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Deep supervised fused similarity hashing for cross-modal retrieval

Multimedia Tools and Applications 2024年第39期83卷 86537-86555页

作者： Ng, Wing W. Y. Xu, Yongzhi Tian, Xing Wang, Hui Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering South China University of Technology Guangzhou China School of Artificial Intelligence South China Normal University Guangzhou China School of Electronics Electrical Engineering and Computer Science Queens University Belfast Belfast United Kingdom

The need for cross-modal retrieval increases significantly with the rapid growth of multimedia information on the Internet. However, most of existing cross-modal retrieval methods neglect the correlation between label similarity and intra-modality similarity in common semantic subspace training, which makes the trained common semantic subspace unable to preserve semantic similarity of original data effectively. Therefore, a novel cross-modal hashing method is proposed in this paper, namely, Deep Supervised Fused Similarity Hashing (DSFSH). The DSFSH mainly consists of two parts. Firstly, a fused similarity method is proposed to exploit the intrinsic inter-modality correlation of data while preserving the intra-modality relationship of data at the same time. Secondly, a novel quantization max-margin loss is proposed. The gap between cosine similarity and Hamming similarity is closed by minimizing this loss. Extensive experimental results on three benchmark datasets show that the proposed method yields better retrieval performance comparing to state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Dual-Path Spectrogram Refinement Network for Robust Speaker Verification

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The accuracy and reliability of automatic speaker verification (ASV) face significant challenges in noisy environments. In recent years, joint training of speech enhancement front-end and ASV back-end has been widely applied to improve the robustness of ASV systems. Traditional joint training directly uses the enhanced speech features as the input of the back-end. However, the diversity of noise types and noise intensities will excessively suppress the enhanced features, resulting in speech distortion or residual noise. To alleviate this problem, we propose a dual-path spectrogram refinement network that enables the enhanced features to learn supplementary information from the separated noise features. In addition, we incorporate coordinate attention into the overall joint architecture to capture more comprehensive frequency and temporal information of the speaker from different spatial positions. We conduct extensive experiments on the VoxCeleb1 test set, the out-of-domain noise test set, and the VOiCES corpus. The experimental results demonstrate that our proposed method significantly improves the accuracy and robustness of speaker verification systems in both clean and noisy environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Multiband Kick Drum Separation with Genre-Guided Optimization 10th

Multiband Kick Drum Separation with Genre-Guided Optimizati...

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10th Conference on Sound and Music technology, CSMT 2023

作者： Li, Si Jiang, Yuanfeng Wang, Zhaowen Qian, Jiale Li, Wei School of Computer Science and Technology Fudan University Shanghai200438 China Department of Music AI and Information Technology Central Conservatory of Music Beijing100091 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789819779611

Kick drum plays an important role in electronic music. When hearing an excellent kick drum sound from a reference music track, music production beginners always feel confused about how it will sound if we solo the kick track and why it sounds perfect in a specific song. Reproducing the kick of a music track is not easy even for professional producers and arrangers. To investigate the ability of artificial intelligence in separating kick-drum sounds, in this paper we propose an end-to-end system which is capable of separating multi-genre high-quality electronic kick drum clips from mixed song tracks. Unlike most of the previous music source separation tasks, we only focus on the electronic kick drum rather than the whole percussion track. First, we design a multiband data representation approach for non-pitched percussions such as kick drums, then adopt a multi-discriminator module trained on a multi-genre kick drum corpus to further optimize the separated kick sound. To the authors’ knowledge, this study marks the first attempt at kick drum separation with stylistic optimization. Objective and subjective evaluations demonstrate that joint multiband representation and genre-guided optimisation can form a high-performance framework, showing a promising solution to the kick drum separation task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Source separation

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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing

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China Communications 2025年第4期22卷 202-222页

作者： Ke Zhijie Xie Yong Syed Hamad Shirazi Li Haifeng School of Computer Technology and Application Qinghai UniversityXining 810016China School of Computer and Information Science Qinghai Institute of TechnologyXining 810016China Guangdong Key Laboratory of Blockchain Security Guangzhou UniversityGuangzhou 510006China Department of Information Technology Hazara UniversityBaffa 21110Pakistan Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining 810016China

Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple *** combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data *** address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing ***,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node ***,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication *** minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive *** analysis demonstrates that EPRFL offers robust security and low *** experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.

