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Hybrid Scalable Video Coding with Neural Compression and Enhancement for Streaming Media 31st

Hybrid Scalable Video Coding with Neural Compression and E...

引用

31st International Conference on Multimedia Modeling, MMM 2025

作者： Ye, Yuyao Yang, Jiayu Zhao, Yang Gao, Mengping Cao, Hongbin Wang, Ronggang Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China School of Computer and Information Hefei University of Technology Hefei China Bytedance Inc. Shenzhen China

ISBN: (纸本)9789819620609

Streaming media is important in modern information consumption industry. However the limited and varied computing resources and bandwidth of client devices pose challenges for video coding. To make a balance between speed and coding efficiency and provide scalability for different devices, we propose a hybrid scalable coding framework (HSVC) which maintains a layered structure including a low resolution basement layer encoded by traditional coding methods and an optional enhancement layer encoded by neural coding methods. The enhancement layer coding is designed with a neural contextual compression module and a neural motion vector-guided enhancement module, which make full use of inter-layer information to boost the performance of enhancement layer and produce better visual quality. Experiments demonstrate that the proposed method outperforms existing scalable coding methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scalable video coding

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Image Segmentation Applied to Multi-species Phenotyping in Fish Farming 24th

Image Segmentation Applied to Multi-species Phenotyping in F...

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24th International Conference on Computational science and Its Applications (ICCSA)

作者： Batista, Fabricio Martins Brega, Jose Remo F. Sao Paulo State Univ UNESP Bauru SP Brazil

ISBN: (纸本)9783031646041;9783031646058

Fish farming has been gaining prominence in recent years, almost doubling world production in a ten-year period. Fish farming products are an important source of protein in coastal or insular countries, as they are the most abundant natural resource in these regions and are part of the daily diet of their populations. Recent technological advances allow the evolution of the practice of fish farming through new tools such as Artificial Intelligence and Internet of Things. Among the tasks that stand out is the phenotyping of animals raised in captivity during various stages of growth to assess the interaction of the species with the environment or even the prevalence of certain characteristics after successive reproductive selections. Phenotyping is usually performed manually through a digital image taken by an expert and post-processed in specialized measurement software using an object of known size as a reference in the image. Based on this problem, this work proposes a computer Vision System to automate the phenotyping of two common species in Brazilian fish farming: Piaractus Mesopotamicus (Pacu) and Colossoma macropomum (Tambaqui). The results indicate a positive correlation between the measurements performed by humans and the proposed computer Vision system, presenting itself as a viable alternative to accelerate the process of collecting information for reproductive selection in commercial fish farming.

关键词： Precision Agriculture Artificial Intelligence Genetic Selection Image Segmentation computer Vision

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Decision Making Analysis of Traffic Accidents on Mountain Roads in Yunnan Province 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Gong, Shu Hua, Gang Department of Computer Science Guangdong University Science and Technology Dongguan523083 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China

ISBN: (数字)9783031200991

ISBN: (纸本)9783031200984

Bipolar objects are widespread in nature, and they describe the opposition and unity of the things. Inspired by decision making characterizing in terms of fuzzy graph structures, we propose a bipolar fuzzy graph-based case study on mountain road traffic accident analysis in Yunnan province. The bipolar membership functions on corresponding bipolar graph are proposed and a novel algorithm for mountain road traffic accident in specific area is given. We analyze the result and give a reasonable explanation for this phenomenon. Some suggestions for reducing the number of mountain traffic accident are proposed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Assessment of Left Atrium Motion Deformation Through Full Cardiac Cycle 37th

Assessment of Left Atrium Motion Deformation Through Full C...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Qayyum, Abdul Mazher, Moona Lee, Angela Solis-Lemus, Jose A. Razzak, Imran Niederer, Steven A. National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Turing Research and Innovation Cluster: Digital Twins The Alan Turing Institute London United Kingdom Department of Computer Science University College London London United Kingdom School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9789819603503

