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.
Results
Showing 1 to 20 of 57 for “"Computer vision tasks"”.
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Attention mechanism in deep neural networks for computer vision tasks
… mechanism has been widely adopted in solving computer vision tasks by guiding deep neural networks (DNNs) to focus on specific image features for better understanding the semantic information of the image. However, the attention mechanism is not only capable of helping DNNs understand …
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Advances in Stimulated Raman Scattering Microscopy via Deep Learning
… of deep learning platforms for a wide variety of computer vision tasks. In this work I document my contributions in integrating SRS microscopy and deep learning towards advancing the capability to study biological systems. Specifically, deep learning will be shown to address technical limitations …
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Acceleration of real-time face recognition pipeline on heterogeneous hardware platforms
… unprecedented results in previously unsolvable computer vision tasks. Face recognition, one of the critical computer vision tasks, also sees breakthrough in terms of accuracy. This thesis presents an accelerated and optimized end-to-end face recognition pipeline. Such a pipeline consists of …
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A computer vision framework towards automated scene understanding & analysis
… intelligence and the increased capability of computer hardware have significantly advanced the field of computer vision – a field of study which enables computers to “see” and extract meaningful information from visual inputs, similar to human perception. A prominent application area within …
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Deep learning in sequential data analysis
… has achieved great success in recent years in computer vision and its related areas. For core computer vision tasks such as image classification, image semantic segmentation, image super-resolution, and object detection from images, deep learning based methods outperform various traditional …
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Learning visual tasks with selective attention
… image can significantly improve performance in computer vision tasks by eliminating irrelevant information from the rest of the input image, and by breaking down complex scenes into simpler and more familiar sub-components. We show that a framework for identifying multiple task-relevant regions …
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A Semantics-driven Methodology for High-quality Image Annotation
In recent years, the field of computer vision has achieved significant advancements, largely driven by high-quality datasets. However, current models struggle to provide the responses that humans expect, especially when addressing complex computer vision tasks. One primary reason for this …
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Characterizing the Energy Requirement of Computer Vision
… energy consumption in neural network learning on Computer Vision tasks. I catalogued the effects of various adjust adjustment from simple batch size adjustment to more complicated hardware configuration (such as power capping). Findings include that adjusting from single precision model to a mixed …
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Using the Internet for object image retrieval and object image classification
… and videos. This content is valuable for many computer vision tasks. In this thesis, two case studies are conducted to show how to leverage information from the Internet for two important computer vision tasks: object image retrieval and object image classification. Case study 1 is on object …
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Reasoning about objects under full occlusion
… learning models can outperform humans on certain tasks, most of them generalize poorly across domains and cannot reason about complex scenes. In this paper, we attempt to resolve this shortcoming by incorporating a physics engine as a prior for scene understanding. We test our approach on two …
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Joint spatial and layer attention for convolutional networks
… the effectiveness of this approach on two computer vision tasks: (i) image-based six degree of freedom camera pose regression and (ii) indoor scene classification. Empirically, we show that combining the “what” and “where” aspects of attention improves network performance on both tasks. We …
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Frequency-domain deconvolution in deep learning
… of a novel approach to deep learning in computer vision tasks: the frequency-domain deconvolution operation. Recognizing the unparalleled success of convolutional neural networks (CNNs) in the realm of computer vision, we critically evaluate the performance and computational demands of …
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Novel feature construction technique for detecting anomalous faces and evaluating style transfer methods
… still necessary like hand-crafted features for computer vision tasks. We demonstrate this in two different domain problems – Anomaly Detection and Style Transfer. We present feature aggregation techniques and also quantitative evaluation procedure for these tasks. For anomaly detection, we …
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Stacked dense-hourglass networks for human pose estimation
… (CNNs) are driving major advances in many computer vision tasks, including the problem of 2D single-person pose estimation. For this task, the Stacked Hourglass Networks (Stack-HgNets) is one of the state-of-the-art architecture that uses residual modules extensively as the basic building …
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Modelling non-linearity in 3D shapes: A comparative study of Gaussian process morphable models and variational autoencoders for 3D shape data
… reliability is crucial for medical imaging and computer vision tasks; however, prior to modelling, the non-linearity in the data is not often considered. The study provides a framework to identify the presence of non-linearity in using principal component analysis (PCA) and autoencoders (AE) …
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Deep learning-based approaches for depth and 6-DoF pose estimation
… thesis, we investigated two important geometric vision problems, namely, depth estimation from a single RGB image, and 6-DoF object pose estimation from a partial point cloud. Geometric vision problems are concerned with extracting information (e.g. depth, agent trajectory, 3D structure, 6-DoF …
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Deep learning of visual features with limited supervision.
… have led to significant improvements in various computer vision tasks. Typically, deep learning models require large-scale labeled data for training, but obtaining annotations is costly in fields like medical imaging and underwater imaging. This dissertation explores methods for learning deep …
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Recurrent convolutional neural networks as models of biological object recognition
Deep feedforward neural network models of vision dominate in both computational neuroscience and engineering. However, the primate visual system contains abundant recurrent connections. In this thesis, we investigate the addition of recurrent connections to the popular framework of convolutional …
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Data-Efficient Learning in Image Synthesis and Instance Segmentation
… achieve remarkable performance on a variety of computer vision tasks, but frequently require large, well-balanced training datasets to achieve high-quality results. Data-efficient performance is critical for downstream tasks such as automated driving or facial recognition. We propose two methods …
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The Removal of False Signals from Convolutional Neural Networks
… comparable to human experts in various computer vision tasks. However, despite this comparable performance, the decision-making process differs significantly between convolutional neural networks and human experts. Whereas human decisions are guided by causal inference, convolutional …
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