Sökning: "utvärdera ett objekt"

Visar resultat 1 - 5 av 112 uppsatser innehållade orden utvärdera ett objekt.

  1. 1. EMONAS : Evolutionary Multi-objective Neuron Architecture Search of Deep Neural Network

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

    Författare :Jiayi Feng; [2023]
    Nyckelord :DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Embedded Systems; DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Inbyggda system;

    Sammanfattning : Customized Deep Neural Network (DNN) accelerators have been increasingly popular in various applications, from autonomous driving and natural language processing to healthcare and finance, etc. However, deploying them directly on embedded system peripherals within real-time operating systems (RTOS) is not easy due to the paradox of the complexity of DNNs and the simplicity of embedded system devices. LÄS MER

  3. 2. Assessing the Efficiency of COLMAP, DROID-SLAM, and NeRF-SLAM in 3D Road Scene Reconstruction

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Marcus Ascard; Farjam Movahedi; [2023]
    Nyckelord :3D reconstruction; Visual SLAM; Pose evaluation; Point cloud evaluation; Road scenes; Technology and Engineering;

    Sammanfattning : 3D reconstruction is a field in computer vision which has evolved rapidly as a result of the recent advancements in deep learning. As 3D reconstruction pipelines now can run in real-time, this has opened up new possibilities for teams developing Advanced Driver Assistance Systems (ADAS), which rely on the camera system of the vehicle to enhance the safety and driving experience. LÄS MER

  4. 3. Recommending digital books to children : Acomparative study of different state-of-the-art recommendation system techniques

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

    Författare :Malvin Lundqvist; [2023]
    Nyckelord :Recommendation Systems; Collaborative Filtering; Matrix Factorization; Multi-Layer Perceptron; Neural Network-based Collaborative Filtering; Implicit Feedback; Deep Learning; Term Frequency-Inverse Document Frequency; Rekommendationssystem; Kollaborativ filtrering; Matrisfaktorisering; Flerlagersperceptron; Neurala nätverksbaserad kollaborativ filtrering; Implicit data; Djupinlärning; Termfrekvens med omvänd dokumentfrekvens;

    Sammanfattning : Collaborative filtering is a popular technique to use behavior data in the form of user’s interactions with, or ratings of, items in a system to provide personalized recommendations of items to the user. This study compares three different state-of-the-art Recommendation System models that implement this technique, Matrix Factorization, Multi-layer Perceptron and Neural Matrix Factorization, using behavior data from a digital book platform for children. LÄS MER

  5. 4. 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

  7. 5. Meta-Pseudo Labelled Multi-View 3D Shape Recognition

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

    Författare :Fehmi Ayberk Uçkun; [2023]
    Nyckelord :3D shape recognition; 3D object classification; 3D shape retrieval; 3D object retrieval; Automatic labelling; Semi-supervised learning; Pseudo labelling; Meta Pseudo Labelling; Multi-View Convolutional Neural Networks; Shape descriptors; Multi-view representations; Deeplearning; 3D-formigenkänning; 3D-objektklassificering; 3D-formhämtning; Hämtning av 3D-objekt; Automatisk märkning; Halv-vägledd lärning; Pseudomärkning; Meta Pseudo-märkning; Multi-View Faltningsnät; Formbeskrivningar; Multi-view representation; Djupinlärning;

    Sammanfattning : The field of computer vision has long pursued the challenge of understanding the three-dimensional world. This endeavour is further fuelled by the increasing demand for technologies that rely on accurate perception of the 3D environment such as autonomous driving and augmented reality. LÄS MER