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LUMOS-DM: Landscape-Based Multimodal Scene Retrieval Enhanced by Diffusion Model 30th

LUMOS-DM: Landscape-Based Multimodal Scene Retrieval Enhance...

引用

30th International Conference on MultiMedia Modeling, MMM 2024

作者： Huynh, Viet-Tham Nguyen, Trong-Thuan Nguyen, Quang-Thuc Tran, Mai-Khiem Nguyen, Tam V. Tran, Minh-Triet Software Engineering Laboratory and Faculty of Information Technology University of Science VNU-HCM Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science University of Dayton Dayton United States

ISBN: (纸本)9783031533013

Information retrieval is vital in our daily lives, with applications ranging from job searches to academic research. In today’s data-driven world, efficient and accurate retrieval systems are crucial. Our research focuses on video data, using a system called LUMOS-DM: Landscape-based Multimodal Scene Retrieval Enhanced by Diffusion Model. This system leverages Vision Transformer and Diffusion Models, taking user-generated sketch images and text queries as input to generate images for video retrieval. Initial testing on a dataset of 100 h of global landscape videos achieved an 18.78% at Top-20 accuracy rate and 36.45% at Top-100 accuracy rate. Additionally, video retrieval has various applications, including generating data for advertising and marketing. We use a multi-modal approach, combining sketch and text descriptions to enhance video content retrieval, catering to a wide range of user needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diffusion

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Simplified Video Retrieval in Virtual Reality with vitrivr-VR 31st

Simplified Video Retrieval in Virtual Reality with vitrivr...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Spiess, Florian Rossetto, Luca Schuldt, Heiko Department of Mathematics and Computer Science University of Basel Basel Switzerland Dublin City University Dublin Ireland

ISBN: (纸本)9789819620739

The use and application of virtual reality (VR) continue to grow as more advanced VR-capable hardware is developed. With VR hardware entering the mainstream, it becomes increasingly important to develop methods to support use cases previously performed using conventional user interfaces, such as desktop systems with keyboard and mouse. One very important such use case is video retrieval and browsing. Previous experiments have indicated that, while there appears to be a benefit to browsing videos in VR, the expression of textual queries remains a challenge. In this paper, we describe a new version of the vitrivr-VR virtual reality multimedia retrieval system, as it will participate in the video browser showdown (VBS) 2025. This new version of vitrivr-VR aims to overcome the two main challenges that have been identified in participation in previous installments of the VBS, namely the retrieval performance and the expression of textual queries, while continuing to leverage the advantages of video browsing in VR. We plan to overcome these challenges using a more performant retrieval backend (vitrivr-engine) and the use of newer VR-capable hardware (Apple Vision Pro) to enable easier text input. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Content based retrieval

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Multiple Attention Network for Facial Expression Recognition 20th

Multiple Attention Network for Facial Expression Recognition

引用

20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Feng, Wenyu Fei, Zixiang Zhou, Wenju Fei, Minrui School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

ISBN: (纸本)9789819970247

Facial expression recognition (FER) has become increasingly important in the field of human-computer interaction. This paper proposes an improved method with attention mechanism to improve FER performance. Our approach is grounded in two crucial observations. Firstly, as multiple categories share similar underlying facial characteristics, distinguishing between them may be subtle. Secondly, recognizing facial expressions demands a comprehensive approach by encoding high-order interactions among localized features from multiple facial regions simultaneously. To address these challenges, we introduce our MAN model consisting of three key components: Multi-branch stack Residual Network (MRN), Transitional Attention Network (TAN), and Appropriate Cascade Structure (ACS). The TAN learns objectives to maximize class separability, while the MRN deploys attention heads to focus on various facial regions and generate attention maps. Additionally, the ACS module provides a more reasonable construction method for the model. Comprehensive experiments on three publicly available datasets (AffectNet, RAF-DB, and CK+) consistently achieves better expression recognition performance. Compared to the ResNet network, our approach yielded an improved accuracy of 3.3%, 3.2%, 4.1% and 1.8% on the Affectnet7 dataset, Affectnet8 dataset, RAF-DB dataset, and CK+ dataset, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Human computer interaction

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Enhanced 3D Dense U-Net with Two Independent Teachers for Infant Brain Image Segmentation 27th

Enhanced 3D Dense U-Net with Two Independent Teachers for In...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Khaled, Afifa Elazab, Ahmed School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783031781971

