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检索条件"任意字段=IEEE Symposium on Large Data Analysis and Visualization"

共 15522 条记录，以下是161-170 订阅

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AutoGaze: A Very Initial Exploration in A SAM2-based Pipeline for Automated Eye-Object Interaction analysis in First-Person Videos

AutoGaze: A Very Initial Exploration in A SAM2-based Pipelin...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee Conference on

作者： Qing Zhang Yifei Huang Jun Rekimoto The University of Tokyo Sony CSL Kyoto

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

This paper presents a novel automated workflow for analyzing eye-tracking data in first-person videos. Our system uses the Segment Anything Model 2 (SAM2) to segment and track objects, correlating them with gaze information to provide a detailed understanding of visual attention and hand-object interactions. The pipeline outputs a structured JSON file containing rich information about gaze behavior, object interactions, and their temporal relationships. We demonstrate the system’s effectiveness in a life science operational context and compare its performance with a multimodal large Language Model (LLM) for automated annotation. Our findings highlight the potential of combining advanced computer vision techniques with LLMs for comprehensive and scalable analysis of gaze behavior in real-world scenarios.

关键词： visualization Solid modeling Three-dimensional displays Pipelines Gaze tracking Virtual reality User interfaces Life sciences Trajectory Videos

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Brain-Adapter: Enhancing Neurological Disorder analysis with Adapter-Tuning Multimodal large Language Models

Brain-Adapter: Enhancing Neurological Disorder Analysis with...

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ieee International symposium on Biomedical Imaging

作者： Jing Zhang Xiaowei Yu Yanjun Lyu Lu Zhang Tong Chen Chao Cao Yan Zhuang Minheng Chen Tianming Liu Dajiang Zhu Computer Science and Engineering The University of Texas at Arlington Arlington TX USA Department of Computer Science Indiana University Indianapolis IN USA School of Computing The University of Georgia Athens GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Understanding brain disorders is crucial for accurate clinical diagnosis and treatment. Recent advances in Multimodal large Language Models (MLLMs) offer a promising approach to interpreting medical images with the support of text descriptions. However, previous research has primarily focused on 2D medical images, leaving richer spatial information of 3D images under-explored, and single-modality-based methods are limited by overlooking the critical clinical information contained in other modalities. To address this issue, this paper proposes Brain-Adapter, a novel approach that incorporates an extra bottleneck layer to learn new knowledge and instill it into the original pre-trained knowledge. The major idea is to incorporate a lightweight bottleneck layer to train fewer parameters while capturing essential information and utilize a Contrastive Language-Image Pre-training (CLIP) strategy to align multimodal data within a unified representation space. Extensive experiments demonstrated the effectiveness of our approach in integrating multimodal data to significantly improve the diagnosis accuracy without high computational costs, highlighting the potential to enhance real-world diagnostic workflows.

关键词： Neurological diseases Training Solid modeling Three-dimensional displays Accuracy large language models Magnetic resonance imaging data models Clinical diagnosis Medical diagnostic imaging

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Development of an Image Recognition Model Using an Image Search Function Based on Multiple Pre-Trained Models

Development of an Image Recognition Model Using an Image Sea...

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ieee/SICE International symposium on System Integration

作者： Hirotada Kuragane Takeshi Sasaki Graduate School of Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Machine learning is widely utilized for data analysis and decision-making, with supervised and unsupervised learning being the primary approaches. However, models suffer from overfitting, where they become overly adapted to the training data. To address this issue, semi-supervised learning has been employed. Semi-supervised learning is an effective technique for dealing with large datasets that are difficult to label, but it faces limitations in fields where ensuring data diversity and quantity is challenging. This paper proposes a robust image recognition model utilizing image search functions from Google. The proposed model improves accuracy by utilizing the order of search results to collect a variety of data and evaluating their reliability. In this paper, the order of search results is defined as "image search depth" to measure the correlation between reliability and accuracy. While it is easy to collect large amounts of data from Google through automated methods, there is a risk that unrelated data could be included, potentially affecting the model’s accuracy. To address this issue, the model is trained with automated preprocessing. As part of this preprocessing, inference is performed on all images in the dataset using multiple pre-trained models that were trained on randomly selected images from the dataset to compute predictions. Images with the prediction above a certain threshold are selected as training data to enhance the final model ’s accuracy. To assess the contribution of preprocessing to accuracy improvement, we calculate accuracy by varying the number of parallel pre-trained models and the threshold values. Furthermore, the final model is evaluated using CIFAR100 to objectively demonstrate its performance. The results indicate that image search depth does not contribute to model accuracy, while the number of parallel pre-trained models and the threshold significantly impact accuracy.