关键词： federated learning fog computing internet of things privacy-preserving robustness

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Source-free Domain Adaptation with Multiple Alignment for Efficient Image Retrieval

Source-free Domain Adaptation with Multiple Alignment for Ef...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Ni, Jinghui Cui, Hui Zhao, Lihai Li, Fengling Han, Xiaohui Xu, Lijuan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China Quan Cheng Laboratory China Shandong Open University China University of Technology Sydney Australia

ISBN: (纸本)9798350368741

Domain adaptation techniques help models generalize to target domains by addressing domain discrepancies between the source and target domain data distributions. These techniques are particularly valuable for cross-domain hashing retrieval, as they reduce training costs while maintaining high retrieval efficiency. However, existing unsupervised domain adaptative hashing methods often require access to both source and target domain data, which may raise privacy concerns regarding source domain data. To address these concerns, restricting access to source domain data is crucial, but this restriction also makes the alignment process between domains more challenging. In this paper, we propose a Source-free Domain Adaptive Hashing with Multiple Alignment (SFDAH-MA) approach for image retrieval. SFDAH-MA integrates model structure alignment, class moment alignment, and semantic relationship alignment to maximize inter-domain knowledge transfer. This comprehensive alignment strategy not only enhances retrieval performance across domains but also minimizes reliance on source domain data, thereby supporting data privacy protection. Extensive experiments show that SFDAH-MA achieves state-of-the-art performance in source-free settings and achieves comparable results to existing unsupervised domain adaptive hashing methods under conventional settings. The source codes of our method are available at: https://***/Nikphyc/SFDAH-MA. © 2025 IEEE.

关键词： Deep hashing image retrieval source-free domain adaptation

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Enhanced YOLOv7-Based Construction Site Environmental Detection Method 4

Enhanced YOLOv7-Based Construction Site Environmental Detect...

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4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Liang, Siheng Huang, Wenming Xiao, Yannan Guilin University of Electronic Technology School of Computer and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Guilin University of Electronic Technology School of Computer and Information Security Guilin China

ISBN: (纸本)9798350343618

This paper proposes an enhanced approach utilizing the YOLOv7 model for automated construction site environment detection and management. The method leverages high-resolution surveillance cameras and computer vision techniques to achieve this goal. The proposed method starts with data collection of the construction site environment. Then produce accurate bounding boxes. These bounding boxes serve as input to an improved YOLOv7 network model, enabling feature extraction and generating high-scoring feature maps with associated weights. Utilizing these weights, image segmentation is performed to obtain the desired detection heads. Experimental results demonstrate the effectiveness of the method in identifying and annotating various environmental features within the construction site, leading to significant improvements in detection accuracy. Compared to similar techniques, this method offers faster detection speed, higher accuracy, and reduced error rates. The proposed construction site environment detection method provides an efficient and automated solution for on-site management in the construction industry, with practical applications and value. © 2023 IEEE.

关键词： Construction industry

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A Low Profile and Broadband Pattern Reconfigurable Dielectric Resonator Antenna

A Low Profile and Broadband Pattern Reconfigurable Dielectri...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Fang, Hai Ting Yang, Wen Wen Guo, Lei Dalian University of Technology Information and Communication Engineering Dalian China Nantong University Information Science and Technology Nantong China Sun Yat-Sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China

