Deep group disentangled embedding and network weight generation for visual inspection

    公开(公告)号:US11087174B2

    公开(公告)日:2021-08-10

    申请号:US16580497

    申请日:2019-09-24

    Abstract: A method is provided for visual inspection. The method includes learning, by a processor, group disentangled visual feature embedding vectors of input images. The input images include defective objects and defect-free objects. The method further includes generating, by the processor using a weight generation network, classification weights from visual features and semantic descriptions. Both the visual features and the semantic descriptions are for predicting defective and defect-free labels. The method also includes calculating, by the processor, a cosine similarity score between the classification weights and the group disentangled visual feature embedding vectors. The method additionally includes episodically training, by the processor, the weight generation network on the input images to update parameters of the weight generation network. The method further includes generating, by the processor using the trained weight generation network, a prediction of a test image as including any of defective objects and defect-free objects.

    DEEP GROUP DISENTANGLED EMBEDDING AND NETWORK WEIGHT GENERATION FOR VISUAL INSPECTION

    公开(公告)号:US20200097771A1

    公开(公告)日:2020-03-26

    申请号:US16580497

    申请日:2019-09-24

    Abstract: A method is provided for visual inspection. The method includes learning, by a processor, group disentangled visual feature embedding vectors of input images. The input images include defective objects and defect-free objects. The method further includes generating, by the processor using a weight generation network, classification weights from visual features and semantic descriptions. Both the visual features and the semantic descriptions are for predicting defective and defect-free labels. The method also includes calculating, by the processor, a cosine similarity score between the classification weights and the group disentangled visual feature embedding vectors. The method additionally includes episodically training, by the processor, the weight generation network on the input images to update parameters of the weight generation network. The method further includes generating, by the processor using the trained weight generation network, a prediction of a test image as including any of defective objects and defect-free objects.

    FEW-SHOT VIDEO CLASSIFICATION
    8.
    发明公开

    公开(公告)号:US20240054782A1

    公开(公告)日:2024-02-15

    申请号:US18366931

    申请日:2023-08-08

    CPC classification number: G06V20/41 G06V20/46 G06V10/774 G06V20/48

    Abstract: Methods and systems for video processing include enriching an input video feature from an input video frame set using a meta-action bank video sub-actions to generate enriched features. Reinforced image representation is performed using reinforcement learning to compare support image frames and query image frames and determine an importance of the input video frame. A classification is performed on the input video frame based on the importance and the enriched features to generate a label. An action is performed responsive to the generated label.

    NETWORK REPARAMETERIZATION FOR NEW CLASS CATEGORIZATION

    公开(公告)号:US20200097757A1

    公开(公告)日:2020-03-26

    申请号:US16580199

    申请日:2019-09-24

    Abstract: A computer-implemented method and system are provided for training a model for New Class Categorization (NCC) of a test image. The method includes decoupling, by a hardware processor, a feature extraction part from a classifier part of a deep classification model by reparametrizing learnable weight variables of the classifier part as a combination of learnable variables of the feature extraction part and of a classification weight generator of the classifier part. The method further includes training, by the hardware processor, the deep classification model to obtain a trained deep classification model by (i) learning the feature extraction part as a multiclass classification task, and (ii) episodically training the classifier part by learning a classification weight generator which outputs classification weights given a training image.

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