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8th International Conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Zhao, Haifeng Liu, Hao Cao, Mingwei Liu, Yi Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui Hefei230601 China PKU-HKUST Imsl Shenzhen Key Lab Shenzhen China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350302455

In this paper, we propose the TDNN-VITS model: an efficient custom speech synthesis system that can synthesize the speech of arbitrary target speakers, well-improving speech quality as well as reducing the amount of adaptive data. Our model consists of a speaker encoding module, which aims to extract speaker timbre information, and a TTS module, which is based on the fully end-to-end VITS model, but improved according to the problems of custom speech. To better improve the speech quality and speaker similarity, we propose a multi-angle fusion speaker embedding approach. With only 10 speech data and about one minute, good results can be achieved by just fine-tuning for half an hour. The experimental results show that our model is improved compared to the previous classical model, and good speech naturalness and speaker similarity are obtained. The ablation experiments also show the effectiveness of our multi-angle fusion. © 2023 IEEE.

关键词： Embeddings

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IMUSHIELD: Defending against inaudible attacks via IMUs on Smartphones 2

IMUSHIELD: Defending against inaudible attacks via IMUs on S...

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2nd International Conference on Electronic Information Technology, EIT 2023

作者： Zhang, Zhenfei Li, Ping Zhu, Biaokai Dong, Chong Lv, Zhao Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Shanxi Police College China School of Electrical and Information Engineering Anhui University of Technology China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

Inaudible attack has brought growing concerns over security of voice assistants. With a well-designed inaudible signal, an adversary can force the voice assistant to execute commands inaudibly like "Siri, open the door". It is challenging to defend against ultrasonic attacks without modifying the hardware. In this paper, we proposed a light-weight system named IMUSHIELD to defend voice assistant against inaudible attack. By comparing the different response of signal from microphone and inertial measurement units (IMUs) to different frequencies on smartphones. IMUSHIELD is able to detect the attacks without modifying the hardware. We have prototyped our method on a number of smartphones and test the performance of IMUSHIELD comprehensively in the real world, the result shows that our average detection accuracy exceeded 90 %. © 2023 SPIE.

关键词： Smartphones

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AlignFuse: Multi-feature Alignment and Fusion for Medical Image Segmentation 8

AlignFuse: Multi-feature Alignment and Fusion for Medical Im...

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8th International Conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Chao, Shengyang Liu, Zijun Huang, Lili Fu, Yanping Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China

ISBN: (纸本)9798350302455

U-Net, which is a U-shaped network, has made great progress on numerous medical image segmentation tracks. However, even if convolutional neural network (CNN) has a talent for extracting local information, it falls short in capturing global information due to the inherent drawback of the convolution operation. In contrast, whereby the design of tokenized images and self-attention mechanism, Transformer has a brilliant performance in modeling global contextual information but has a poor performance in grasping low-level details on account of the patch division which leads to ignorance of local correlation in each patch. In this paper, we develop a hybrid network structure, AlignFuse, which is in possession of the advantages of both CNN and Transformer. AlignFuse fits CNN and Transformer together in a parallel style, where Transformer leverages the high-level features and CNN pays more attention to details. Moreover, owing to the downsampling operation in CNNs, traditional context-free feature aggregation operations lead to misalignment issues between feature maps. To alleviate this problem, we propose to use Feature Aligned Fusion Module (FAFM) to effectively align and fuse the feature maps from CNN and Transformer in each decoder stage. We conduct experiments on Gland, breast cancer, and automatic cardiac segmentation, with competitive results obtained. © 2023 IEEE.

关键词： Convolution

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Feature Mutual Reinforcement Learning and Resampling for RGB-T Tracking 2

Feature Mutual Reinforcement Learning and Resampling for RGB...

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2nd International Conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2023

作者： Wang, Futian Zhou, Xuan Wang, Wenqi Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781510664968

More and more researchers have paid attention to the tracking of visible-thermal infrared (RGB-T). How to fully exploit the complementary features of visible and thermal infrared images and fully integrate them is a key issue. After extracting image features, many researchers simply fuse the features by adding, connecting operations or designing fusion modules. However, these methods ignore the effects of different levels of fusion features on target modeling and specific feature extraction. In this work, we propose a RGB-T tracking network (MRLRNet) based on feature mutual reinforcement learning and resampling. Specifically, we design a feature mutual reinforcement learning module, which combines different layers of features to achieve progressive fusion. After each layer feature is extracted, the aggregation features are used to enhance specific modal features to achieve better specific feature representation and reduce noise and redundancy features. At the same time, we design a resampling module, which calculates the offset of two adjacent frames by phase correlation operation, and recalculates the Gaussian sample points to solve the problem of ground target loss caused by sudden camera movement. A large number of experiments on three RGB-T tracking datasets, GTOT, RGBT234 and LasHeR, demonstrate the effectiveness of this method. © 2023 SPIE.