Unlike Right Atrium (RA), Left Atrium (LA) presents distinctive challenges, including much thinner myocardial walls, complex and irregular morphology, as well as diversity in individual’s structure, making off-the-shelf methods designed for the Left Ventricle (LV) may not work in the context of the left atrium. To overcome aforementioned challenges, we are the first to present comprehensive technical workflow designed for 4D registration modeling to automatically analyze LA motion using high-resolution 3D Cine MR images. We integrate segmentation network and 4D registration process to precisely delineate LA segmentation throughout the full cardiac cycle. Additionally, an image 4D registration network is employed to extract LA displacement vector fields (DVFs). Our findings show the potential of proposed end to end framework in providing clinicians with novel regional biomarkers for left atrium motion tracking and deformation, carrying significant clinical implications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Intrusion Detection System Based on Adversarial Domain Adaptation Algorithm 1

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Fei, Jiahui Sun, Yunpeng Wang, Yuejin Lian, Zhichao School of Cyberspace Security Nanjing University of Science and Technology Nanjing China School of Computing Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9789819998937

ISBN: (纸本)9789819998920

With the explosive growth of the Internet, massive high-dimensional data and multiple attack types make intrusion detection systems face greater challenges. In practical application scenarios, the amount of abnormal data is small, and intrusion detection systems in different scenarios cannot be quickly migrated, and specific intrusion detection systems need to be trained for different scenarios, which greatly wastes manpower and material resources. Therefore, in view of the hierarchical characteristics of network data streams, this paper uses CNN and RNN networks to extract the spatiotemporal features of network data streams, then input them into GAN for unsupervised learning. Considering that long and short-term recurrent neural network (LSTM-RNN) has been shown to be able to obtain information and learn complex time series by remembering the backward (or even forward) time steps of cells, this paper replaces the generator and discriminator of GAN with LSTM-RNN. Anomaly detection is then performed based on residual loss and identification loss. Finally, this paper uses the deep domain adaptation algorithm to map the target domain and the source domain, and then optimizes the confusion loss of the domain by adversarial training, and finally extracts the invariant features of the target and the source domain. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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Workshop Report for IFIP WG 13.2's HCSE@INTERACT 2023 International Workshop on Human-Centered Software Engineering: Rethinking the Interplay of Human-computer Interaction and Software Engineering in the Age of Digital Transformation 19th

Workshop Report for IFIP WG 13.2's HCSE@INTERACT 2023 Intern...

引用

19th International-Federation-for-Information-Processing-Technical-Committee-13 (IFIP TC13) International Conference on Human-computer Interaction (INTERACT)

作者： Ardito, Carmelo Bernhaupt, Regina Sauer, Stefan LUM Giuseppe Degennaro Univ Casamassima Italy Eindhoven Univ Technol Eindhoven Netherlands Paderborn Univ Paderborn Germany

ISBN: (纸本)9783031616877;9783031616884

This paper presents the results of the International Workshop on Human-Centered Software Engineering, which was part of the 19th International Conference promoted by the IFIP Technical Committee 13 on Human-computer Interaction (Interact 2023). The leading topic of this edition of the workshop was "Rethinking the Interplay of Human-computer Interaction and Software Engineering in the Age of Digital Transformation". The workshop was characterized by an innovative format designed to bolster research efforts across various stages. Ten papers were presented by authors and discussed in the workshop. In terms of topics, there were three key sessions focusing on digitizing manufacturing processes, understanding users, and digitization for smart life Seven of these papers were extended in these proceedings.

关键词： digital transformation sustainability agile development low-code development digital twins ethical considerations cybersecurity

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BAMG: Text-Based Person Re-identification via Bottlenecks Attention and Masked Graph Modeling 17th

BAMG: Text-Based Person Re-identification via Bottlenecks A...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Cheng, Keyang Zou, Wenxuan Gu, Hongjian Ouyang, Anxiang Jiangsu University Zhenjiang Xuefu Road 301 Zhenjiang China University of Science and Technology of China Hefei Jinzhai Road 96 Hefei China

ISBN: (纸本)9789819609659

In the realm of computer vision, traditional person re-identifi-cation (ReID) methods have primarily focused on matching pedestrian identities across varied cameras and temporal instances. Text-based Person Re-identification (TBPReID) extends these efforts by utilizing textual descriptions alongside images to enhance retrieval applications, such as tracking suspects or locating missing children. A Text-based Person Re-identification framework based on bottleneck attention and masked graph modeling(BAMG) is introduced in this paper, which incorporates the prowess of CLIP’s pre-trained models into an advanced architecture. BAMG features a bottleneck fusion module for optimized modal integration, a Masked Graph Modeling (MGM) component for enhanced feature extraction, and additional supportive modules that refine the processing of multimodal data. BAMG not only enhances the alignment and interaction between text and image data but also significantly boosts the accuracy and robustness of the identification process. Through evaluations on the CUHK-PEDES dataset, the BAMG model has achieved a rank-1 accuracy of 79% and a mean average precision (mAP) of 68%. These results establish BAMG as a leading framework, setting new benchmarks for performance and adaptability in the field of multimodal learning environments focused on text-based person re-identification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Counting Unique Objects in Geo-Tagged Street Images: A Case Study of Homeless Encampments in Los Angeles 31st

