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Invariant Model Learning on Local-Aware Wasserstein Geodesic for Domain Adaptation

Invariant Model Learning on Local-Aware Wasserstein Geodesic...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： You-Wei Luo Yi-Ming Zhai Chuan-Xian Ren School of Mathematics Jiaying University School of Mathematics Sun YatSen University China Mobile Internet Co. Ltd.

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As an important learning paradigm for signal processing and pattern recognition, unsupervised domain adaptation (UDA), which deals with the learning bias induced by the changing data environments (i.e., domains), has achieved great success in real-world applications. Mainstream UDA methods commonly adopt the discrepancy optimization on the two data domains directly, which ignore the hidden information in the latent intermediate domains and cannot sufficiently learn the invariant knowledge across domains; moreover, they usually fail when the domain gap is significant. In this work, a novel learning principle called Wasserstein invariant risk (WIR) is developed to gradually reduce the bias. The core idea is to recover the latent domains along the Wasserstein geodesic with local structure preservation, and explicitly map them into an invariant space via barycenter mapping. Intuitively, the Wasserstein geodesic captures the non-Euclidean structures of the latent domains. Methodologically, the barycenter mapping along geodesic can: 1) reduce the data discrepancy gradually and explore the hidden information; 2) ensure the consistency of risk estimation across domains; 3) admit invariance property of learning model for changing environments. Extensive experiments on visual UDA classification show the superiority of WIR over SOTA methods.

关键词： Geometry Degradation Adaptation models visualization Nonlinear distortion Estimation data models Pattern recognition Speech processing Optimization

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Band Prompting Aided SAR and Multi-Spectral data Fusion Framework for Local Climate Zone Classification

Band Prompting Aided SAR and Multi-Spectral Data Fusion Fram...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haiyan Lan Shujun Li Mingjie Xie Xuanjia Zhao Hongning Liu Pengming Feng Dongli Xu Guangjun He Jian Guan College of Computer Science and Technology Harbin Engineering University Harbin China School of Astronautics Beihang University Beijing China School of Software Dalian University of Technology Dalian China State Key Laboratory of Space-Ground Integrated Information Technology Beijing China Independent Researcher

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Local climate zone (LCZ) classification is of great value for understanding the complex interactions between urban development and local climate. Recent studies have increasingly focused on the fusion of synthetic aperture radar (SAR) and multi-spectral data to improve LCZ classification performance. However, it remains challenging due to the distinct physical properties of these two types of data and the absence of effective fusion guidance. In this paper, a novel band prompting aided data fusion framework is proposed for LCZ classification, namely BP-LCZ, which utilizes textual prompts associated with band groups to guide the model in learning the physical attributes of different bands and semantics of various categories inherent in SAR and multi-spectral data to augment the fused feature, thus enhancing LCZ classification performance. Specifically, a band group prompting (BGP) strategy is introduced to align the visual representation effectively at the level of band groups, which also facilitates a more adequate extraction of semantic information of different bands with textual information. In addition, a multivariate supervised matrix (MSM) based training strategy is proposed to alleviate the problem of positive and negative sample confusion by completing the supervised information. The experimental results demonstrate the effectiveness and superiority of the proposed data fusion framework.

关键词： Training visualization Semantics data integration Signal processing Robustness data mining Speech processing Synthetic aperture radar Meteorology

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Video Trailer Generation using Multimodal data Analysis

Video Trailer Generation using Multimodal Data Analysis

引用

international conference on Communication Systems and Network Technologies (CSNT)

作者： Pranathi Praveen Shreeja Rajesh Prajna Rama Murthy Nikhil Giridhar Surabhi Narayan Department of Computer Science and Engineering PES University Bengaluru India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Trailers have been a vital part of the entertainment industry to pique audience interest. The process of trailer generation has been evolving to provide effective trailers to the public. In this paper we propose a novel approach by utilizing the audio and video modalities to extract key trailer segments that are compiled to create an effective trailer which highlights the impactful scenes of the short film. In contrast to previous work in this domain, the novel approach gives greater emphasis to the auditory features, since auditory features have a significant impact on the film in the horror genre. The dataset is a compilation of 311 short films and their respective trailers from various reputable public sources. Our approach introduces an audio guided visual model that compliments the acoustic features ensuring that the extracted segments are key moments both in terms of audio and video. The results obtained indicate that a significant proportion of predicted segments are deemed highly trailer-worthy.

关键词： Support vector machines visualization Image segmentation Accuracy data analysis Communication systems Entertainment industry Motion pictures Feature extraction Robustness

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Automated Analysis of Pharmaceutical Literature Based on Natural Language Processing

Automated Analysis of Pharmaceutical Literature Based on Nat...