关键词： Reinforcement learning

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EdgeFormer: Edge-Assisted Transformer for Thermal Images Semantic Segmentation 2

EdgeFormer: Edge-Assisted Transformer for Thermal Images Sem...

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2nd International Conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2023

作者： Wang, Futian Ding, Zhongfeng Shi, Tao Tang, Jin Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781510664968

Most problems in the power system are related to the temperature of electrical equipment. The abnormal high temperature of electrical equipment will not only cause damage to equipment itself, but also threaten the safety of people's life and property. Therefore, patrol inspectors of power system will carry out routine inspection on electric equipment to ensure safety. Semantic segmentation for electrical equipment carries a big weight in the inspection. With the result of semantic segmentation of power equipment, patrol inspectors can quickly judge whether the temperature of power equipment is normal, and then take the corresponding action. For this reason, we propose the EdgeFormer, which is a typical end-to-end network using thermal image to segment electrical equipment. In our method, we employ an edge information extraction network to attain rich edge features to promote the segmentation performance in electrical equipment's edges and interiors, and we also design the global enhancing module (GEM) to get rich semantic information. Besides, we also propose a feature inserting module (FIM) to fuse the feature maps from different stages together. Lots of experiments have been conducted on the LS-ETS dataset and the results show that our EdgeFormer has achieved the best performance. © 2023 SPIE.

关键词： Semantic Segmentation

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Prototype Transferred Diverse Features for Day-night Cross-domain Facial Expression Recognition 16

Prototype Transferred Diverse Features for Day-night Cross-d...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Ma, Teng Qi, Yuhan Wang, ZhuoRan Li, YiPei Wang, Jiaxiang State Grid Corporation of Shanxi Province Shanxi 030025 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Beijing Institute of Technology China Dublin International College Ireland

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Facial Expression Recognition (FER) encounters significant challenges due to the limited sensitivity of visible light images in low light conditions. Most existing cross-domain emotion recognition studies have focused on domain adaptation for different visible light datasets, often neglecting the problem of emotion recognition under varying lighting conditions. To address this, we introduce the first cross-domain emotion recognition study for day and night environments, emphasizing the modal migration between normal visible light and low-light emotion maps. Considering the limited number of emotion maps and the reduced emotional detail in nighttime imaging, we designed an attention-switching diversified feature capture module to focus on facial expressions and extract more local emotional features that aid in migration. Given the significant domain shifts between samples in different lighting environments, we further developed a prototype feature transfer module to learn modality-independent category features and reduce domain differences between visible and low-light features. To tackle the challenge of numerous low sentiment value samples in the dataset, we introduce a high-confidence blending module to filter informative visible and infrared samples for fusion, producing features that combine the styles of both domains. Compared to state-of-the-art methods in domain adaptation and cross-domain emotion recognition, our approach demonstrates superior performance and validates its effectiveness. © 2025 SPIE.

关键词： Emotion Recognition

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Multi-Request Data Cache Optimization Strategy in Edge computing 26

Multi-Request Data Cache Optimization Strategy in Edge Compu...

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26th ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed computing, SNPD-Winter 2023

作者： Wang, Futian Wu, Zhimin Tang, Jin Li, Chenglong Zhang, Cheng School of Computer Science and Technology Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei China School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Anhui University Hefei China

ISBN: (纸本)9798350345865

With the rapid growth in the number of mobile devices, more and more data is created and requested by users. By caching the data to the edge server, users can obtain the content they request in a closer place, which reduces the network load and improves the user experience. However, existing articles only focus on a single request of users, while users often request multiple content at one time, which leads to more complex caching scenarios. In this paper, we will study an edge data cache scenario with multiple requests, and design a cost function to optimize the cache results. In this paper, we formulate the data cache problem as a mixed integer programming problem and minimize our cost function. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users, and the results reveal that our approach significantly outperform the baseline approaches. © 2023 IEEE.

