Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 32 for “"Vision Transformers"”.
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Searching for Efficient Multi-Stage Vision Transformers
Vision Transformer (ViT) demonstrates that Transformer for natural language processing can be applied to image classification tasks and result in comparable performance to convolutional neural networks (CNN), which have been studied in computer vision for years. This naturally raises the question …
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Estimating diurnal patterns of land surface temperature using vision transformers and satellite images
… such as climatic zones and elevation. Built on a Vision Transformer (ViT) architecture with a Masked Autoencoder strategy, DayView directly addresses three core challenges: (1) estimating diurnal cycles from sparse observations, (2) incorporating environmental context to refine fluctuation …
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Forecasting and Modelling Space Weather with Deep Learning Methods
… and model space weather conditions. Firstly, vision transformers are used to forecast solar wind speed from solar EUV images, with improvements over previous work. Secondly, solar irradiance is forecast using pre-trained vision transformers that consume nine solar EUV/UV image channels, with …
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Next generation tools for smart electron microscopy
… of high-resolution rescanning, cutting-edge vision models, incorporation of 3D information and vision transformers for improved neuronal segmentation and pipeline speedup. Our goal is to develop tools that improve the existing SmartEM pipeline, making it more versatile and effective for …
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Object Detection Using Vision Transformed EfficientDet
… approach for object detection by integrating Vision Transformers (ViT) into the EfficientDet architecture. The field of computer vision, encompassing artificial intelligence, focuses on the interpretation and analysis of visual data. Recent advancements in deep learning, particularly …
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Applied Plankton Image Classification for Imaging FlowCytobot Data
… by the IFCB - Convolutional Neural Nets (CNNs), Vision Transformers (ViT), and self-supervised learning (MAE). The benefits and downsides of each model are analyzed and discussed for future IFCB operators to process their data using the methods that best align with their research questions, along …
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Towards Effective Theories for Deep Learning and Beyond
… made across multiple domains such as computer vision, natural language processing, and Reinforcement learning. However, the theoretical understanding of its success is limited, and its behavior constantly defies our traditional theoretical understanding of machine learning. We will present our …
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Trustworthy and Efficient Knowledge Sharing across Tasks in Deep Neural Networks
… methods on convolutional neural networks and vision transformers across multiple datasets. Our proposed methods consistently outperform popular baseline methods in terms of accuracy and efficiency.
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An investigation into the use of ConvNext within IICS/IIDS framework for person Re-ID
… of ConvNeXt, a CNN-based network inspired by vision transformers, into the Intra and Inter Camera Similarity (IICS) and Intra and Inter Domain Similarity (IIDS) frameworks for unsupervised person Re-ID. Building upon IICS/IIDS framework that generates pseudo labels through intra and inter …
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Self-supervised Representation Learning in Computer Vision and Reinforcement Learning
… a new method based on hyperbolic embeddings, vision transformers and contrastive loss. We demonstrate the advantage of hyperbolic space over the widely used Euclidean space for metric learning. The method outperforms the current state-of-the-art by a significant margin.
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MODEL ADAPTATION FOR EDGE AI
… are inadequate for emerging workloads like transformers. We explore minifloats, reduced-precision floating-point formats that significantly reduce the memory footprint while offering greater flexibility due to their dynamic range. Finally, models such as Vision Transformers (ViTs) struggle …
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Adversarial robustness without perturbations
… model, and is in fact highly peformant on modern vision transformers that natively use smooth GeLU over piecewise linear ReLUs. On ImageNet-1K, Gradient Norm regularization achieves more than 90% of the performance of state-of-the-art Adversarial Training with PGD-3 (52% vs. 56%) with 60% of the …
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Towards a Reliable Deep Learning Framework for Prostate Cancer Diagnosis using Ultrasound
… performance on noisy ultrasound data. We explore vision transformers for efficient feature extraction, followed by noise-resistant fine-tuning with multiple-instance learning (MIL). We then propose a novel multi-objective loss function that combines predictions from the extractor and the MIL …
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REPRESENTATION LEARNING FOR VISUAL TASKS: A STUDY OF ATTENTION AND INFORMATION SELECTION
… utilizes Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Neural Networks (GNNs) and targets improvements in image classification (single and multi-label) and fine-grained image retrieval. Four primary contributions are detailed: (1) CNN2Graph, a hybrid CNN-GNN …
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Towards Accurate and Fair Deepfake Detection
… the detection of deepfake videos using a pair of vision transformers pre-trained by a self-supervised masked autoencoding setup. Our method consists of two distinct components, one of which focuses on learning spatial information from individual RGB frames of the video, while the other learns …
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Towards Secure and Resilient Machine Learning Systems
… beyond traditional domains such as computer vision (CV) and natural language processing (NLP). One of the most significant breakthroughs is the development of transformer models, which leverage the attention mechanism to achieve state-of-the-art performance across various tasks. Transformers …
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