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Showing result 1 - 5 of 42 essays matching the above criteria.
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1. Automatic Semantic Segmentation of Indoor Datasets
University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskapAbstract : Background: In recent years, computer vision has undergone significant advancements, revolutionizing fields such as robotics, augmented reality, and autonomoussystems. Key to this transformation is Simultaneous Localization and Mapping(SLAM), a fundamental technology that allows machines to navigate and interactintelligently with their surroundings. READ MORE
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2. Key Sentence Extraction From CRISPR-Cas9 Articles Using Sentence Transformers
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : The annotation of CRISPR-related articles and extraction of key content has traditionally relied on manual efforts. Manual annotation is error-prone and timeconsuming. This thesis presents an alternative approach using transfer learning and pre-trained models based on the Transformer architecture. READ MORE
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3. Automatic Semantic Role Labelling (SRL) in Swedish
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : In this paper, using deep learning networks, the first end-to-end semantic role labelling model (SRL) has been developed for Swedish texts. This Swedish SRL model can, with a given Swedish sentence, perform trigger identification, frame classification and argument extraction tasks automatically in a series. READ MORE
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4. Sim2Real: Generating synthetic images from industry CAD models with domain randomization
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Deep learning methods for computer vision applications require massive visual data for model training. Although it is possible to utilize public datasets such as ImageNet, MS COCO, and CIFAR-100, it becomes problematic when there is a need for more task-specific data when new training data collection typically is needed. READ MORE
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5. Cell Identification from Microscopy Images using Deep Learning on Automatically Labeled Data
University essay from Lunds universitet/Institutionen för elektro- och informationsteknikAbstract : In biology, cell counting provides a fundamental metric for live-cell experiments. Unfortunately, most researchers are constrained to using tedious and invasive methods for counting cells. Automatic identification of cells in microscopy images would therefore be a valuable tool for such researchers. READ MORE