关键词： Analytical models Accuracy Correlation Computational modeling Training data Semisupervised learning Predictive models data models Internet Reliability

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A Reality Check on Pre-training for Exemplar-free Class-Incremental Learning

A Reality Check on Pre-training for Exemplar-free Class-Incr...

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ieee Workshop on Applications of Computer Vision (WACV)

作者： Eva Feillet Adrian Popescu Céline Hudelot CEA List Universite Paris-Saclay Palaiseau France CentraleSupélec MICS Universite Paris-Saclay Gif-sur-Yvette France

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Exemplar-free class-incremental learning (EFCIL) aims to classify streaming data without storing examples from the past. Recent EFCIL works suggest that (i) models pre-trained with large amounts of data should be used to initialize learning, (ii) self-supervised learned transformers generalize better than supervised convolutional models, (iii) adding generated data to the pre-training dataset can improve incremental accuracy. In this article, we question the above assertions by comprehensively evaluating various initial training strategies combined with four EFCIL algorithms using four large-scale datasets. Our results indicate that: (i) Pre-trained models are preferable when the domain of the incremental classification task is well represented in the pre-training datasets, but training with initial data remains useful when the domain shift is significant, (ii) supervised convolutional networks remain competitive, particularly when improving representation transferability using data augmentation or a projector, (iii) adding classes from an external dataset to train the initial model boosts performance when the initial set of classes is small but has a limited effect otherwise, (iv) additional classes generated with a diffusion model are not necessarily more useful than a well-chosen set of ImageNet classes to improve model transferability. We provide a nuanced analysis of these results and formulate recommendations to facilitate the practical adoption of EFCIL algorithms.

关键词： Training visualization Accuracy Feature extraction data augmentation Transformers data models Classification algorithms Streams Residual neural networks

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Efficient Human-in-the-Loop Pancreatic Tumor Annotation via large-Scale Pre-Trained Model with Adaptive Post-Processing

Efficient Human-in-the-Loop Pancreatic Tumor Annotation via ...

引用

ieee International symposium on Biomedical Imaging

作者： Xinze Zhou Yuxuan Zhao Chuntung Zhuang Dexin Yu Alan Yuille Zongwei Zhou Johns Hopkins University Qilu Hospital of Shandong University

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Deep learning has significantly impacted fields like medical image analysis. However, the effectiveness of supervised learning models depends on large volumes of annotated data. Annotating medical images, especially for tumor regions, is costly and time-intensive. To overcome this limitation, this paper propose a novel framework to accelerate tumor annotation, producing a unique dataset with pancreatic lesions labeled in 300 CT scans from multiple centers. Traditional annotation methods would require an experienced radiologist around 216 hours for this task, whereas our framework completed it in 57 hours, achieving similar or even improved annotation quality. This success stems from two core elements: (1) pre-training the model on a large-scale multi-organ dataset, then fine-tuning it on the target domain before using the fine-tuned model to generate preliminary labels, and (2) adaptive post-processing based on statistical insights from an extensive in-house JHH dataset and clinical evidence. More importantly, our framework could help both the AI model and annotation processes, substantially reducing the annotation costs required to produce large-scale datasets for tumor detection and segmentation tasks for other organs, such as liver and kidney. With our framework, we introduce AbdomenAtlas2.0-Mini, a dataset of 300 CT scans with high-quality voxel-level pancreatic lesions annotations.

