JP7801997B2 - 画像内の物体の向きを予測するためのニューラル・ネットワークを使用する訓練及び推論 - Google Patents
画像内の物体の向きを予測するためのニューラル・ネットワークを使用する訓練及び推論Info
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- JP7801997B2 JP7801997B2 JP2022524086A JP2022524086A JP7801997B2 JP 7801997 B2 JP7801997 B2 JP 7801997B2 JP 2022524086 A JP2022524086 A JP 2022524086A JP 2022524086 A JP2022524086 A JP 2022524086A JP 7801997 B2 JP7801997 B2 JP 7801997B2
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- G06F18/2178—Validation; Performance evaluation; Active pattern learning techniques based on feedback of a supervisor
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- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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- G06T2207/30248—Vehicle exterior or interior
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Landscapes
- Engineering & Computer Science (AREA)
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Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2025139977A JP2026000914A (ja) | 2019-11-20 | 2025-08-25 | 画像内の物体の向きを予測するためのニューラル・ネットワークを使用する訓練及び推論 |
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US16/690,015 US12266144B2 (en) | 2019-11-20 | 2019-11-20 | Training and inferencing using a neural network to predict orientations of objects in images |
| US16/690,015 | 2019-11-20 | ||
| PCT/US2020/060917 WO2021101907A1 (en) | 2019-11-20 | 2020-11-17 | Training and inferencing using a neural network to predict orientations of objects in images |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2025139977A Division JP2026000914A (ja) | 2019-11-20 | 2025-08-25 | 画像内の物体の向きを予測するためのニューラル・ネットワークを使用する訓練及び推論 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JP2023502575A JP2023502575A (ja) | 2023-01-25 |
| JP7801997B2 true JP7801997B2 (ja) | 2026-01-19 |
Family
ID=73834593
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2022524086A Active JP7801997B2 (ja) | 2019-11-20 | 2020-11-17 | 画像内の物体の向きを予測するためのニューラル・ネットワークを使用する訓練及び推論 |
| JP2025139977A Pending JP2026000914A (ja) | 2019-11-20 | 2025-08-25 | 画像内の物体の向きを予測するためのニューラル・ネットワークを使用する訓練及び推論 |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2025139977A Pending JP2026000914A (ja) | 2019-11-20 | 2025-08-25 | 画像内の物体の向きを予測するためのニューラル・ネットワークを使用する訓練及び推論 |
Country Status (8)
| Country | Link |
|---|---|
| US (2) | US12266144B2 (https=) |
| JP (2) | JP7801997B2 (https=) |
| KR (1) | KR20220079673A (https=) |
| CN (1) | CN114787879B (https=) |
| AU (1) | AU2020387942A1 (https=) |
| DE (1) | DE112020005696T5 (https=) |
| GB (1) | GB2603092A (https=) |
| WO (1) | WO2021101907A1 (https=) |
Families Citing this family (43)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11833681B2 (en) * | 2018-08-24 | 2023-12-05 | Nvidia Corporation | Robotic control system |
| DE102019122790B4 (de) * | 2018-08-24 | 2021-03-25 | Nvidia Corp. | Robotersteuerungssystem |
| WO2020101127A1 (en) * | 2018-11-13 | 2020-05-22 | Samsung Electro-Mechanics Co., Ltd. | Driving support system and method |
| JP7105918B2 (ja) * | 2018-12-27 | 2022-07-25 | 富士フイルム株式会社 | 領域特定装置、方法およびプログラム |
| US10614345B1 (en) | 2019-04-12 | 2020-04-07 | Ernst & Young U.S. Llp | Machine learning based extraction of partition objects from electronic documents |
| US11113518B2 (en) | 2019-06-28 | 2021-09-07 | Eygs Llp | Apparatus and methods for extracting data from lineless tables using Delaunay triangulation and excess edge removal |