Accurate segmentation of infant brain images from magnetic resonance imaging (MRI) scans is crucial for studying brain development. Existing deep learning methods often rely on encoder-decoder structures with local operators, limiting their ability to efficiently capture long-range information. Moreover, these models struggle to integrate diverse tissue properties from different MRI sequences, leading to computational and memory challenges during inference. To address these limitations, we propose a novel model, 3D-DenseUNet, which incorporates adaptable global aggregation blocks to mitigate spatial information loss during down-sampling. We enhance the model with a self-attention module that integrates feature maps across spatial and channel dimensions, improving representation potential and discrimination ability. Furthermore, we introduce a novel learning scheme, termed "two independent teachers," which leverages model weights instead of label predictions. In this scheme, each teacher model is trained on a specific MRI sequence (T1 and T2) to capture diverse tissue properties. A fusion model is then employed to enhance test accuracy while reducing computational overhead. Empirical evaluations on two distinct datasets demonstrate the effectiveness of our approach. Our source code is publicly available at https://***/AfifaKhaled/Two-Independent-Teachers-are-Better-Role-Model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Deep Learning Residential Building Segmentation for Evaluation of Suburban Areas Development 24th

Deep Learning Residential Building Segmentation for Evaluati...

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24th International Conference on Computational science (ICCS)

作者： Lysak, Agnieszka Luckner, Marcin Jan Kochanowski Univ Inst Phys Ul Uniwersytecka 7 PL-25406 Kielce Poland Warsaw Univ Technol Fac Math & Informat Sci Ul Koszykowa 75 PL-00662 Warsaw Poland

ISBN: (纸本)9783031637858;9783031637834

Deep neural network models are commonly used in computer vision problems, e.g., image segmentation. Convolutional neural networks have been state-of-the-art methods in image processing, but new architectures, such as Transformer-based approaches, have started outperforming previous techniques in many applications. However, those techniques are still not commonly used in urban analyses, mostly performed manually. This paper presents a framework for the residential building semantic segmentation architecture as a tool for automatic urban phenomena monitoring. The method could improve urban decision-making processes with automatic city analysis, which is predisposed to be faster and even more accurate than those made by human researchers. The study compares the application of new deep network architectures with state-of-the-art solutions. The analysed problem is urban functional zone segmentation for the urban sprawl evaluation using targeted land cover map construction. The proposed method monitors the expansion of the city, which, uncontrolled, can cause adverse effects. The method was tested on photos from three residential districts. The first district has been manually segmented by functional zones and used for model training and evaluation. The other two districts have been used for automated segmentation by models' inference to test the robustness of the methodology. The test resulted in 98.2% accuracy.

关键词： Transformers SegFormer Deep learning Semantic segmentation computer vision

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Document-Level Relation Extraction Model Based on Boundary Distance Loss and Long-Tail Relation Enhancement 21st

Document-Level Relation Extraction Model Based on Boundary D...

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

作者： Liu, Xin Yilahun, Hankiz Imam, Seyyare Hamdulla, Askar School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi830017 China School of National Security Studies Xinjiang University Urumqi830017 China

ISBN: (纸本)9789819601189

In today’s data-driven era, deriving meaningful insights from documents poses a considerable challenge. The task of Document-level Relation Extraction (DocRE) seeks to discern relationships among multiple entities within documents, mirroring real-world scenarios and introducing more complexity than sentence-level relation extraction (SLRE). DocRE encounters issues such as numerous widely dispersed entities, an imbalance between positive and negative instances, and a long-tail distribution of relation types. We introduce a model for document-level relation extraction that utilizes boundary distance loss and long-tail relation enhancement to tackle these challenges. This approach adeptly captures intricate interactions among entities, enhancing multi-relation extraction performance. Our model achieved improvements of 3.22% and 3.15% in F1 and Ign-F1 scores on the RE-DocRED dataset, respectively. Moreover, it significantly surpasses recent strong baseline models on the JacRED dataset. The experimental results confirm the model’s effectiveness and generalizability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Boundary distance adaptive threshold loss Document-level relation extraction Long-tail relationship enhancement Multi-relation extraction

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Improving Speaker Recognition by Time-Frequency Domain Feature Enhanced Method 20th

Improving Speaker Recognition by Time-Frequency Domain Featu...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Han, Jin Zi, Yunfei Xiong, Shengwu College of Computer and Artificial Intelligence Wuhan University of Technology Wuhan China Sanya Science and Education Innovation Park Wuhan University of Technology Hainan Wuhan572000 China