关键词： Deep learning Adaptation models Image analysis Annotations Computed tomography Supervised learning Streaming media Human in the loop Lesions Biomedical imaging

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Skyeyes: Ground Roaming using Aerial View Images

Skyeyes: Ground Roaming using Aerial View Images

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ieee Workshop on Applications of Computer Vision (WACV)

作者： Zhiyuan Gao Wenbin Teng Gonglin Chen Jinsen Wu Ningli Xu Rongjun Qin Andrew Feng Yajie Zhao University of Southern California Institute for Creative Technologies The Ohio State University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Integrating aerial imagery-based scene generation into applications like autonomous driving and gaming enhances realism in 3D environments, but challenges remain in creating detailed content for occluded areas and ensuring real-time, consistent rendering. In this paper, we introduce Skyeyes, a novel framework that can generate photoreal-istic sequences of ground view images using only aerial view inputs, thereby creating a ground roaming experience. More specifically, we combine a 3D representation with a view consistent generation model, which ensures coher-ence between generated images. This method allows for the creation of geometrically consistent ground view images, even with large view gaps. The images maintain improved spatial-temporal coherence and realism, enhancing scene comprehension and visualization from aerial perspectives. To the best of our knowledge, there are no publicly avail-able datasets that contain pairwise geo-aligned aerial and ground view imagery. Therefore, we build a large, synthetic, and geo-aligned dataset using Unreal Engine. Both qualitative and quantitative analyses on this synthetic dataset display superior results compared to other leading syn-thesis approaches. See the project page for more results: ***/website-skyeyes/.

关键词： Solid modeling visualization Three-dimensional displays Statistical analysis Noise Spatial coherence Diffusion models Rendering (computer graphics) Real-time systems Synthetic data

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Conceptual In-Context Learning and Chain of Concepts: Solving Complex Conceptual Problems Using large Language Models

Conceptual In-Context Learning and Chain of Concepts: Solvin...

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Computational Intelligence in Natural Language Processing and Social Media (CI-NLPSoMe), ieee symposium on

作者： Nishtha N. Vaidya Thomas A. Runkler Thomas Hubauer Veronika Haderlein-Hoegberg Maja Milicic Brandt Siemens AG Munich Germany Technical University of Munich Germany

ISBN: (数字)9798331508418

ISBN: (纸本)9798331508425

Science and engineering problems fall in the category of complex conceptual problems that require specific conceptual information (CI) like mathllogic-related know-how, process information, or engineering guidelines to solve them. large Language Models (LLMs) are promising agents to solve such complex conceptual problems due to their implications in advancing engineering and science tasks like assisted problem solving. But vanilla LLMs, trained on open-world data, lack the necessary CI. In this work, we specifically explore shallow customization methods (SCMs) of LLMs for solving complex conceptual problems. We propose two novel SCM algorithms for LLMs, to augment LLMs with CI and enable LLMs to solve complex conceptual problems: Conceptual In-Context Learning (C-ICL) and Chain of Concepts (CoC). The problem tackled in this paper is the generation of proprietary data models in the engineering/industry domain based on conceptual information in data modelling guidelines. We evaluate our algorithms on varied sizes of the OpenAI LLMs against four evaluation metrics related to syntactic and semantic correctness, time and cost incurred. The proposed algorithms perform better than currently popular LLM SCMs like In-context Learning (ICL) and Chain of Thoughts (CoT). It was observed that as compared to CoT, response correctness increased by 30.6% and 29.88% for the new SCMs C-ICL and CoC, respectively. Qualitative analysis suggests that the proposed new SCMs activate emergent capabilities in LLMs, previously unobserved in the existing SCMs. They make the problem solving processes more transparent and reduce hallucinations and the tendency of model responses to copy examples from prompts (parroting).

关键词： Measurement Social networking (online) large language models Semantics Syntactics data models Natural language processing Problem-solving Tuning Guidelines

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Ultra-High Order Independent Component analysis for Intrinsic Connectivity Networks in Resting-State Functional Magnetic Resonance Imaging data

Ultra-High Order Independent Component Analysis for Intrinsi...

