Sökning: "multi-modality fusion"

Hittade 3 uppsatser innehållade orden multi-modality fusion.

  1. 1. Instance segmentation using 2.5D data

    Master-uppsats, Linköpings universitet/Institutionen för systemteknik

    Författare :Jonathan Öhrling; [2023]
    Nyckelord :instance segmentation; multi-modality; segmentation; multi-modality fusion; CNN; RGBD; ToF; Mask R-CNN; RTMDet; MMDetection; COCO; NYUDepth;

    Sammanfattning : Multi-modality fusion is an area of research that has shown promising results in the domain of 2D and 3D object detection. However, multi-modality fusion methods have largely not been utilized in the domain of instance segmentation. LÄS MER

  3. 2. There’s a Microwave in the Hallway

    Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

    Författare :Yasmeen Emampoor; [2022-04-20]
    Nyckelord :embodied question answering; visual question answering; multi-modality; information fusion;

    Sammanfattning : Embodied Question Answering (EQA) is a task in which an agent situated in virtual environment navigates from its current position to an object (Navigation), and then answer a question about it (Visual Question Answering, VQA), for example “What color is the table in the table in the kitchen?” This project examines how an agent modelled as a deep neural network uses semantic information from its language model and visual information to answer questions in the second task. This is important since due to the regular nature of the task and the dataset it could be that the model is answering questions purely based on general semantic information from its language model (tables are frequently brown) and not relying on the visual scene, a phenomenon that is commonly known as hallucinating. LÄS MER

  4. 3. Multimodal Machine Learning in Human Motion Analysis

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

    Författare :Jia Fu; [2022]
    Nyckelord :Multimodal machine learning; Modal fusion; Human motion classification; Multimodal maskininlärning; Modal fusion; Mänsklig rörelseklassificering;

    Sammanfattning : Currently, most long-term human motion classification and prediction tasks are driven by spatio-temporal data of the human trunk. In addition, data with multiple modalities can change idiosyncratically with human motion, such as electromyography (EMG) of specific muscles and respiratory rhythm. LÄS MER