Counting Unique Objects in Geo-Tagged Street Images: A Case...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Ghasemi, Narges Kim, Seon Ho Alfarrarjeh, Abdullah Shahabi, Cyrus Department of Computer Science University of Southern California Los Angeles United States Department of Computer Science German Jordanian University Amman11180 Jordan

ISBN: (纸本)9789819620739

Accurately counting unique objects in geo-tagged images presents significant challenges, particularly when the same objects appear in multiple images due to overlapping coverage areas, which can result in redundant counts. Traditional object detection and counting techniques, whether applied to images or videos, often struggle to accurately enumerate unique objects due to issues such as spatial redundancy, varying perspectives, and inconsistent object appearances across different images. This paper proposes a novel approach to address the problem of counting unique objects in geo-tagged images, specifically focusing on the challenges of distinguishing between distinct and duplicate objects. The proposed approach is characterized by two key features: 1) machine learning techniques are utilized to detect objects and improve distinguishing between them under varying imaging conditions, and 2) a spatial data structure is incorporated to organize the detected objects based on their geographical coordinates, allowing for unique object identification through spatial proximity analysis. As a case study, we apply our approach to counting homeless encampments in Los Angeles using Google Street View images, demonstrating its effectiveness in reducing counting redundancies and improving accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object detection

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A Novel Geometric-Encoded and Feature-Fused Model for Pressure Distribution Prediction on Airfoils 21st

A Novel Geometric-Encoded and Feature-Fused Model for Pressu...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Fang, Shiwei Xiang, Yu Zhang, Jun Wang, Wenyong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China International Institute of Next Generation Internet Macau University of Science and Technology Macau519020 China

ISBN: (纸本)9789819601219

Predicting the pressure distribution on an airfoil is significantly influenced by the coordinates, flight state, and the geometric shape of the airfoil. Existing methods of geometric shape representation commonly require pre-modeled airfoil data or images, which lack flexibility and overlook the potential relationship with the flight state. To address this challenge, we propose a novel Geometric-Encoded and Feature-Fused Model (GEFF), which achieves airfoil geometric encoding and feature fusion within an end-to-end model and applies it to the prediction of pressure distribution. In the model, considering the positional relationships of samples along the airfoil, we propose a novel distance attention mechanism where physical distance relationships are integrated into the calculations to generate encoded geometric features from coordinates. These encoded features and flight state features are then fused to predict the corresponding pressure distribution. Extensive ablation studies and comparison experiments were performed on GEFF. GEFF demonstrates high accuracy. Compared to Transformer, GEFF reduces the predicted mean square error by an average of 16.0%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Signal encoding

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Empowering Translational Health Data science Capabilities in Population Health Management A Case of Building a Data Competence Center 23rd

Empowering Translational Health Data Science Capabilities in...

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23rd IFIP WG 6.11 Conference on e-Business, e-Services and e-Society (I3E)

作者： Lefebvre, Armel de Schipper, Lisette Haas, Marcel Spruit, Marco Leiden Univ Med Ctr Dept Publ Hlth & Primary Care Albinusdreef 2 NL-2333 ZA Leiden Netherlands Leiden Univ LIACS POB 9512 NL-2300 RA Leiden Netherlands

ISBN: (纸本)9783031722332;9783031722349

In this paper we present the outcomes of a survey conducted among the research community of a Population Health Management department. The goal is to investigate how (translational) data science applications can be supported in a complex ecosystem of data sources and regulations of secondary healthcare data use. The envisioned solution is the creation of a data competence center as a multidisciplinary unit mixing research and professional support staff to provide data science technology, training, and resources to (early-career) researchers to address current challenges that are considerably impacting data quality and reproducibility in PHM research.

关键词： Population Health Management Data Infrastructure Data Competence Center Translational Data science Reproducibility

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