Sökning: "semantiska modeller"

Visar resultat 1 - 5 av 21 uppsatser innehållade orden semantiska modeller.

  1. 1. Exploring the Depth-Performance Trade-Off : Applying Torch Pruning to YOLOv8 Models for Semantic Segmentation Tasks

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Xinchen Wang; [2024]
    Nyckelord :Deep Learning; Semantic segmentation; Network optimization; Network pruning; Torch Pruning; YOLOv8; Network Depth; Djup lärning; Semantisk segmentering; Nätverksoptimering; Nätverksbeskärning; Fackelbeskärning; YOLOv8; Nätverksdjup;

    Sammanfattning : In order to comprehend the environments from different aspects, a large variety of computer vision methods are developed to detect objects, classify objects or even segment them semantically. Semantic segmentation is growing in significance due to its broad applications in fields such as robotics, environmental understanding for virtual or augmented reality, and autonomous driving. LÄS MER

  3. 2. Image-Guided Zero-Shot Object Detection in Video Games : Using Images as Prompts for Detection of Unseen 2D Icons

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Axel Larsson; [2023]
    Nyckelord :Computer Vision; Deep learning; Machine learning; Object detection; Zeroshot; Datorseende; Djupinlärning; Maskininlärning; Objektdetektering; Zero-shot;

    Sammanfattning : Object detection deals with localization and classification of objects in images, where the task is to propose bounding boxes and predict their respective classes. Challenges in object detection include large-scale annotated datasets and re-training of models for specific tasks. LÄS MER

  4. 3. Using a Deep Generative Model to Generate and Manipulate 3D Object Representation

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Yu Hu; [2023]
    Nyckelord :Neural networks; point cloud; 3D shape generation; 3D shape manipulation; classification; Neurala nätverk; punktmoln; generering av 3D-former; manipulation av 3Dformer; klassificering;

    Sammanfattning : The increasing importance of 3D data in various domains, such as computer vision, robotics, medical analysis, augmented reality, and virtual reality, has gained giant research interest in generating 3D data using deep generative models. The challenging problem is how to build generative models to synthesize diverse and realistic 3D objects representations, while having controllability for manipulating the shape attributes of 3D objects. LÄS MER

  5. 4. Online Unsupervised Domain Adaptation

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Theodoros Panagiotakopoulos; [2022]
    Nyckelord :Unsupervised Domain Adaptation; Continual Learning; Curriculum Learning; Clear2Rain; Self-Supervised Learning; Semantic Segmentation; Transfer Learning; Online Learning; Unsupervised Domain Adaptation; Kontinuerligt lärande; Curriculum Learning; Clear2Rain; Self-Supervised Learning; Semantisk Segmentering; Transfer Learning; Online Learning;

    Sammanfattning : Deep Learning models have seen great application in demanding tasks such as machine translation and autonomous driving. However, building such models has proved challenging, both from a computational perspective and due to the requirement of a plethora of annotated data. LÄS MER

  7. 5. Basil-GAN

    Master-uppsats, KTH/Matematisk statistik

    Författare :Jonatan Risberg; [2022]
    Nyckelord :GAN; mathematical statistics; deep neural networks; generative models; latent space exploration; sequential data; GAN; matematisk statistik; djupa neurala nätverk; generativa modeller; utforskning av latenta rum; sekventiell data;

    Sammanfattning : Developments in computer vision has sought to design deep neural networks which trained on a large set of images are able to generate high quality artificial images which share semantic qualities with the original image set. A pivotal shift was made with the introduction of the generative adversarial network (GAN) by Goodfellow et al.. LÄS MER