检索结果-内蒙古大学图书馆

AUTOMATICALLY ADJUSTABLE MULTI-SCALE FEATURE EXTRACTION FRAMEWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

学校读者我要写书评

暂无评论

AUTOMATICALLY ADJUSTABLE MULTI-SCALE FEATURE EXTRACTION FRAM...

2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

作者： Yang, Jiaqi Du, Bo Wu, Chen Zhang, Liangpei State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China

Recently, deep learning-based methods have shown the great potential in hyperspectral image (HSI) classification. Nevertheless, feature extraction by convolutional neural network (CNN) is often performed on only one scale, resulting in multi-scale information loss. To address this problem, in this paper, we propose an automatically adjustable multi-scale feature extraction framework (A2MFE-Framework) for hyperspectral classification, including a scale reference network and two scale transformation networks. With the well-designed architecture, A2MFE-Framework can not only extract multi-scale features, but also automatically change the network structure to match input features of different scales. Experimental results on two benchmark HSI datasets demonstrate that the A2MFE-Framework can better capture multi-scale features of different objects via an automatically adjustable feature extraction framework with higher classification accuracy compared with previous methods. ©2021 IEEE

关键词： Feature extraction

HPCNet: A Hybrid Progressive Coupled network for Image Deraining

学校读者我要写书评

暂无评论

HPCNet: A Hybrid Progressive Coupled Network for Image Derai...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Qiong Wang Kui Jiang Jinyi Lai Zheng Wang Jianhui Zhang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Huawei Technologies Cloud BU College of Computer Science and Technology Hangzhou Dianzi University

Convolutional neural networks (CNNs) and Transformers have significantly succeeded in low-level vision tasks. Although prominent complementary characteristics exist regarding the larger receptive field and better convergence, only some efforts have compacted them efficiently due to their individual and nonnegligible weakness. In this paper, we propose a hybrid progressive coupled network (HPCNet) for rain perturbation removal, which integrates the advantages of these two architectures while maintaining both effectiveness and efficiency. In particular, we achieve the progressive decomposition and association of rain-free and rain features, designed as an asymmetrical dual-path mutual representation network to alleviate the computational cost. Meanwhile, we equip the network with high-efficiency convolutions and resolution rescaling strategy to trade off the computational complexity. Extensive experiments show that our method outperforms MPRNet on average while saving 87.2% and 61.1% of the computational cost and parameters.

关键词：

MANDARIN LOMBARD FLAVOR CLASSIFICATION

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Liu, Qingmu Yang, Yuhong Li, Baifeng Chen, Hongyang Tu, Weiping Lin, Song National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan China Guangdong OPPO Mobile Telecommunications Corp. China

The Lombard effect refers to individuals’ unconscious modulation of vocal effort in response to variations in the ambient noise levels, intending to enhance speech intelligibility. The impact of different decibel levels and types of background noise on Lombard effects remains unclear. Building upon the characteristic of Lombard speech that individuals adjust their speech to improve intelligibility dynamically based on the self-feedback speech, we propose a flavor classification approach for the Lombard effect. We first collected Mandarin Lombard speech under different noise conditions, then simulated self-feedback speech, and ultimately conducted the statistical test on the word correct rate. We found that both SSN and babble noise types result in four distinct categories of Mandarin Lombard speech in the range of 30 to 80 dBA with different transition points. Copyright © 2023, The Authors. All rights reserved.

关键词： Speech intelligibility

Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhu, Miaoxi Shen, Li Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China JD Explore Academy China The University of Sydney Australia

The growing size of available data has attracted increasing interest in solving minimax problems in a decentralized manner for various machine learning tasks. Previous theoretical research has primarily focused on the convergence rate and communication complexity of decentralized minimax algorithms, with little attention given to their generalization. In this paper, we investigate the primal-dual generalization bound of the decentralized stochastic gradient descent ascent (D-SGDA) algorithm using the approach of algorithmic stability under both convex-concave and nonconvex-nonconcave settings. Our theory refines the algorithmic stability in a decentralized manner and demonstrates that the decentralized structure does not destroy the stability and generalization of D-SGDA, implying that it can generalize as well as the vanilla SGDA in certain situations. Our results analyze the impact of different topologies on the generalization bound of the D-SGDA algorithm beyond trivial factors such as sample sizes, learning rates, and iterations. We also evaluate the optimization error and balance it with the generalization gap to obtain the optimal population risk of D-SGDA in the convex-concave setting. Additionally, we perform several numerical experiments which validate our theoretical findings. Copyright © 2023, The Authors. All rights reserved.