引用

Digital Analysis and Processing, Intelligent Computation (DAPIC), international conference on

作者： Xue Zeng Tingli Pan Yuanjiao Feng Yingqian Xu School of Pharmacy (School of Traditional Chinese Medicine) Chongqing Medical and Pharmaceutical College Chongqing China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

The rapid growth in the volume of pharmaceutical literature has posed significant challenges to traditional manual analysis methods. This study proposes an automated analysis method for pharmaceutical literature based on natural language processing. Through the construction of domain-specific dictionaries, design of text preprocessing workflows, and application of deep learning models for named entity recognition and relation extraction, efficient automated semantic analysis of pharmaceutical literature has been achieved. Experimental results show that this method achieves 90% accuracy in extracting key information such as drug names, mechanisms of action, and indications. Furthermore, the drug knowledge graph constructed based on the extracted structured information provides visual knowledge support for drug research and development. This method significantly improves the efficiency of literature analysis, paving a new way for pharmaceutical researchers to quickly acquire and utilize knowledge from literature.

关键词： Drugs Deep learning visualization Precision medicine Semantics Knowledge graphs Named entity recognition Manuals data mining Research and development

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Advanced Underwater Imagery Enhancement using CycleGAN for Marine Research and Photography

Advanced Underwater Imagery Enhancement using CycleGAN for M...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Abhishek Sarsiha Stewart Kirubakaran S Anitha J Santhiya P Ebenezer V Shobana M Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Sciences and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering SNS College of Technology Coimbatore India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

This paper proposes the Underwater Image Enhancement using CycleGAN generative adversarial network (GAN). It can be seen that underwater images encounter kinds of color distortion, low contrast, and haziness due to the interaction of light with water. CycleGAN helps our system to overcome the challenge of fine tuning raw underwater images to better quality images in terms of color and contrast. The goal of this method is to serve those in marine biology, underwater photography, and research by delivering superior visual data for enhanced identification and representation purposes. We mention below that we have compared the outcome with existing approaches, judged by standard image quality measures. Consequently, the results show the possible application of CycleGAN for achieving better image quality with minimized preprocessing time.

关键词： Photography Image quality Pervasive computing visualization Image color analysis Social networking (online) Generative adversarial networks Image enhancement Tuning Standards

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Identifying Intersections of Planes using Convolutional Neural Networks

Identifying Intersections of Planes using Convolutional Neur...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Kirill D. Volkov Pavel A. Panilov Tatiana Y. Tsibizova Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This research introduces a sophisticated methodology for analyzing the geometric characteristics of objects within two-dimensional images, emphasizing the detection of plane intersections. Unlike traditional algorithms, such as the Canny edge detector, which excel at generic edge detection but lack the specificity to discern intersection lines of planes, the proposed method leverages convolutional neural networks (CNNs) to enable advanced feature recognition. The hallmark of this approach lies in its ability to distinguish between positive and negative features of geometric elements, a capability critical for tasks requiring nuanced classification and contextual understanding. By integrating parallel data processing channels within the CNN architecture, the algorithm performs a multi-faceted analysis, isolating lines formed exclusively by plane intersections while filtering out irrelevant features, such as handwritten text or noise. This novel approach offers significant advantages in intelligent image classification tasks, particularly in environments characterized by high levels of visual complexity and noise.

关键词： Training Analytical models Adaptation models visualization Image edge detection Noise Training data Feature extraction Convolutional neural networks Testing

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Exploring Image-Based Approaches for Effective Synthetic Time-Series data Generation

Exploring Image-Based Approaches for Effective Synthetic Tim...

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international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Samgwon Oh Seungmin Oh Junghoon Lee Doyeon Kim Minsoo Hahn Jinsul Kim Department of Intelligence Electronics Computer Engineering Chonnam National University Gwangju Korea Department of Intelligence Information Convergence Chonnam National University Gwangju Korea Department of Computational and Data Science Astana IT University Astana Kazakhastan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

The effective generation of synthetic time-series data is crucial for addressing data scarcity in various domains such as network monitoring, financial analysis, and healthcare. This study explores image-based approaches for transforming time-series data into visual representations, enabling the application of advanced image synthesis techniques. We systematically evaluate multiple transformation methods, including recurrence plots, Gramian Angular Fields (GAF), and Markov transition fields, to identify the most suitable techniques for preserving temporal and structural characteristics. The generated synthetic data are validated using statistical similarity metrics and tested on machine learning tasks, such as anomaly detection and forecasting, to assess their practical utility. Experimental results demonstrate that image-based synthetic data generation not only retains essential time-series features but also enhances model robustness under limited data conditions. This research contributes to the growing field of synthetic data generation by providing insights into the integration of time-series and image processing techniques. Future work will focus on refining these methods and exploring their scalability across diverse applications.

