Sökning: "Vision-Transformers"
Visar resultat 1 - 5 av 15 uppsatser innehållade ordet Vision-Transformers.
1. Mutual Enhancement of Environment Recognition and Semantic Segmentation in Indoor Environment
Master-uppsats,Sammanfattning : Background:The dynamic field of computer vision and artificial intelligence has continually evolved, pushing the boundaries in areas like semantic segmentation andenvironmental recognition, pivotal for indoor scene analysis. This research investigates the integration of these two technologies, examining their synergy and implicayions for enhancing indoor scene understanding. LÄS MER
2. Classifying femur fractures using federated learning
Master-uppsats, Linköpings universitet/Statistik och maskininlärningSammanfattning : The rarity and subtle radiographic features of atypical femoral fractures (AFF) make it difficult to distinguish radiologically from normal femoral fractures (NFF). Compared with NFF, AFF has subtle radiological features and is associated with the long-term use of bisphosphonates for the treatment of osteoporosis. LÄS MER
3. Few-Shot Learning for Quality Inspection
Uppsats för yrkesexamina på avancerad nivå, Högskolan i Halmstad/Akademin för informationsteknologiSammanfattning : The goal of this project is to find a suitable Few-Shot Learning (FSL) model that can be used in a fault detection system for use in an industrial setting. A dataset of Printed Circuit Board (PCB) images has been created to train different FSL models. LÄS MER
4. Learning Embeddings for Fashion Images
Master-uppsats, Linköpings universitet/DatorseendeSammanfattning : Today the process of sorting second-hand clothes and textiles is mostly manual. In this master’s thesis, methods for automating this process as well as improving the manual sorting process have been investigated. LÄS MER
5. Using Machine Learning to Optimize Near-Earth Object Sighting Data at the Golden Ears Observatory
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : This research project focuses on improving Near-Earth Object (NEO) detection using advanced machine learning techniques, particularly Vision Transformers (ViTs). The study addresses challenges such as noise, limited data, and class imbalance. LÄS MER