关键词： Stability

Zero-Shot Sharpness-Aware Quantization for Pre-trained Language Models

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhu, Miaoxi Zhong, Qihuang Shen, Li Ding, Liang Liu, Juhua Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China JD Explore Academy China The University of Sydney Australia

Quantization is a promising approach for reducing memory overhead and accelerating inference, especially in large pre-trained language model (PLM) scenarios. While having no access to original training data due to security and privacy concerns has emerged the demand for zero-shot quantization. Most of the cutting-edge zero-shot quantization methods primarily 1 apply to computer vision tasks, and 2 neglect of overfitting problem in the generative adversarial learning process, leading to sub-optimal performance. Motivated by this, we propose a novel zero-shot sharpness-aware quantization (ZSAQ) framework for the zero-shot quantization of various PLMs. The key algorithm in solving ZSAQ is the SAM-SGA optimization, which aims to improve the quantization accuracy and model generalization via optimizing a minimax problem. We theoretically prove the convergence rate for the minimax optimization problem and this result can be applied to other nonconvex-PL minimax optimization frameworks. Extensive experiments on 11 tasks demonstrate that our method brings consistent and significant performance gains on both discriminative and generative PLMs, i.e., up to +6.98 average score. Furthermore, we empirically validate that our method can effectively improve the model generalization. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

EMALG: AN ENHANCED MANDARIN LOMBARD GRID CORPUS WITH MEANINGFUL SENTENCES

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Li, Baifeng Liu, Qingmu Yang, Yuhong Chen, Hongyang Tu, Weiping Lin, Song National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan China Guangdong OPPO Mobile Telecommunications Corp. China

This study investigates the Lombard effect, where individuals adapt their speech in noisy environments. We introduce an enhanced Mandarin Lombard grid (EMALG) corpus with meaningful sentences, enhancing the Mandarin Lombard grid (MALG) corpus. EMALG features 34 speakers and improves recording setups, addressing challenges faced by MALG with nonsense sentences. Our findings reveal that in Mandarin, meaningful sentences are more effective in enhancing the Lombard effect. Additionally, we uncover that female exhibit a more pronounced Lombard effect than male when uttering meaningful sentences. Moreover, our results reaffirm the consistency in the Lombard effect comparison between English and Mandarin found in previous research. Copyright © 2023, The Authors. All rights reserved.

关键词：

Stability and generalization of the decentralized stochastic gradient descent ascent algorithm 23

学校读者我要写书评

暂无评论

Stability and generalization of the decentralized stochastic...

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Miaoxi Zhu Li Shen Bo Du Dacheng Tao School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China JD Explore Academy China The University of Sydney Australia

关键词：

Joint Secure and Covert Communications for Active STAR-RIS Assisted ISAC Systems

学校读者我要写书评

暂无评论

IEEE Transactions on Wireless Communications 2025年

作者： Guo, Liang Jia, Jie Mu, Xidong Liu, Yuanwei Chen, Jian Wang, Xingwei Northeastern University School of Computer Science and Engineering China Northeastern University Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China BelfastBT3 9DT United Kingdom Department of Electrical and Electronic Engineering China

This paper investigates the design of jointly supporting physical layer security (PLS) and covert communications (CCs) in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (a-STAR-RIS) assisted integrated sensing and communication (ISAC) system. Due to the unified waveform design of ISAC signals, we consider a challenging scenario with two targets being suspicious attackers, where one warden target potentially detects the confidential transmission behavior of covert users and another eavesdropper target attempts to intercept the broadcasted confidential information of security users. We investigate the joint beamforming design at the base station (BS) and the a-STAR-RIS to achieve a high-quality sensing beampattern while meeting covertness and security communication requirements. (1) For the ideal scenario with perfect channel state information (CSI) and precise target locations, we propose an alternative optimization (AO) method to address the optimization problem involving highly coupled variables. Specifically, the optimal beamforming design at the BS is handled using the semi-definite relaxation (SDR) technique, while the beamforming design at the a-STAR-RIS is addressed through a penalty-based iterative algorithm. (2) A more practical case with uncertain target locations and imperfect CSI is considered to achieve a robust beamforming design, where the non-deterministic outage probability constraints are effectively transformed by employing the Bernstein-type inequality. Numerical results demonstrate the superiority of the a-STAR-RIS over the baseline cases and certify that the proposed algorithms can effectively balance the tradeoff among the sensing quality, covert and secure communication requirements. Besides, results also show that the proposed robust beamforming scheme can construct adequate sensing beampattern, even with imperfect CSI and uncertain target locations. © 2002-2012 IEEE.

关键词： Beamforming

Exploring Leximin Principle for Fair Core-Selecting Combinatorial Auctions: Payment Rule Design and Implementation

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Cheng, Hao Kong, Shufeng Deng, Yanchen Liu, Caihua Wu, Xiaohu An, Bo Wang, Chongjun State Key Laboratory for Novel Software Technology Nanjing University China School of Software Engineering Sun Yat-Sen University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Artificial Intelligence Guilin University of Electronic Technology China National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications China

Core-selecting combinatorial auctions (CAs) restrict the auction result in the core such that no coalitions could improve their utilities by engaging in collusion. The minimum-revenue-core (MRC) rule is a widely used core-selecting payment rule to maximize the total utilities of all bidders. However, the MRC rule can suffer from severe unfairness since it ignores individuals’ utilities. To address this limitation, we propose to explore the leximin principle to achieve fairness in core-selecting CAs since the leximin principle prefers to maximize the utility of the worst-off;the resulting bidder-leximin-optimal (BLO) payment rule is then theoretically analyzed and an effective algorithm is further provided to compute the BLO outcome. Moreover, we conduct extensive experiments to show that our algorithm returns fairer utility distributions and is faster than existing algorithms of core-selecting payment rules. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning