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    Learning Data Augmentation Strategies for Object Detection
    12.
    发明申请
    Learning Data Augmentation Strategies for Object Detection 有权

    公开(公告)号：US20220215682A1

    公开(公告)日：2022-07-07

    申请号：US17702438

    申请日：2022-03-23

    Applicant: Google LLC

    Inventor： Jon Shlens ,  Ekin Dogus Cubuk ,  Quoc Le ,  Tsung-Yi Lin ,  Barret Zoph ,  Golnaz Ghiasi

    IPC: G06V30/194 ,  G06K9/62 ,  G06T3/60 ,  G06T3/20 ,  G06T11/00

    Abstract: Example aspects of the present disclosure are directed to systems and methods for learning data augmentation strategies for improved object detection model performance. In particular, example aspects of the present disclosure are directed to iterative reinforcement learning approaches in which, at each of a plurality of iterations, a controller model selects a series of one or more augmentation operations to be applied to training images to generate augmented images. For example, the controller model can select the augmentation operations from a defined search space of available operations which can, for example, include operations that augment the training image without modification of the locations of a target object and corresponding bounding shape within the image and/or operations that do modify the locations of the target object and bounding shape within the training image.

    Scale-Permuted Machine Learning Architecture
    14.
    发明申请
    Scale-Permuted Machine Learning Architecture 有权

    公开(公告)号：US20240378509A1

    公开(公告)日：2024-11-14

    申请号：US18784068

    申请日：2024-07-25

    Applicant: Google LLC

    Inventor： Xianzhi Du ,  Yin Cui ,  Tsung-Yi Lin ,  Quoc V. Le ,  Pengchong Jin ,  Mingxing Tan ,  Golnaz Ghiasi ,  Xiaodan Song

    IPC: G06N20/00 ,  G06F11/34 ,  G06N3/04

    Abstract: A computer-implemented method of generating scale-permuted models can generate models having improved accuracy and reduced evaluation computational requirements. The method can include defining, by a computing system including one or more computing devices, a search space including a plurality of candidate permutations of a plurality of candidate feature blocks, each of the plurality of candidate feature blocks having a respective scale. The method can include performing, by the computing system, a plurality of search iterations by a search algorithm to select a scale-permuted model from the search space, the scale-permuted model based at least in part on a candidate permutation of the plurality of candidate permutations.

    Scale-permuted machine learning architecture
    16.
    发明授权
    Scale-permuted machine learning architecture 有权

    公开(公告)号：US12079695B2

    公开(公告)日：2024-09-03

    申请号：US17061355

    申请日：2020-10-01

    Applicant: Google LLC

    Inventor： Xianzhi Du ,  Yin Cui ,  Tsung-Yi Lin ,  Quoc V. Le ,  Pengchong Jin ,  Mingxing Tan ,  Golnaz Ghiasi ,  Xiaodan Song

    IPC: G06N20/00 ,  G06F11/34 ,  G06N3/04

    CPC classification number: G06N20/00 ,  G06F11/3495 ,  G06N3/04

    Abstract: A computer-implemented method of generating scale-permuted models can generate models having improved accuracy and reduced evaluation computational requirements. The method can include defining, by a computing system including one or more computing devices, a search space including a plurality of candidate permutations of a plurality of candidate feature blocks, each of the plurality of candidate feature blocks having a respective scale. The method can include performing, by the computing system, a plurality of search iterations by a search algorithm to select a scale-permuted model from the search space, the scale-permuted model based at least in part on a candidate permutation of the plurality of candidate permutations.

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