Model Surgery for Pre-Trained Model-Based Class-Increme...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Sun, Hai-Long Zhou, Da-Wei Zhao, Hanbin Gan, Le Zhan, De-Chuan Ye, Han-Jia School of Artificial Intelligence Nanjing University China National Key Laboratory for Novel Software Technology Nanjing University China College of Computer Science and Technology Zhejiang University China

ISBN: (纸本)157735897X

Class-Incremental Learning (CIL) requires models to continually acquire knowledge of new classes without forgetting old ones. Despite Pre-trained Models (PTMs) have shown excellent performance in CIL, catastrophic forgetting still occurs as the model learns new concepts. Existing work seeks to utilize lightweight components to adjust the PTM, while the forgetting phenomenon still comes from parameter and retrieval levels. Specifically, iterative updates of the model result in parameter drift, while mistakenly retrieving irrelevant modules leads to the mismatch during inference. To this end, we propose MOdel Surgery (MOS) to rescue the model from forgetting previous knowledge. By training task-specific adapters, we continually adjust the PTM to downstream tasks. To mitigate parameter-level forgetting, we present an adapter merging approach to learn task-specific adapters, which aims to bridge the gap between different components while reserve task-specific information. Besides, to address retrieval-level forgetting, we introduce a training-free self-refined adapter retrieval mechanism during inference, which leverages the model’s inherent ability for better adapter retrieval. By jointly rectifying the model with those steps, MOS can robustly resist catastrophic forgetting in the learning process. Extensive experiments on seven benchmark datasets validate MOS’s state-of-the-art performance. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

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When debugging encounters artificial intelligence: state of the art and open challenges

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Science China(Information Sciences) 2024年第4期67卷 42-84页

作者： Yi SONG Xiaoyuan XIE Baowen XU School of Computer Science Wuhan University State Key Laboratory of Novel Software Technology Nanjing University

Both software debugging and artificial intelligence techniques are hot topics in the current field of software engineering. Debugging techniques, which comprise fault localization and program repair, are an important part of the software development lifecycle for ensuring the quality of software systems. As the scale and complexity of software systems grow, developers intend to improve the effectiveness and efficiency of software debugging via artificial intelligence(artificial intelligence for software debugging, AI4SD). On the other hand, many artificial intelligence models are being integrated into safety-critical areas such as autonomous driving, image recognition, and audio processing, where software debugging is highly necessary and urgent(software debugging for artificial intelligence, SD4AI). An AI-enhanced debugging technique could assist in debugging AI systems more effectively, and a more robust and reliable AI approach could further guarantee and support debugging techniques. Therefore, it is important to take AI4SD and SD4AI into consideration comprehensively. In this paper, we want to show readers the path, the trend, and the potential that these two directions interact with each other. We select and review a total of 165 papers in AI4SD and SD4AI for answering three research questions, and further analyze opportunities and challenges as well as suggest future directions of this cross-cutting area.

关键词： software debugging fault localization program repair artificial intelligence machine learning

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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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FCA-based θ-iceberg core decomposition in graphs

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1423-1428页

作者： Hao, Fei Xinchang, Khamphaphone Park, Doo-Soon Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an China School of Computer Science Shaanxi Normal University Xi’an710119 China Department of Computer Science and Engineering Soonchunhyang University Asan31538 Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan31538 Korea Republic of

Complex networking analysis is a powerful technique for understanding both complex networks and big graphs in ubiquitous computing. Particularly, there are several novel metrics, such as k-clique and k-core are proposed in order to study the relative importance of nodes in complex networks. Among of those metrics, k-core analysis is an effective approach for simplifying graphical structure. However, the relation between k and the scale of networks is not explored in most existing literature. Toward this end, this paper formulate a new research problem, θ-Iceberg Core decomposition in graphs, which is able to incorporate a parameter θ (0 © Springer-Verlag GmbH Germany, part of Springer Nature 2017.

关键词： Formal concept analysis

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Actor of Things: Resilient and Efficient Distributed AIoT Applications in an Actor System 31

Actor of Things: Resilient and Efficient Distributed AIoT Ap...

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31st Asia-Pacific software Engineering Conference, APSEC 2024

作者： Ren, Tianqi Cao, Chun Ma, Jun Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9798331534011

Complex and distributed AIoT applications usually involve multiple interconnected heterogeneous devices, leading to requirements of modeling device states and interaction diagrams. It is found that actor model is well-suited for distributed IoT contexts due to its inherent distributive nature and concurrency safety, but existing works lack runtime scalability and support for asynchronous task execution. In this paper, we propose Actor of Things, an approach to transform AIoT applications into efficient and resilient actor systems. We first transform synchronous function calls into Actor Task Graph which decouples nodes with real actors and executes asynchronously. The graph is then executed by Actor Runtime which deploys and schedules actors on IoT devices committed to lowest latency. Preliminary evaluations of our implementation demonstrate the advantages of proposed method in building distributed AIoT applications. © 2024 IEEE.

关键词： Actor Model Distributed Systems Internet of Things software Architecture

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