公开(公告)号：US20210133911A1

公开(公告)日：2021-05-06

申请号：US16474540

申请日：2017-04-07

Applicant: INTEL CORPORATION

Inventor： Anbang YAO ,  Dongqi CAI ,  Libin WANG ,  Lin XU ,  Ping HU ,  Shandong WANG ,  Wehnua CHENG ,  Yiwen GUO ,  Liu YANG ,  Yuqing HOU ,  Zhou SU

IPC: G06T1/20 ,  G06N3/00 ,  G06N3/04 ,  G06N3/08 ,  G06N20/00

Abstract: Described herein are advanced artificial intelligence agents for modeling physical interactions. An apparatus to provide an active artificial intelligence (AI) agent includes at least one database to store physical interaction data and compute cluster coupled to the at least one database. The compute cluster automatically obtains physical interaction data from a data collection module without manual interaction, stores the physical interaction data in the at least one database, and automatically trains diverse sets of machine learning program units to simulate physical interactions with each individual program unit having a different model based on the applied physical interaction data.

公开(公告)号：US20230274580A1

公开(公告)日：2023-08-31

申请号：US18014722

申请日：2020-08-14

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Shandong WANG ,  Ming LU ,  Yuqing HOU ,  Yangyuxuan KANG ,  Yurong CHEN

IPC: G06V40/20 ,  G06T7/20 ,  G06V10/44 ,  G06V10/82

CPC classification number: G06V40/23 ,  G06T7/20 ,  G06V10/44 ,  G06V10/82 ,  G06T2207/20044 ,  G06T2207/30196

Abstract: A method and system of image processing for action classification uses fine-grained motion-attributes.

公开(公告)号：US20230343068A1

公开(公告)日：2023-10-26

申请号：US17918080

申请日：2020-06-15

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Yikai WANG ,  Ming LU ,  Shandong WANG ,  Feng CHEN

IPC: G06V10/764 ,  G06V10/32 ,  G06V10/44 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06V10/764 ,  G06V10/32 ,  G06V10/454 ,  G06V10/774 ,  G06V10/82

Abstract: Techniques related to implementing and training image classification networks are discussed. Such techniques include applying shared convolutional layers to input images regardless of resolution and applying normalization selectively based on the input image resolution. Such techniques further include training using mixed image size parallel training and mixed image size ensemble distillation.

公开(公告)号：US20210104086A1

公开(公告)日：2021-04-08

申请号：US16971132

申请日：2018-06-14

Applicant: Intel Corporation

Inventor： Shandong WANG ,  Ming LU ,  Anbang YAO ,  Yurong CHEN

IPC: G06T13/40 ,  G06N3/04 ,  G06N3/08 ,  G06K9/00 ,  G06T19/20 ,  G06K9/62

Abstract: Techniques related to capturing 3D faces using image and temporal tracking neural networks and modifying output video using the captured 3D faces are discussed. Such techniques include applying a first neural network to an input vector corresponding to a first video image having a representation of a human face to generate a morphable model parameter vector, applying a second neural network to an input vector corresponding to a first and second temporally subsequent to generate a morphable model parameter delta vector, generating a 3D face model of the human face using the morphable model parameter vector and the morphable model parameter delta vector, and generating output video using the 3D face model.

公开(公告)号：US20250148761A1

公开(公告)日：2025-05-08

申请号：US18717894

申请日：2022-03-03

Applicant: Intel Corporation

Inventor： Dongqi CAI ,  Anbang YAO ,  Chao LI ,  Shandong WANG ,  Yurong CHEN

IPC: G06V10/771 ,  G06T5/20 ,  G06V10/40 ,  G06V20/64

Abstract: The disclosure provides an apparatus, method, device and medium for 3D dynamic sparse convolution. The method includes: receiving an input feature map of a 3D data sample; performing input feature map partition to divide the input feature map into a plurality of disjoint input feature map groups; performing a shared 3D dynamic sparse convolution to the plurality of disjoint input feature map groups respectively to obtain a plurality of output feature maps corresponding to the plurality of disjoint input feature map groups, wherein the shared 3D dynamic sparse convolution comprises a shared 3D dynamic sparse convolutional kernel; and performing output feature map grouping to sequentially stack the plurality of output feature maps to obtain an output feature map corresponding to the input feature map. (FIG. 2).

