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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Knowledge Atlas on the Medical Artificial Intelligence research-A Bibliometric Analysis Based on CiteSpace and VOSviewer 2

Knowledge Atlas on the Medical Artificial Intelligence Resea...

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2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum, AIBDF 2022

作者： Shi, Li Han, Lingling Huang, Ke Ge, Fangzhen Zhang, Lele School of Computer Science and Technology Huaibei Normal University Huaibei235000 China School of Economics and Management Huaibei Normal University Huaibei235000 China Engineering Research Center of Cognitive Behavioral Intelligent Computing and Application Huaibei235000 China

ISBN: (纸本)9781510663107

In order to draw and analyze the current status of the research on the application of artificial intelligence in the field of health in China and abroad, we explored the hotspots and frontiers of this research field and the evolution path from 2012 to 2022. This research uses 3743 articles included in the core database of Web of science and 728 related research articles contained in CNKI as the basis for data analysis, and uses the advantages of bibliometrics visualization software of CiteSpace and VOSviewer to study institutions, countries, authors and literature keywords. In this research area, the research of medical artificial intelligence is growing day by day. By analyzing and sorting out the current research status, research hotspots and evolution path in this field of research, and trying to point out the deficiencies in the research in this field in China, the paper hopes to provide more references for researchers and practitioners of medical artificial intelligence related research. © 2023 SPIE.

关键词： Data visualization

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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Real Field Error Correction for Coded Distributed computing based Training

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IEEE Transactions on Cognitive Communications and networking 2024年

作者： Dai, Mingjun Zhang, Jiale Zhao, Yinglin Hua, Zhiyou Zhang, Shengli Shenzhen University Guangdong Provincial Engineering Center for Ubiquitous Computing and Intelligent Networking College of Electronic and Information Engineering Shenzhen518060 China Kashi University School of Computer Science Kashi844000 China

This work targets at real field error corrections in coded distributed computing (CDC) based deep learning (DL). On one hand, shift-and-addition (SA) encoding and zigzag decoding (ZD) is adopted in the encoding and decoding process, respectively, which collectively aim at reaping low computational complexity. On the other hand, storing real number induces truncation error, which effect becomes prominent under SA operation, and the truncation error may magnify and spread to a wide area along the ZD decoding process. These errors may temporarily drag the iteration point away from its working route which is equivalent to slowing down the training process. Firstly, to alleviate the truncation error magnification effect, we propose CDC-Pre/Post framework which pre-treats the data before encoding and post-treats the data after decoding. Secondly, to correct errors, we propose a method that uses convolution code to re-interprete the SA encoding structure, and design largest cluster decoding (LCD) algorithm. Thirdly, to enjoy both fast ZD and error correction capability of LCD, we propose smooth transition from ZD to LCD (ST-ZD-LCD) which is incremental with newly received packet. Experimental results show advantage of our scheme in two fold: First, it achieves significantly better error correction performance than naive ZD. Second, the training time of ST-ZD-LCD based distributed DL system is shortened significantly. © 2015 IEEE.

关键词： Encoding (symbols)

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Pheromone Matrix Eigenvalue-based Convergence Assessment Indicator for Ant Colony Optimization 11

Pheromone Matrix Eigenvalue-based Convergence Assessment Ind...

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11th International Conference on Machine Intelligence Theory and Applications, MiTA 2024

作者： Li, Xu Shi, Lin Li, Jian-Yu Xu, Jing Zhan, Zhi-Hui South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Guangdong University of Finance and Economics Research Center of Intelligent Computing & Big Data Technology Guangdong Guangzhou510320 China Nankai University College of Artificial Intelligence Tianjin300350 China

ISBN: (纸本)9798350385670

As a crucial member of the evolutionary computation (EC), ant colony optimization (ACO) converges to the optimal result through a continuous process of iterative optimization. However, determining the convergence status of an ant colony remains a challenging task without well-established methods. To this end, this paper proposes a global evaluation indicator - average absolute eigenvalue of pheromone matrix (AAEPM) by constructing a pheromone matrix and analyzing its eigenvalue variations. This indicator aims to assess the convergence status of the ant colony during the evolutionary process. Experimental results show that the AAEPM is correlated with the convergence status of ACO and is not significantly influenced by algorithm parameters and problem scale. © 2024 IEEE.

关键词： Anthropogenic

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LinNet: Linear Network for Efficient Point Cloud Representation Learning 38

LinNet: Linear Network for Efficient Point Cloud Representat...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Deng, Hao Jing, Kunlei Cheng, Shengmei Liu, Cheng Ru, Jiawei Bo, Jiang Wang, Lin State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University China Shaanxi Key Laboratory of Higher Education Institution of Generative Artificial Intelligence and Mixed Reality China School of Software Engineering Xi'an Jiaotong University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of downsampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73.7%, 81.4%, and 69.1% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available at https://***/DengH293/LinNet. © 2024 Neural information processing systems foundation. All rights reserved.

