公开(公告)号：US11574155B2

公开(公告)日：2023-02-07

申请号：US17226561

申请日：2021-04-09

Applicant: Nvidia Corporation

Inventor： Aayush Prakash ,  Shoubhik Debnath ,  Jean-Francois Lafleche ,  Eric Cameracci ,  Gavriel State ,  Marc Teva Law

IPC: G06K9/62 ,  G06V20/00 ,  G06V10/82 ,  G06V20/70 ,  G06V20/56

Abstract: Approaches are presented for training and using scene graph generators for transfer learning. A scene graph generation technique can decompose a domain gap into individual types of discrepancies, such as may relate to appearance, label, and prediction discrepancies. These discrepancies can be reduced, at least in part, by aligning the corresponding latent and output distributions using one or more gradient reversal layers (GRLs). Label discrepancies can be addressed using self-pseudo-statistics collected from target data. Pseudo statistic-based self-learning and adversarial techniques can be used to manage these discrepancies without the need for costly supervision from a real-world dataset.

公开(公告)号：US20210374489A1

公开(公告)日：2021-12-02

申请号：US17226561

申请日：2021-04-09

Applicant: Nvidia Corporation

Inventor： Aayush Prakash ,  Shoubhik Debnath ,  Jean-Francois Lafleche ,  Eric Cameracci ,  Gavriel State ,  Marc Teva Law

IPC: G06K9/62 ,  G06K9/00

Abstract: Approaches are presented for training and using scene graph generators for transfer learning. A scene graph generation technique can decompose a domain gap into individual types of discrepancies, such as may relate to appearance, label, and prediction discrepancies. These discrepancies can be reduced, at least in part, by aligning the corresponding latent and output distributions using one or more gradient reversal layers (GRLs). Label discrepancies can be addressed using self-pseudo-statistics collected from target data. Pseudo statistic-based self-learning and adversarial techniques can be used to manage these discrepancies without the need for costly supervision from a real-world dataset.

公开(公告)号：US20240320993A1

公开(公告)日：2024-09-26

申请号：US18673785

申请日：2024-05-24

Applicant: Nvidia Corporation

Inventor： Aayush Prakash ,  Shoubhik Debnath ,  Jean-Francois Lafleche ,  Eric Cameracci ,  Gavriel State ,  Marc Teva Law

IPC: G06V20/70 ,  G06F18/10 ,  G06F18/20 ,  G06F18/24 ,  G06V10/764 ,  G06V10/82 ,  G06V10/84 ,  G06V20/00 ,  G06V20/56

CPC classification number: G06V20/70 ,  G06F18/10 ,  G06F18/24 ,  G06F18/29 ,  G06V10/764 ,  G06V10/82 ,  G06V10/84 ,  G06V20/00 ,  G06V20/56

Abstract: Approaches are presented for training and using scene graph generators for transfer learning. A scene graph generation technique can decompose a domain gap into individual types of discrepancies, such as may relate to appearance, label, and prediction discrepancies. These discrepancies can be reduced, at least in part, by aligning the corresponding latent and output distributions using one or more gradient reversal layers (GRLs). Label discrepancies can be addressed using self-pseudo-statistics collected from target data. Pseudo statistic-based self-learning and adversarial techniques can be used to manage these discrepancies without the need for costly supervision from a real-world dataset.

公开(公告)号：US11995883B2

公开(公告)日：2024-05-28

申请号：US18163482

申请日：2023-02-02

Applicant: Nvidia Corporation

Inventor： Aayush Prakash ,  Shoubhik Debnath ,  Jean-Francois Lafleche ,  Eric Cameracci ,  Gavriel State ,  Marc Teva Law

IPC: G06V10/82 ,  G06F18/10 ,  G06F18/20 ,  G06F18/24 ,  G06V10/764 ,  G06V10/84 ,  G06V20/00 ,  G06V20/56 ,  G06V20/70

CPC classification number: G06V10/82 ,  G06F18/10 ,  G06F18/24 ,  G06F18/29 ,  G06V10/764 ,  G06V10/84 ,  G06V20/00 ,  G06V20/70 ,  G06V20/56

Abstract: Approaches are presented for training and using scene graph generators for transfer learning. A scene graph generation technique can decompose a domain gap into individual types of discrepancies, such as may relate to appearance, label, and prediction discrepancies. These discrepancies can be reduced, at least in part, by aligning the corresponding latent and output distributions using one or more gradient reversal layers (GRLs). Label discrepancies can be addressed using self-pseudo-statistics collected from target data. Pseudo statistic-based self-learning and adversarial techniques can be used to manage these discrepancies without the need for costly supervision from a real-world dataset.

公开(公告)号：US20230177826A1

公开(公告)日：2023-06-08

申请号：US18163482

申请日：2023-02-02

Applicant: Nvidia Corporation

Inventor： Aayush Prakash ,  Shoubhik Debnath ,  Jean-Francois Lafleche ,  Eric Cameracci ,  Gavriel State ,  Marc Teva Law

IPC: G06V10/82 ,  G06V20/70 ,  G06F18/10 ,  G06F18/24 ,  G06F18/20 ,  G06V10/764 ,  G06V10/84 ,  G06V20/00

CPC classification number: G06V10/82 ,  G06V20/70 ,  G06F18/10 ,  G06F18/24 ,  G06F18/29 ,  G06V10/764 ,  G06V10/84 ,  G06V20/00 ,  G06V20/56

Abstract: Approaches are presented for training and using scene graph generators for transfer learning. A scene graph generation technique can decompose a domain gap into individual types of discrepancies, such as may relate to appearance, label, and prediction discrepancies. These discrepancies can be reduced, at least in part, by aligning the corresponding latent and output distributions using one or more gradient reversal layers (GRLs). Label discrepancies can be addressed using self-pseudo-statistics collected from target data. Pseudo statistic-based self-learning and adversarial techniques can be used to manage these discrepancies without the need for costly supervision from a real-world dataset.