公开(公告)号：US20240312055A1

公开(公告)日：2024-09-19

申请号：US18569996

申请日：2021-12-10

Applicant: INTEL CORPORATION

Inventor： Shandong WANG ,  Yurong CHEN ,  Ming LU ,  Li XU ,  Anbang YAO

IPC: G06T7/73 ,  G06T7/80

CPC classification number: G06T7/74 ,  G06T7/80 ,  G06T2207/20084 ,  G06T2207/30196 ,  G06T2207/30221

Abstract: This disclosure describes systems, methods, and devices related to real-time multi-person three-dimensional pose tracking using a single camera. A method may include receiving, by a device, two-dimensional image data from a camera, the two-dimensional image data representing a first person and a second person; generating, based on the two-dimensional image data, two-dimensional positions of body parts represented by the first person; generating, using a deep neural network, based on the two-dimensional positions, a three-dimensional pose regression of the body parts represented by the first person; identifying, based on the two-dimensional positions and the three-dimensional pose regression, contact between a ground plane and a foot of the first person; generating an absolute three-dimensional position of the contact between the ground plane and the foot of the first person; generating, based on the absolute three-dimensional position, a three-dimensional pose of the body parts represented by the first person.

公开(公告)号：US20220230268A1

公开(公告)日：2022-07-21

申请号：US17517316

申请日：2021-11-02

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Dongqi CAI ,  Libin WANG ,  Lin XU ,  Ping HU ,  Shandong WANG ,  Wenhua CHENG ,  Yiwen GUO ,  Liu YANG ,  Yuqing HOU ,  Zhou SU

IPC: G06T1/20 ,  G06N20/00 ,  G06N3/00 ,  G06N3/04 ,  G06N3/08

Abstract: Described herein are advanced artificial intelligence agents for modeling physical interactions. In one embodiment, an apparatus to provide an active artificial intelligence (AI) agent includes at least one database to store physical interaction data and compute cluster coupled to the at least one database. The compute cluster automatically obtains physical interaction data from a data collection module without manual interaction, stores the physical interaction data in the at least one database, and automatically trains diverse sets of machine learning program units to simulate physical interactions with each individual program unit having a different model based on the applied physical interaction data.

公开(公告)号：US20250045573A1

公开(公告)日：2025-02-06

申请号：US18709267

申请日：2022-03-03

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Yikai WANG ,  Zhaole SUN ,  Yi YANG ,  Feng CHEN ,  Zhuo WANG ,  Shandong WANG ,  Yurong CHEN

IPC: G06N3/0495 ,  G06N3/0464

Abstract: The disclosure relates to decimal-bit network quantization of CNN models. Methods, apparatus, systems, and articles of manufacture for quantizing a CNN model includes, for a convolutional layer of the CNN model: allocating a 1-bit convolutional kernel subset to the convolutional layer, wherein the convolutional layer includes 32-bit or 16-bit floating-point convolutional kernels with a size of K×K and the 1-bit convolutional kernel subset includes 2N 1-bit convolutional kernel candidates with the size of K×K, 1≤N

公开(公告)号：US20230386072A1

公开(公告)日：2023-11-30

申请号：US18031564

申请日：2020-12-01

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Yangyuxuan KANG ,  Shandong WANG ,  Ming LU ,  Yurong CHEN ,  Wenjian SHAO ,  Yikai WANG ,  Haojun XU ,  Chao YU ,  Chong WONG

IPC: G06T7/73 ,  G06V40/10

CPC classification number: G06T7/73 ,  G06V40/103 ,  G06T2207/30196 ,  G06T2207/20084 ,  G06V10/82

Abstract: Techniques related to 3D pose estimation from a 2D input image are discussed. Such techniques include incrementally adjusting an initial 3D pose generated by applying a lifting network to a detected 2D pose in the 2D input image by projecting each current 3D pose estimate to a 2D pose projection, applying a residual regressor to features based on the 2D pose projection and the detected 2D pose, and combining a 3D pose increment from the residual regressor to the current 3D pose estimate.

公开(公告)号：US20200242734A1

公开(公告)日：2020-07-30

申请号：US16474927

申请日：2017-04-07

Applicant: INTEL CORPORATION

Inventor： Shandong WANG ,  Yiwen GUO ,  Anbang YAO ,  Dongqi CAI ,  Libin WANG ,  Lin XU ,  Ping HU ,  Wenhua CHENG ,  Yurong CHEN

IPC: G06T3/40 ,  G06T1/20 ,  G06N3/08 ,  G06N3/04 ,  G06N5/04 ,  G06N20/10 ,  G06N20/20 ,  G06K9/62

Abstract: Methods and systems are disclosed using improved Convolutional Neural Networks (CNN) for image processing. In one example, an input image is down-sampled into smaller images with a smaller resolution than the input image. The down-sampled smaller images are processed by a CNN having a last layer with a reduced number of nodes than a last layer of a full CNN used to process the input image at a full resolution. A result is outputted based on the processed down-sampled smaller images by the CNN having a last layer with a reduced number of nodes. In another example, shallow CNN networks are built randomly. The randomly built shallow CNN networks are combined to imitate a trained deep neural network (DNN).