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Self-Bidirectional Decoupled Distillation for Time Series Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4101-4110页

作者： Xiao, Zhiwen Xing, Huanlai Qu, Rong Li, Hui Feng, Li Zhao, Bowen Yang, Jiayi Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu610031 China Tangshan Institute of Southwest Jiaotong University Tangshan063000 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China University of Nottingham School of Computer Science NottinghamNG7 2RD 455356 United Kingdom Xi'an Jiaotong University School of Mathematics and Statistics Xi'an710049 China Tongji University School of Software Engineering Shanghai201804 China

Over the years, many deep learning algorithms have been developed for time series classification (TSC). A learning model's performance usually depends on the quality of the semantic information extracted from lower and higher levels within the representation hierarchy. Efficiently promoting mutual learning between higher and lower levels is vital to enhance the model's performance during model learning. To this end, we propose a self-bidirectional decoupled distillation (self-BiDecKD) method for TSC. Unlike most self-distillation algorithms that usually transfer the target-class knowledge from higher to lower levels, self-BiDecKD encourages the output of the output layer and the output of each lower level block to form a bidirectional decoupled knowledge distillation (KD) pair. The bidirectional decoupled KD promotes mutual learning between lower and higher level semantic information and extracts the knowledge hidden in the target and nontarget classes, helping self-BiDecKD capture rich representations from the data. Experimental results show that compared with a number of self-distillation algorithms, self-BiDecKD wins 35 out of 85 University of California, Riverside (UCR) 2018 datasets and achieves the smallest AVeraGe (AVG)_rank score, namely 3.2882. In particular, compared with a nonself-distillation baseline, self-BiDecKD results in 58/8/19 regarding 'win'/'tie'/'lose.' © 2020 IEEE.

关键词： Semantics

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Noise processing and multitask learning for far-field dialect classification

Noise processing and multitask learning for far-field dialec...

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作者： Wang, Hai Wang, Yan Ma, Yuhui Qin, Chenguang Zhang, Kan Gao, Ling Ren, Jie School of Information and Technology Northwest University Xi'an China The State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services Northwest University Xi'an China Xi'an Polytechnic University Xi'an China School of Computer Science Shaanxi Normal University Xi'an China

Deep learning has made great achievements in the field of speech recognition. With the popularization of embedded devices such as intelligent speaker and the demand for dialect interaction scenes, it poses great challenges to far-field speech recognition and dialect language recognition. In order to solve the dialect language recognition of embedded devices in far-field speech recognition, we propose a deep learning neural network model with multitask learning. First, the audio is passed through the end-to-end noise reduction model to improve the effect of audio recognition. Then we define dialect recognition as the main task and dialect area as the auxiliary task, using the multitask learning method to improve the accuracy of dialect classification. The experimental results show that the end-to-end noise reduction model can improve the accuracy of audio recognition, and the best effect can be 7.54% higher than the baseline, and the accuracy of dialect language recognition can be improved by about 5% through multi task learning model. © 2022 John Wiley & Sons, Ltd.

关键词： Speech enhancement

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Fine-grained Semantic Alignment with Transferred Person-SAM for Text-based Person Retrieval 24

Fine-grained Semantic Alignment with Transferred Person-SAM ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yihao Yang, Meng Cao, Rui School of Computer Science and Engineering Sun Yat-Sen University Guangdong Guangzhou China State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Xi'an China Ministry of Education Guangdong Guangzhou China School of Information Science and Technology State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services Northwest University Shaanxi Xi'an China

ISBN: (纸本)9798400706868

Addressing the disparity in description granularity and information gap between images and text has long been a formidable challenge in text-based person retrieval (TBPR) tasks. Recent researchers tried to solve this problem by random local alignment. However, they failed to capture the fine-grained relationships between images and text, so the information and modality gaps remain on the table. We align image regions and text phrases at the same semantic granularity to address the semantic atomicity gap. Our idea is first to extract and then exploit the relationships between fine-grained locals. We introduce a novel Fine-grained Semantic Alignment with Transferred Person-SAM (SAP-SAM) approach. By distilling and transferring knowledge, we propose a Person-SAM model to extract fine-grained semantic concepts at the same granularity from images and texts of TBPR and its relationships. With the extracted knowledge, we optimize the fine-grained matching via Explicit Local Concept Alignment and Attentive Cross-modal Decoding to discriminate fine-grained image and text features at the same granularity level and represent the important semantic concepts from both modalities, effectively alleviating the granularity and information gaps. We evaluate our proposed approach on three popular TBPR datasets, demonstrating that SAP-SAM achieves state-of-the-art results and underscores the effectiveness of end-to-end fine-grained local alignment in TBPR tasks. © 2024 ACM.

关键词： Visual languages

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