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ieee International symposium on Biomedical Imaging

作者： Shiva Mirzaeian Kyle M. Jensen Adithya Ram Ballem Vince D. Calhoun Armin Iraji Tri-institutional Center for Translational Research in Neuroimaging and Data Science(TReNDS) Atlanta GA Department of Mathematics and Statistics Georgia State University Atlanta GA Department of Computer Science Georgia State University Atlanta GA Department of Psychology Georgia State University Atlanta GA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Spatial group independent component analysis (sgr-ICA) has become a crucial method to understand brain function in functional magnetic resonance imaging (fMRI) research, especially in resting-state fMRI (rs-fMRI) studies. Early studies using low-order sgr-ICA identified large-scale brain networks, but higher-order sgr-ICA (75–150 components) revealed more refined intrinsic connectivity networks (ICNs). While previous studies employing high-order sgr-ICA models (300 or even 1000 components) were often limited by small datasets, we applied an ultra-high-order ICA model with 500 components to a large rs-fMRI dataset of over 100,000 individuals' selecting approximately 58,000 after quality control. This comprehensive dataset enabled us to delineate the brain into fine-grained intrinsic connectivity networks (ICNs), offering a robust and detailed representation of these networks. We also evaluate functional connectivity differences between healthy people and individuals with schizophrenia. Our results demonstrate that ultra-high-order sgr-ICA provides reliable, precise estimations of ICNs and captures significant group differences between healthy people and schizophrenia patients. These findings highlight the potential of ultra-high-order sgr-ICA for improving rs-fMRI's clinical applications and single-subject analyses.

关键词： Stability criteria Independent component analysis Quality control Organizations Functional magnetic resonance imaging Schizophrenia Brain modeling Reliability Indexes Biomedical imaging

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VSS-SAM: Visual State Space-Enhanced SAM for 3D Medical Image Segmentation

VSS-SAM: Visual State Space-Enhanced SAM for 3D Medical Imag...

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ieee International symposium on Biomedical Imaging

作者： Jinxuan Lyu Xuhao Dong Bin Zhang Shengping Liu Haifeng Wang Dong Liang Yihang Zhou Research Center for Medical AI Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China School of Pharmacy and Bioengineering Chongqing University of Technology Chongqing China Paul C. Lauterbur Research Center for Biomedical Imaging Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The Segment Anything Model (SAM) is a large general segmentation model proposed by Meta, which has shown amazing performance in many natural image segmentation tasks. Recently, MA-SAM has been proposed to transplant SAM model to medical applications. However, such models still has shortcomings in utilizing the three-dimensional information of medical images. In this paper, we proposed a new structure to fully utilizing 3D information on medical images to enhance the performance of SAM, called VSS-SAM. Particularly, the proposed method applied two branches: 1) SAM as an encoder to learn the basic topology relations among slices, and 2) parallel Mamba as second branch to effectively capture long-range spatial dependencies. Finally, a new decoder was proposed to integrate the multi-view feature representations extracted from the two branches and output the final prediction, aiming to achieve optimal segmentation performance. We validated our method on three publicly available datasets. Experimental results show that the segmentation performance of VSS-SAM is significantly better than that of existing methods in multiple data sets.

关键词： Image segmentation visualization Solid modeling Biomedical equipment Three-dimensional displays Feature extraction Topology Organ transplantation Decoding Biomedical imaging

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Driving the Future of Connected Autonomous Mobility with Advanced AI and Multi-Modal data Fusion: Plenary Talk

Driving the Future of Connected Autonomous Mobility with Adv...

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International symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Juraj Gazda Technical University of Košice Košice Slovakia

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

In this talk, we will explore the intersection of connected autonomous vehicles (CAVs), edge computing, multi-agent cooperation, computer vision, and large language models (LLMs) to tackle emerging challenges in intelligent transportation. Edge computing enables decentralized, low-latency processing, essential for real-time decision-making in tasks such as obstacle detection and navigation. Multi-agent cooperation, in this context, focuses on efficient sharing of radio and computational resources, ensuring that vehicles can dynamically allocate bandwidth and processing power to maintain high performance in communication and computation-heavy environments. Computer vision technologies enable vehicles to interpret complex surroundings through advanced scene analysis, enhancing safety and decision-making. Additionally, LLMs contribute to processing large amounts of contextual data, enabling more human-like reasoning and complex decision-making. Together, these technologies offer a holistcapproach to advancing the capabilities of autonomous vehicles, creating more robust, intelligent, and sustainable transportation systems.

关键词： Computer vision Decision making Transportation Radio navigation Real-time systems Computational efficiency Safety Autonomous vehicles Machine intelligence Edge computing

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