关键词： Measurement visualization Scalability data collection Generative adversarial networks data models Forecasting Monitoring Anomaly detection Synthetic data

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Classification of Eye-Tracking data Based on Spatiotemporal Attention Encoding

Classification of Eye-Tracking Data Based on Spatiotemporal ...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiaju He Chen Xia Kuan Li Tian Zhang Beijing University of Chemical Technology Northwestern Polytechnical University The Affiliated Hospital of Northwest University Xi’an Jiaotong University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Eye movement classification can decode cognitive processes, offering valuable insights for a wide range of applications. However, existing eye movement classification models primarily focus on static fixation-based features and often neglect the encoding of spatiotemporal eye movement features, which are crucial for accurately reconstructing visual attention. To address this limitation, we propose a spatiotemporal attention encoding (STAE) model that jointly captures both spatial and temporal features for eye-tracking classification. First, we utilize a Vision Transformer (ViT) to extract spatial features from fixations by taking global competition into consideration. We then introduce a global weighting Gated Recurrent Unit (GRU) model to capture temporal correlations from the feature sequence. Specifically, we propose a hidden-state-based weighting to fuse the influence of different fixations on the current fixation. In the experiment, we evaluated our model on three tasks: autism spectrum disorder (ASD) identification, visual task classification, and age classification. Experiential results across three databases demonstrate that our model outperforms existing methods and shows strong adaptability across various eye movement classification tasks. The code is available at https://***/HectorTo/spatiotemporal-attention-encoding-STAE-.

关键词： Adaptation models visualization Fuses Gaze tracking Signal processing Feature extraction Transformers Encoding Spatiotemporal phenomena Speech processing

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Multivariate Time Series data Mining for Failure Prediction & Root Cause Analysis

Multivariate Time Series Data Mining for Failure Prediction ...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Naman Agarwal Gagan Raj Gupta Vishwesh Jatala Aniket Saha Radcom Department of CSE IIT Bhilai Chennai Mathematical Institute

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper presents an analysis of a large-scale, high-dimensional industrial dataset containing over 2 million data points collected over several months. The dataset includes more than 200 failures of various types, each resulting from complex causes. Utilizing state-of-the-art unsupervised multivariate anomaly detection algorithms, a system was developed that predicts failures several minutes in advance, introducing a new evaluation metric termed lead time. This system also identifies the location and potential causes of these failures. Initial application of current anomaly detection algorithms yielded low F1 scores, due to either low recall or low precision. To address this issue, a visual reasoning framework was created to reduce false positives by analyzing motifs and discords in the top-N anomalous signals contributing to the anomaly. We find that the human-in-the-loop approach enhances the precision and F1-score of multivariate algorithms by leveraging human judgment for final decision-making. Another key finding is that the LSTM-based anomaly detection algorithm achieves sufficient lead time for the industrial failure prediction studied in this paper.

关键词： Measurement visualization Root cause analysis Time series analysis Signal processing algorithms Reinforcement learning Signal processing Prediction algorithms Speech processing Anomaly detection

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Sound-VECaps: Improving Audio Generation with visually Enhanced Captions

Sound-VECaps: Improving Audio Generation with Visually Enhan...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yi Yuan Dongya Jia Xiaobin Zhuang Yuanzhe Chen Zhuo Chen Yuping Wang Yuxuan Wang Xubo Liu Xiyuan Kang Mark D. Plumbley Wenwu Wang Centre for Vision Speech and Signal Processing (CVSSP) University of Surrey ByteDance

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generative models have shown significant achievements in audio generation tasks. However, existing models struggle with complex and detailed prompts, leading to potential performance degradation. We hypothesize that this problem stems from the simplicity and scarcity of the training data. This work aims to create a large-scale audio dataset with rich captions for improving audio generation models. We first develop an automated pipeline to generate detailed captions by transforming predicted visual captions, audio captions, and tagging labels into comprehensive descriptions using a Large Language Model (LLM). The resulting dataset, Sound-VECaps, comprises 1.66M high-quality audio-caption pairs with enriched details including audio event orders, occurred places and environment information. We then demonstrate that training the text-to-audio generation models with Sound-VECaps significantly improves the performance on complex prompts. Furthermore, we conduct ablation studies of the models on several downstream audio-language tasks, showing the potential of Sound-VECaps in advancing audio-text representation *** and demos are available at https://***/Sound-VECaps-demo/.

关键词： Training Degradation visualization Large language models Pipelines Training data Benchmark testing Tagging Signal processing Speech processing

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