| US11915465B2 (en) * | 2019-08-21 | 2024-02-27 | Eygs Llp | Apparatus and methods for converting lineless tables into lined tables using generative adversarial networks |
| KR20210076691A (ko) * | 2019-12-16 | 2021-06-24 | 삼성전자주식회사 | 프레임워크 간 뉴럴 네트워크의 학습을 검증하는 방법 및 장치 |
| KR102871465B1 (ko) * | 2020-01-02 | 2025-10-16 | 엘지전자 주식회사 | 로컬 장치의 성능 향상 |
| US11443442B2 (en) * | 2020-01-28 | 2022-09-13 | Here Global B.V. | Method and apparatus for localizing a data set based upon synthetic image registration |
| US11625934B2 (en) | 2020-02-04 | 2023-04-11 | Eygs Llp | Machine learning based end-to-end extraction of tables from electronic documents |
| EP3862926A1 (en) * | 2020-02-10 | 2021-08-11 | Robert Bosch GmbH | Method of identifying filters in a neural network, system and storage medium of the same |
| US11675879B2 (en) * | 2020-02-20 | 2023-06-13 | K2Ai, LLC | Apparatus and method for operating a detection and response system |
| US20210264284A1 (en) * | 2020-02-25 | 2021-08-26 | Ford Global Technologies, Llc | Dynamically routed patch discriminator |
| US11887323B2 (en) * | 2020-06-08 | 2024-01-30 | Ford Global Technologies, Llc | Self-supervised estimation of observed vehicle pose |
| US12422791B2 (en) * | 2020-06-12 | 2025-09-23 | Massachusetts Institute Of Technology | Simulation-based training of an autonomous vehicle |
| US20220027672A1 (en) * | 2020-07-27 | 2022-01-27 | Nvidia Corporation | Label Generation Using Neural Networks |
| US20220058444A1 (en) * | 2020-08-19 | 2022-02-24 | Capital One Services, Llc | Asymmetric adversarial learning framework for multi-turn dialogue response generation |
| EP4075382B1 (en) * | 2021-04-12 | 2025-04-23 | Toyota Jidosha Kabushiki Kaisha | A method for training a neural network to deliver the viewpoints of objects using pairs of images under different viewpoints |
| US12579216B2 (en) | 2021-06-02 | 2026-03-17 | Nvidia Corporation | Techniques for classification with neural networks |
| CN113362313B (zh) * | 2021-06-18 | 2024-03-15 | 四川启睿克科技有限公司 | 一种基于自监督学习的缺陷检测方法及系统 |
| CN113536971B (zh) * | 2021-06-28 | 2024-09-13 | 中科苏州智能计算技术研究院 | 一种基于增量学习的目标检测方法 |
| US12183050B1 (en) * | 2021-07-30 | 2024-12-31 | Nvidia Corporation | Inferencing using neural networks |
| US12536733B2 (en) * | 2021-09-10 | 2026-01-27 | Nvidia Corporation | Single-image inverse rendering |
| KR102818055B1 (ko) * | 2021-09-23 | 2025-06-10 | 주식회사 제이엔제이테크 | 인공지능을 활용한 조제 검수 시스템 |
| US12567170B2 (en) * | 2021-11-15 | 2026-03-03 | Toyota Research Institute, Inc. | Producing a depth map from two-dimensional images |
| KR20230086045A (ko) * | 2021-12-08 | 2023-06-15 | 고려대학교 산학협력단 | Can 메시지 분석과 신경망 모델을 이용한 경량화된 실시간 이상 탐지 방법 |
| US11896376B2 (en) * | 2022-01-27 | 2024-02-13 | Gaize | Automated impairment detection system and method |
| CN115277098B (zh) * | 2022-06-27 | 2023-07-18 | 深圳铸泰科技有限公司 | 一种基于智能学习的网络流量异常检测装置及方法 |
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| CN115565177B (zh) * | 2022-08-16 | 2023-06-20 | 北京百度网讯科技有限公司 | 文字识别模型训练、文字识别方法、装置、设备及介质 |
| KR20240069405A (ko) | 2022-11-11 | 2024-05-20 | 삼성전자주식회사 | 대칭 탐지를 위한 장치의 동작 방법 및 이를 수행하는 장치 |
| KR102924100B1 (ko) | 2022-11-22 | 2026-02-06 | 한국과학기술원 | 3d 객체 텍스처 맵 생성 장치 및 방법 |
| CN116416644A (zh) * | 2022-12-30 | 2023-07-11 | 支付宝(杭州)信息技术有限公司 | 一种支付意愿的识别方法和系统 |