ISBN: (纸本)9789819970216

Many existing speaker recognition algorithms have the problem that single-domain feature extraction cannot represent the speech characteristics well, and this problem will affect the accuracy of speaker recognition. To solve this problem, we propose a time-frequency domain feature enhanced deep speaker (TFDS). The proposed algorithm can combine time domain and frequency domain, enhance the traditional MFCC feature extraction, and make up for the shortcomings of other algorithms that only extract features in a single domain. The deep speaker network architecture includes ResCNN, GRU, time averaging layer, style transformation layer, length normalization layer, and the loss is triple loss. Representation of experimental results performed on the librisspeech dataset results show that TFDS has higher accuracy and lower Equal Error Rate than deep speaker, and the time-frequency domain feature enhanced method can also be combined with other networks to improve the accuracy of speaker recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Feature extraction

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Agitated Behaviors Detection in Children with ASD Using Wearable Data 1

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20th International Conference on Smart Living and Public Health, ICOST 2023

作者： Montassar, Imen Chikhaoui, Belkacem Wang, Shengrui Department of Computer Science Université de Sherbrooke SherbrookeQc Canada Department of Science and Technology Université TÉLUQ Quebec CityQc Canada

ISBN: (数字)9783031439506

ISBN: (纸本)9783031439490

Children diagnosed with Autism Spectrum Disorder (ASD) often exhibit agitated behaviors that can isolate them from their peers. This study aims to examine if wearable data, collected during everyday activities, could effectively detect such behaviors. First, we used the Empatica E4 device to collect real data including Blood Volume Pulse (BVP), Electrodermal Activity (EDA), and Acceleration (ACC), from a 9-years-old male child with autism over 6 months. Second, we analyzed and extracted numerous features from each signal, and employed different classifiers including Support Vector Machine (SVM), Random Forest (FR), eXtreme Gradient Boosting (XGBoost), and TabNet. Our preliminary findings showed good performance in comparison with the state of the art. Notably, XGBoost demonstrated the highest performance in terms of accuracy, precision, recall, and F1-score. The accuracy achieved in this paper using XGBoost is equal to 80 % which exceeds previous research. © 2023, The Author(s).

关键词： Support vector machines

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Investigating Swimming Effect of Hologram in Mixed Reality 27th

Investigating Swimming Effect of Hologram in Mixed Reality

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Raj, Subin Harshitha, B.R. Chakrabarti, Amaresh Biswas, Pradipta Indian Institute of Science Bangalore India

ISBN: (纸本)9783031783111

The continuum of eXtended Reality Displays consists of Augmented Reality, Mixed Augmented Reality or Mixed Reality and Virtual Reality systems. Among these systems, mixed reality systems promise new set of applications as it combines both 3D rendering of virtual reality and real world mapping of augmented reality interfaces. Many mixed reality headsets like Microsoft HoloLens relies on holograms to render 3D imagery on real world objects. However, ensuring the stability of these holograms, popularly known an swimming effect, is crucial, as their position appears altered from the user’s perspective when viewed through a Head-Mounted Display (HMD). This paper proposed a new way of tracking object in real time and analysis of swimming effect of the hologram using robot. A user study involving 10 users using 50 randomly chosen viewpoints found that the proposed method is as accurate as the state of the art World Locking Tool (WLT) with additional advantage of tracking dynamic objects. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Holograms

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ReFuSeg: Regularized Multi-modal Fusion for Precise Brain Tumour Segmentation 9th

ReFuSeg: Regularized Multi-modal Fusion for Precise Brain T...

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9th International Workshop on Brain Lesion workshop, BrainLes 2023 and 3rd Stroke Workshop on Imaging and Treatment CHallenges, SWITCH 2023 Held in Conjunction with 26th Medical Image Computing and computer Assisted Intervention, MICCAI 2023

作者： Kasliwal, Aditya Sagaram, Sankarshanaa Srivastava, Laven Seth, Pratinav Khan, Adil Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Mechanical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031761591

Semantic segmentation of brain tumours is a fundamental task in medical image analysis that can help clinicians in diagnosing the patient and tracking the progression of any malignant entities. Accurate segmentation of brain lesions is essential for medical diagnosis and treatment planning. However, failure to acquire specific MRI imaging modalities can prevent applications from operating in critical situations, raising concerns about their reliability and overall trustworthiness. This paper presents a novel multi-modal approach for brain lesion segmentation that leverages information from four distinct imaging modalities while being robust to real-world scenarios of missing modalities, such as T1, T1c, T2, and FLAIR MRI of brains. Our proposed method can help address the challenges posed by artifacts in medical imagery due to data acquisition errors (such as patient motion) or a reconstruction algorithm’s inability to represent the anatomy while ensuring a trade-off in accuracy. Our proposed regularization module makes it robust to these scenarios and ensures the reliability of lesion segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantic Segmentation

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