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 40 for “"Instance Segmentation"”.
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Amodal video instance segmentation
… models for the task of semantic amodal instance level video object segmentation, i.e., the task to delineate objects and their occluded parts in video data. We propose Amodal-Net with three improvements: First, we leverage temporal information. Specifically, we employ 3D convolutions and …
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Robust online video instance segmentation with track queries
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-08-01
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Data-Efficient Learning in Image Synthesis and Instance Segmentation
… learning for the tasks of image synthesis and instance segmentation. We first propose a method of high-quality and diverse image generation from finetuning to only 5-100 images. Our method factors a pretrained model into a small but highly expressive weight space for finetuning, which …
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Spatio-temporal human action detection and instance segmentation in videos
… videos and also addresses the space-time action instance segmentation problem. Both action detection and instance segmentation play vital roles in video understanding. Firstly, we propose a novel human action detection approach based on a frame-level deep feature representation combined with a …
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Cross-classification clustering : multi-object tracking technique for 3-D instance segmentation in connectomics
… tracking of multiple objects, such as segmentation of videos and medical imagery.
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G A N mask R-CNN : instance semantic segmentation benefits from generative adversarial networks
In designing instance segmentation ConvNets that reconstruct masks, segmentation is often taken as its literal definition -assigning label to every pixel- for defining the loss functions. That is, using losses that compute the difference between pixels in the predicted (reconstructed) mask and the …
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Strategies for the Characterization and Virtual Testing of SLM 316L Stainless Steel
… time consuming, traditionally requiring manual segmentation of imaged features. Two Python-based image analysis tools are developed here to automate the instance segmentation of manufacturing defects and subgranular cell features commonly found in SLM 316L stainless steel (SS) for quantitative …
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Addressing Occlusion in Panoptic Segmentation
… in the context of the newly introduced panoptic segmentation task. The panoptic segmentation task combines both semantic and instance segmentation to perform labelling of the entire image. The novel classification method replaces the object detection pipeline in UPSNet, a Mask R-CNN based design …
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DEEP MULTI-TASK LEARNING FOR FACE AND HUMAN ANALYSIS
… tasks, i.e. human parsing and human instance segmentation, can be addressed within one model to achieve fine-grained human analysis in images. Built on the commonly used deep multi-task learning architecture, we further model the interactions and relations among tasks in multi-task …
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Advancing Chart Question Answering with Robust Chart Component Recognition
… is particularly effective for complex instance segmentation tasks that involve a wide variety of class objects with unique visual structures. It utilizes an end-to-end transformer architecture, which enhances its ability to handle the intricacies of diverse and distinct object features. …
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Towards Comprehensive Visual Understanding via Deep Neural Networks
… Domain Adaptation (UDA), Exemplar-guided Video Segmentation (EVS), Video Instance Segmentation (VIS), Video Semantic Segmentation (VSS), Video Panoptic Segmentation (VPS), and Human-Object Interaction Detection (HOI Detection).
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Geometrical Models for 2D Morphometry in Bioimages
… We show that it can be used as an alternative instance segmentation method with state-of-the-art results, and packaged it as a plugin for the popular microscopy image analysis platform napari to facilitate its use. We also explored the use of geometrical model parameters as direct measures of …
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Unsupervised Machine Learning Application for the Identification of Kimberlite Ore Facie using Convolutional Neural Networks and Deep Embedded Clustering
… R-CNN) model with a ResNet 101 CNN backbone for instance segmentation of individual rocks from each image. These individual rock images are extracted and used for the second phase of the modelling pipeline - utilizing an unsupervised clustering method known as Convolutional Deep Embedded …
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AI-POWERED DIGITAL TWIN MODELING USING LARGE HIGHWAY INFRASTRUCTURE DATASETS
… assessment, earth surface feature detection by instance segmentation and dynamic FEM simulations for structural behavior under environmental and operational loads.In this study, machine learning models were trained on extensive historical highway datasets to predict geotechnical properties, …
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Visual and auditory scene parsing
… for the research community to solve semantic and instance segmentation tasks. Then I investigate the challenge of parsing a large number of object categories in the wild. An open vocabulary scene parsing model which combines a convolutional neural network with a structured knowledge graph is …
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National crop field delineation for the United States
… in matching predicted individual field instances with the ground truth instances (measured by mean instance Intersection over Union, or mIoU) was around 0.5. This lower instance score was largely due to the post-processing step, which converts the model’s probability predictions into …
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Robotic Pruning for Indoor Indeterminate Plants
… trajectory based on machine learning-derived segmentation. This adjustment significantly increases the detection area of plant features, improving identification accuracy and efficiency. Building on foreground isolation and instance segmentation, the thesis then presents an automated method …
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Tree-Level Forest Monitoring with Artificial Intelligence Using Neural Networks and High-Resolution Imagery
… on tree detection and not individual tree segmentation. In this collection of studies, I utilize sub-meter multispectral imagery with an instance segmentation convolutional neural network (CNN) to how variations of the training data and model architecture impact the model's ability to …
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Model Acceleration for Efficient Deep Learning Computing
… GPU while achieving slightly better zero-shot instance segmentation performance than the state-of-the-art model. For conditional image generation, our approach achieves 52x computational cost reduction without performance degradation.
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