ISBN: (纸本)9781733509657

In this paper, a low profile and broadband pattern reconfigurable dielectric resonator antenna (DRA) is proposed. Four different radiation patterns, including one conical pattern, two tilted patterns and one broadside pattern, are realized by changing the phase difference of the two excitation ports. The antenna has a broad impedance bandwidth of 15.1% (3.37-3.9 GHz) with a low profile of $\sim 0.1\lambda 0$. The simulated antenna gains of 5.42 dBi, 8 dBi and 8.48 dBi are achieved over the impedance bandwidth for conical, tilted, and broadside patterns, respectively. The antenna is a good candidate for 5G complex communication applications. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Microwave antennas

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4th International Conference on information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Zhou, Ya Zhao, Ning Huang, Guimin Deng, Nanxiao Guo, Qingkai School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350337181

In sentence similarity research methods, sentence similarity is often calculated from semantic aspects, however, the influence of other features is ignored. For example, the influence of sentence syntactic structure and word order on sentence similarity is not considered. Therefore, This paper proposes a method to measure the degree of similarity between sentences based on a multi-feature fusion of semantics, syntactic similarity and word order similarity, respectively. For the aspect of semantic similarity, in this paper, we first preprocess the sentence and obtain the word vectors, and then obtains sentence vector features through neural networks, so as to calculate the sentence semantic similarity;Finally, for the aspect of word order similarity, word order is considered as the auxiliary feature information to calculate sentence similarity since word order has a smaller effect on sentence similarity compared to the above information. The experimental results show that the proposed model has an accuracy of 77.4 % and an F1 value of 84.3% on the MRPC dataset. © 2023 IEEE.

关键词： Semantics

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Faster and More Robust Low-Resource Nearest Neighbor Machine Translation 12th

Faster and More Robust Low-Resource Nearest Neighbor Machin...

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12th National CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Sun, Shuo Hou, Hongxu Yang, Zongheng Wang, Yisong College of Computer Science Inner Mongolia University National and Local Joint Engineering Research Center of Intelligent Information Hohhot China Processing Technology for Mongolian Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9783031446955

Transformer-based neural machine translation (NMT) models have achieved performance close to human-level on some languages, but still suffer from poor interpretability and scalability of the models. Many advanced studies enhance the model’s translation ability by building external memory modules and utilizing retrieval operations, however, it suffers from poor robustness and low decoding efficiency while improving the model performance, especially for low-resource translation tasks. In this paper, we propose a confidence-based gating mechanism to optimize the decoding efficiency by building a sub-network to determine the confidence of the model’s own translation capability and then decide whether the current translation needs to be retrieved from the memory module. By reducing the number of retrievals to improve the model’s translation speed without degrading the translation quality as much as possible. In addition, we use a nonparametric dynamic Monte Carlo-based algorithm to fuse retrieval probabilities and model predictions to improve the generalization and robustness of the model. Extensive experiments on different datasets demonstrate the effectiveness of our method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Neural machine translation

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Multi-Perspective Frame Element Representation for Machine Reading Comprehension 8th

Multi-Perspective Frame Element Representation for Machine ...

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8th China Conference on Knowledge Graph and Semantic Computing, CCKS 2023

作者： Guo, Shaoru Guan, Yong Li, Ru School of Computer and Information Technology Shanxi University Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China

ISBN: (纸本)9789819972234

Semantics understanding is a critical aspect of Machine Reading Comprehension (MRC). In recent years, researchers have started leveraging the semantic knowledge provided by FrameNet to enhance the performance of MRC systems. While significant efforts have been dedicated to Frame representation, there is a noticeable lack of research on Frame Element (FE) representation, which is equally crucial for MRC. We propose a groundbreaking approach called the Multi-Perspective Frame Elements Representation (MPFER) method. It aims to comprehensively model FEs from three distinct perspectives: FE definition, Frame (semantic scenario), and FE relation. By considering these multiple perspectives, our proposed model significantly improves the representation and understanding of FEs in MRC tasks. To validate the effectiveness of the MPFER method, we conducted extensive experiments. The results clearly demonstrate that our proposed model outperforms existing state-of-the-art methods. The superiority of our approach highlights its potential for advancing the field of MRC and showcasing the importance of properly modeling FEs for better semantic understanding. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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