关键词： Cost functions

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Speaker diarization based on multi-timescale feature fusion 2

Speaker diarization based on multi-timescale feature fusion

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2nd International Conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2023

作者： Wang, Futian Chen, Lailong Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781510664968

Deep and convolutional neural networks have performed well in capturing speaker characteristics, while the ECAPA-TDNN model has demonstrated outstanding performance in both the fields of speaker validation and speaker diarization. Within this essay, during the speech segmentation stage, we uniformly redivide the speech on multiple time scales based on the oracle voice activity detection. Meanwhile, we fine-tune the ECAPA-TDNN architecture by adding a RepVGG module to extract more abundant features, then aggregate all of the outputs. Finally, we use DOVER-Lap to integrate the results obtained after the clustering of multiple schemes in a way to obtain the final temporal labeling. The best results achieves 1.91% of the diarization error rate on the classical AMI conference corpus. © 2023 SPIE.

关键词： Speech recognition

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Camera-Proxy Enhanced Identity-Recalibration Learning for Unsupervised Visible-Infrared Person Re-Identification

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Xia, Run-Sen Wang, Xue-Yan Chen, Si-Bao Tang, Jin Luo, Bin Anhui University Moe Key Laboratory of Icsp Imis Laboratory of Anhui Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Zenmorn-AHU Ai Joint Laboratory School of Computer Science and Technology Hefei230601 China

Visible-Infrared person Re-Identification (VI-ReID) involves querying images of the same person across visible and infrared modalities. To minimize annotation costs, Unsupervised Visible-Infrared person Re-Identification (UVI-ReID) using pseudo-label contrastive learning has emerged. Traditional UVI-ReID approaches often neglected camera domain information and relied on inadequate update strategies during training, only using cosine distance for testing, which led to incorrect mapping of cross-modal relationships. To address these issues, we propose Camera-proxy Enhanced Identity-recalibration Learning (CEIL). It consists of two main stages: first, it employs intramodal contrastive learning in conjunction with the camera-proxy, updates the memory bank using our innovative Difficulty-aware Cluster-based Memory Updating (DCMU) strategy, and applies Camera Domain-driven Local correlation (CDL) Loss to enhance the learning process. Then utilizes cross-modal contrastive learning, featuring our Proxy-enhanced Cross-modal Mapping (PCM) module, to recalibrate the identity relationships between different modalities. Graph network-based Camera constraint adjustment Re-ranking (GCR) method is adopted during test, utilizing camera domain information to recalibrate the correspondence between identities. Extensive experiments have demonstrated that CEIL achieving state-of-the-art performance on the SYSU-MM01, RegDB, and LLCM datasets and the GCR, as a general unsupervised re-ranking method, can further enhance performance of model on these datasets. The code will be released at https://***/maybeextra/CEIL. © 1991-2012 IEEE.

关键词： Alignment

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Semantic-Aware Dual Contrastive Learning for Multi-Label Image Classification 26

Semantic-Aware Dual Contrastive Learning for Multi-Label Ima...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Ma, Leilei Sun, Dengdi Wang, Lei Zhao, Haifeng Luo, Bin School of Computer Science and Technology Anhui University China School of Artificial Intelligence Anhui University China School of Computer Science and Engineering Nanjing University of Science and Technology China Anhui Provincial Key Laboratory of Multimodal Cognitive Computing Anhui University China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

ISBN: (纸本)9781643684369

Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent works have focused on modeling label relationships with graph and understanding object regions using class activation maps (CAM). However, these methods ignore the complex intra- and inter-category relationships among specific semantic features, and CAM is prone to generate noisy information. To this end, we propose a novel semantic-aware dual contrastive learning framework that incorporates sample-to-sample contrastive learning (SSCL) as well as prototype-to-sample contrastive learning (PSCL). Specifically, we leverage semantic-aware representation learning to extract category-related local discriminative features and construct category prototypes. Then based on SSCL, label-level visual representations of the same category are aggregated together, and features belonging to distinct categories are separated. Meanwhile, we construct a novel PSCL module to narrow the distance between positive samples and category prototypes and push negative samples away from the corresponding category prototypes. Finally, the discriminative label-level features related to the image content are accurately captured by the joint training of the above three parts. Experiments on five challenging large-scale public datasets demonstrate that our proposed method is effective and outperforms the state-of-the-art methods. Code and supplementary materials are released on https://***/yu-gi-oh-leilei/SADCL. © 2023 The Authors.

关键词： Image classification

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