公开(公告)号：US20200151489A1

公开(公告)日：2020-05-14

申请号：US16678100

申请日：2019-11-08

Applicant: NVIDIA Corporation

Inventor： Parthasarathy Sriram ,  Fnu Ratnesh Kumar ,  Anil Ubale ,  Farzin Aghdasi ,  Yan Zhai ,  Subhashree Radhakrishnan

IPC: G06K9/34 ,  G06N3/08 ,  G06N3/04 ,  G06K9/00 ,  G06T7/246 ,  G06K9/42

Abstract: In various examples, sensor data—such as masked sensor data—may be used as input to a machine learning model to determine a confidence for object to person associations. The masked sensor data may focus the machine learning model on particular regions of the image that correspond to persons, objects, or some combination thereof. In some embodiments, coordinates corresponding to persons, objects, or combinations thereof, in addition to area ratios between various regions of the image corresponding to the persons, objects, or combinations thereof, may be used to further aid the machine learning model in focusing on important regions of the image for determining the object to person associations.

公开(公告)号：US20250005956A1

公开(公告)日：2025-01-02

申请号：US18828950

申请日：2024-09-09

Applicant: NVIDIA Corporation

Inventor： Parthasarathy Sriram ,  Fnu Ratnesh Kumar ,  Anil Ubale ,  Farzin Aghdasi ,  Yan Zhai ,  Subhashree Radhakrishnan

IPC: G06V40/10 ,  G06N3/045 ,  G06N3/08 ,  G06T7/246 ,  G06V10/26 ,  G06V10/44 ,  G06V10/82 ,  G06V20/52

Abstract: In various examples, sensor data—such as masked sensor data—may be used as input to a machine learning model to determine a confidence for object to person associations. The masked sensor data may focus the machine learning model on particular regions of the image that correspond to persons, objects, or some combination thereof. In some embodiments, coordinates corresponding to persons, objects, or combinations thereof, in addition to area ratios between various regions of the image corresponding to the persons, objects, or combinations thereof, may be used to further aid the machine learning model in focusing on important regions of the image for determining the object to person associations.

公开(公告)号：US20240221166A1

公开(公告)日：2024-07-04

申请号：US18395198

申请日：2023-12-22

Applicant: NVIDIA Corporation

Inventor： Zhiding Yu ,  Shuaiyi Huang ,  De-An Huang ,  Shiyi Lan ,  Subhashree Radhakrishnan ,  Jose M. Alvarez Lopez ,  Anima Anandkumar

IPC: G06T7/12 ,  G06V10/764 ,  G06V20/70

CPC classification number: G06T7/12 ,  G06V10/764 ,  G06V20/70 ,  G06T2207/20081

Abstract: Video instance segmentation is a computer vision task that aims to detect, segment, and track objects continuously in videos. It can be used in numerous real-world applications, such as video editing, three-dimensional (3D) reconstruction, 3D navigation (e.g. for autonomous driving and/or robotics), and view point estimation. However, current machine learning-based processes employed for video instance segmentation are lacking, particularly because the densely annotated videos needed for supervised training of high-quality models are not readily available and are not easily generated. To address the issues in the prior art, the present disclosure provides point-level supervision for video instance segmentation in a manner that allows the resulting machine learning model to handle any object category.

公开(公告)号：US20240169545A1

公开(公告)日：2024-05-23

申请号：US18355856

申请日：2023-07-20

Applicant: NVIDIA Corporation

Inventor： Shiyi Lan ,  Zhiding Yu ,  Subhashree Radhakrishnan ,  Jose Manuel Alvarez Lopez ,  Animashree Anandkumar

IPC: G06T7/11 ,  G06T1/20

CPC classification number: G06T7/11 ,  G06T1/20 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20132

Abstract: Class agnostic object mask generation uses a vision transformer-based auto-labeling framework requiring only images and object bounding boxes to generate object (segmentation) masks. The generated object masks, images, and object labels may then be used to train instance segmentation models or other neural networks to localize and segment objects with pixel-level accuracy. The generated object masks may supplement or replace conventional human generated annotations. The human generated annotations may be misaligned compared with the object boundaries, resulting in poor quality labeled segmentation masks. In contrast with conventional techniques, the generated object masks are class agnostic and are automatically generated based only on a bounding box image region without relying on either labels or semantic information.

公开(公告)号：US20220261593A1

公开(公告)日：2022-08-18

申请号：US17177068

申请日：2021-02-16

Applicant: NVIDIA Corporation

Inventor： Zhiding Yu ,  Shiyi Lan ,  Chris Choy ,  Subhashree Radhakrishnan ,  Guilin Liu ,  Yuke Zhu ,  Anima Anandkumar

IPC: G06K9/62 ,  G06T7/11 ,  G06K9/00 ,  G06N3/04 ,  G06N3/08

Abstract: Apparatuses, systems, and techniques to train one or more neural networks. In at least one embodiment, one or more neural networks are trained to perform segmentation tasks based at least in part on training data comprising bounding box annotations.