| US20240233408A1 (en) * | 2023-01-05 | 2024-07-11 | Toyota Research Institute, Inc. | System and method for training a multi-view 3d object detection framework |
| DE102023000563B3 (de) * | 2023-02-20 | 2024-02-01 | Mercedes-Benz Group AG | Informationstechnisches-System, Fahrzeug und Verfahren zum Einbringen einer Aktualisierung auf ein Zielsystem |
| AU2023202005B1 (en) | 2023-03-31 | 2024-04-04 | Canva Pty Ltd | Image rotation |
| WO2025006356A2 (en) * | 2023-06-29 | 2025-01-02 | Gutz Analytics | Multi-modal dynamic variational autoencoders for longitudinal analysis of multi-omics data |
| US20250148540A1 (en) * | 2023-11-07 | 2025-05-08 | Nec Laboratories America, Inc. | Adversarial imitation learning engine for action risk estimation based on sensor data |
| US20250209568A1 (en) * | 2023-12-21 | 2025-06-26 | Sony Interactive Entertainment Inc. | Generation super sampling |
| US20250245848A1 (en) * | 2024-01-31 | 2025-07-31 | Huawei Technologies Co., Ltd. | Methods and systems for performing gaze estimation with meta prompting |
| CN119026708B (zh) * | 2024-07-13 | 2026-01-23 | 北京智源人工智能研究院 | 基于模块化自编码的集成网络的训练方法和装置 |
| EP4693220A1 (en) * | 2024-08-06 | 2026-02-11 | Nokia Solutions and Networks Oy | Apparatus, method, and system for providing symbiotic autonomous training of machine learning models |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130002525A1 (en) | 2011-06-29 | 2013-01-03 | Bobby Duane Foote | System for locating a position of an object |
| JP2018022416A (ja) | 2016-08-05 | 2018-02-08 | 日本放送協会 | 顔方向推定装置及びそのプログラム |
| CN110175572A (zh) | 2019-05-28 | 2019-08-27 | 深圳市商汤科技有限公司 | 人脸图像处理方法及装置、电子设备及存储介质 |
| US20190294907A1 (en) | 2014-11-05 | 2019-09-26 | Samsung Electronics Co., Ltd. | Device and method to generate image using image learning model |
Family Cites Families (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5459636A (en) | 1994-01-14 | 1995-10-17 | Hughes Aircraft Company | Position and orientation estimation neural network system and method |
| JP5471038B2 (ja) * | 2009-05-27 | 2014-04-16 | アイシン精機株式会社 | 校正目標検出装置と、校正目標を検出する校正目標検出方法と、校正目標検出装置のためのプログラム |
| US8581647B2 (en) | 2011-11-10 | 2013-11-12 | Qualcomm Incorporated | System and method of stabilizing charge pump node voltage levels |
| US9449392B2 (en) * | 2013-06-05 | 2016-09-20 | Samsung Electronics Co., Ltd. | Estimator training method and pose estimating method using depth image |
| JP2016201609A (ja) | 2015-04-08 | 2016-12-01 | 日本電気通信システム株式会社 | 加入者端末装置、通信サービス提供システム、通信制御方法、及び、通信制御プログラム |
| US9965719B2 (en) * | 2015-11-04 | 2018-05-08 | Nec Corporation | Subcategory-aware convolutional neural networks for object detection |
| CN106022244B (zh) * | 2016-05-16 | 2019-09-17 | 广东工业大学 | 基于递归神经网络建模的无监督人群异常监测及定位方法 |
| WO2018020277A1 (en) * | 2016-07-28 | 2018-02-01 | Google Llc | Domain separation neural networks |
| JP6957624B2 (ja) * | 2016-12-15 | 2021-11-02 | グーグル エルエルシーGoogle LLC | ターゲット・ドメイン画像へのソース・ドメイン画像の変換 |
| JP6987508B2 (ja) * | 2017-02-20 | 2022-01-05 | オムロン株式会社 | 形状推定装置及び方法 |
| JP6833630B2 (ja) * | 2017-06-22 | 2021-02-24 | 株式会社東芝 | 物体検出装置、物体検出方法およびプログラム |
| US20180373980A1 (en) * | 2017-06-27 | 2018-12-27 | drive.ai Inc. | Method for training and refining an artificial intelligence |
| CN110838124B (zh) * | 2017-09-12 | 2021-06-18 | 深圳科亚医疗科技有限公司 | 用于分割具有稀疏分布的对象的图像的方法、系统和介质 |
| US10769411B2 (en) * | 2017-11-15 | 2020-09-08 | Qualcomm Technologies, Inc. | Pose estimation and model retrieval for objects in images |
