Task agnostic open-set prototypes for few-shot open-set recognition

    公开(公告)号:US12019641B2

    公开(公告)日:2024-06-25

    申请号:US18153899

    申请日:2023-01-12

    CPC classification number: G06F16/2462 G06F16/285

    Abstract: Systems and techniques are provided for processing one or more data samples. For example, a neural network classifier can be trained to perform few-shot open-set recognition (FSOSR) based on a task-agnostic open-set prototype. A process can include determining one or more prototype representations for each class included in a plurality of support samples. A task-agnostic open-set prototype representation can be determined, in a same learned metric space as the one or more prototype representations. One or more distance metrics can be determined for each query sample of one or more query samples, based on the one or more prototype representations and the task-agnostic open-set prototype representation. Based on the one or more distance metrics, each query sample can be classified into one of classes associated with the one or more prototype representations or an open-set class associated with the task-agnostic open-set prototype representation.

    Systems and methods of image processing based on gaze detection

    公开(公告)号:US11798204B2

    公开(公告)日:2023-10-24

    申请号:US17685278

    申请日:2022-03-02

    CPC classification number: G06T11/00 G06F3/013 G06V40/174 G06V40/18

    Abstract: Imaging systems and techniques are described. An imaging system receives image data representing at least a portion (e.g., a face) of a first user as captured by a first image sensor. The imaging system identifies that a gaze of the first user as represented in the image data is directed toward a displayed representation of at least a portion (e.g., a face) of a second user. The imaging system identifies an arrangement of representations of users for output. The imaging system generates modified image data based on the gaze and the arrangement at least in part by modifying the image data to modify at least the portion of the first user in the image data to be visually directed toward a direction corresponding to the second user based on the gaze and the arrangement. The imaging system outputs the modified image data arranged according to the arrangement.

    RESIDUAL NORMALIZATION FOR IMPROVED NEURAL NETWORK CLASSIFICATIONS

    公开(公告)号:US20220405547A1

    公开(公告)日:2022-12-22

    申请号:US17807479

    申请日:2022-06-17

    Abstract: Certain aspects of the present disclosure provide techniques for residual normalization. A first tensor comprising a frequency dimension and a temporal dimension is accessed. A second tensor is generated by applying a frequency-based instance normalization operation to the first tensor, comprising, for each respective frequency bin in the frequency dimension, computing a respective frequency-specific mean of the first tensor. A third tensor is generated by: scaling the first tensor by a scale value, and aggregating the scaled first tensor and the second tensor. The third tensor is provided as input to a layer of a neural network.

    BROADCASTED RESIDUAL LEARNING
    6.
    发明申请

    公开(公告)号:US20220309344A1

    公开(公告)日:2022-09-29

    申请号:US17656621

    申请日:2022-03-25

    Abstract: Certain aspects of the present disclosure provide techniques for efficient broadcasted residual machine learning. An input tensor comprising a frequency dimension and a temporal dimension is received, and the input tensor is processed with a first convolution operation to generate a multidimensional intermediate feature map comprising the frequency dimension and the temporal dimension. The multidimensional intermediate feature map is converted to a one-dimensional intermediate feature map in the temporal dimension using a frequency dimension reduction operation, and the one-dimensional intermediate feature map is processed using a second convolution operation to generate a temporal feature map. The temporal feature map is expanded to the frequency dimension using a broadcasting operation to generate a multidimensional output feature map, and the multidimensional output feature map is augmented with the multidimensional intermediate feature map via a first residual connection.

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