| US10403031B2 (en) | 2017-11-15 | 2019-09-03 | Google Llc | Learning to reconstruct 3D shapes by rendering many 3D views |
| US10789717B2 (en) * | 2017-11-24 | 2020-09-29 | Electronics And Telecommunications Research Institute | Apparatus and method of learning pose of moving object |
| JP2019152543A (ja) * | 2018-03-02 | 2019-09-12 | 株式会社東芝 | 目標認識装置、目標認識方法及びプログラム |
| US20200041276A1 (en) * | 2018-08-03 | 2020-02-06 | Ford Global Technologies, Llc | End-To-End Deep Generative Model For Simultaneous Localization And Mapping |
| US10839234B2 (en) * | 2018-09-12 | 2020-11-17 | Tusimple, Inc. | System and method for three-dimensional (3D) object detection |
| US11507822B2 (en) * | 2018-10-31 | 2022-11-22 | General Electric Company | Scalable artificial intelligence model generation systems and methods for healthcare |
| US20210015449A1 (en) | 2019-07-16 | 2021-01-21 | GE Precision Healthcare LLC | Methods and systems for processing and displaying fetal images from ultrasound imaging data |
| US11308353B2 (en) * | 2019-10-23 | 2022-04-19 | Adobe Inc. | Classifying digital images in few-shot tasks based on neural networks trained using manifold mixup regularization and self-supervision |
| US20210110552A1 (en) * | 2020-12-21 | 2021-04-15 | Intel Corporation | Methods and apparatus to improve driver-assistance vision systems using object detection based on motion vectors |
| US12197713B2 (en) * | 2022-02-03 | 2025-01-14 | Adobe Inc. | Generating and applying editing presets |
-
2019
- 2019-11-20 US US16/690,015 patent/US12266144B2/en active Active
-
2020
- 2020-11-17 GB GB2205954.7A patent/GB2603092A/en active Pending
- 2020-11-17 KR KR1020227016210A patent/KR20220079673A/ko active Pending
- 2020-11-17 DE DE112020005696.1T patent/DE112020005696T5/de active Pending
- 2020-11-17 JP JP2022524086A patent/JP7801997B2/ja active Active
- 2020-11-17 CN CN202080078653.5A patent/CN114787879B/zh active Active
- 2020-11-17 AU AU2020387942A patent/AU2020387942A1/en active Pending
- 2020-11-17 WO PCT/US2020/060917 patent/WO2021101907A1/en not_active Ceased
-
2025
- 2025-03-28 US US19/094,621 patent/US20250384647A1/en active Pending
- 2025-08-25 JP JP2025139977A patent/JP2026000914A/ja active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130002525A1 (en) | 2011-06-29 | 2013-01-03 | Bobby Duane Foote | System for locating a position of an object |
| US20190294907A1 (en) | 2014-11-05 | 2019-09-26 | Samsung Electronics Co., Ltd. | Device and method to generate image using image learning model |
| JP2018022416A (ja) | 2016-08-05 | 2018-02-08 | 日本放送協会 | 顔方向推定装置及びそのプログラム |
| CN110175572A (zh) | 2019-05-28 | 2019-08-27 | 深圳市商汤科技有限公司 | 人脸图像处理方法及装置、电子设备及存储介质 |
Non-Patent Citations (2)
| Title |
|---|
| 二宮 宏史 Hiroshi NINOMIYA,深層学習を用いた多様体構築による3次元物体の姿勢推定に関する予備検討 Preliminary study on deep manifold embedding for 3D object pose estimation,電子情報通信学会技術研究報告 Vol.116 No.91 IEICE Technical Report,日本,一般社団法人電子情報通信学会 The Institute of Electronics,Information and Communication Engineers,第116巻 |
| 川西 康友 Yasutomo KAWANISHI,姿勢を表現する多様体に基づくGANsを用いた物体姿勢推定の検討 A Study on GANs based on Pose Manifold for Rigid Object Pose Estimation,電子情報通信学会技術研究報告 Vol.117 No.238 IEICE Technical Report,日本,一般社団法人電子情報通信学会 The Institute of Electronics,Information and Communication Engineers,第117巻 |
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