公开(公告)号：US20240005628A1

公开(公告)日：2024-01-04

申请号：US18031064

申请日：2020-11-19

Applicant: Intel Corporation

Inventor： Dongqi CAI ,  Anbang YAO ,  Yikai WANG ,  Ming LU ,  Yurong CHEN

IPC: G06V10/44 ,  G06V10/80 ,  G06V10/82

CPC classification number: G06V10/454 ,  G06V10/82 ,  G06V10/811 ,  G06V10/806

Abstract: Techniques related to bidirectional compact deep fusion networks for multimodal image inputs are discussed. Such techniques include applying a shared convolutional layer and independent batch normalization layers to input volumes for each modality and fusing features from the resultant output volumes in both directions across the modalities.

公开(公告)号：US20230359873A1

公开(公告)日：2023-11-09

申请号：US18142997

申请日：2023-05-03

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Hao ZHAO ,  Ming LU ,  Yiwen GUO ,  Yurong CHEN

IPC: G06N3/063 ,  G06N3/04 ,  G06N3/08 ,  G06V10/82 ,  G06F18/214 ,  G06V10/764 ,  G06V10/44 ,  G06V20/70 ,  G06V10/94 ,  G06V20/10 ,  G06V20/40

CPC classification number: G06N3/063 ,  G06N3/04 ,  G06N3/08 ,  G06V10/82 ,  G06F18/214 ,  G06V10/764 ,  G06V10/454 ,  G06V20/70 ,  G06V10/955 ,  G06V20/10 ,  G06V20/41

Abstract: Methods and apparatus for discrimitive semantic transfer and physics-inspired optimization in deep learning are disclosed. A computation training method for a convolutional neural network (CNN) includes receiving a sequence of training images in the CNN of a first stage to describe objects of a cluttered scene as a semantic segmentation mask. The semantic segmentation mask is received in a semantic segmentation network of a second stage to produce semantic features. Using weights from the first stage as feature extractors and weights from the second stage as classifiers, edges of the cluttered scene are identified using the semantic features.

公开(公告)号：US20240370716A1

公开(公告)日：2024-11-07

申请号：US18769906

申请日：2024-07-11

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Hao ZHAO ,  Ming LU ,  Yiwen GUO ,  Yurong CHEN

IPC: G06N3/063 ,  G06F18/214 ,  G06N3/04 ,  G06N3/08 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V10/94 ,  G06V20/10 ,  G06V20/40 ,  G06V20/70

Abstract: Methods and apparatus for discrimitive semantic transfer and physics-inspired optimization in deep learning are disclosed. A computation training method for a convolutional neural network (CNN) includes receiving a sequence of training images in the CNN of a first stage to describe objects of a cluttered scene as a semantic segmentation mask. The semantic segmentation mask is received in a semantic segmentation network of a second stage to produce semantic features. Using weights from the first stage as feature extractors and weights from the second stage as classifiers, edges of the cluttered scene are identified using the semantic features.

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：US20230377335A1

公开(公告)日：2023-11-23

申请号：US18030452

申请日：2020-11-10

Applicant: Intel Corporation

Inventor： Liwei LIAO ,  Ming LU ,  Haihua LIN ,  Xiaofeng TONG ,  Wenlong LI

IPC: G06V20/40 ,  G06V10/82 ,  G06T7/73

CPC classification number: G06V20/42 ,  G06V20/46 ,  G06V10/82 ,  G06T7/73 ,  G06T2207/10016 ,  G06T2207/30196 ,  G06T2207/30242 ,  G06T2207/20084 ,  G06T2207/30224

Abstract: Techniques related to key person recognition in multi-camera immersive video attained for a scene are discussed. Such techniques include detecting predefined person formations in the scene based on an arrangement of the persons in the scene, generating a feature vector for each person in the detected formation, and applying a classifier to the feature vectors to indicate one or more key persons in the scene.

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：US20240176998A1

公开(公告)日：2024-05-30

申请号：US18431458

申请日：2024-02-02

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Hao ZHAO ,  Ming LU ,  Yiwen GUO ,  Yurong CHEN

IPC: G06N3/063 ,  G06F18/214 ,  G06N3/04 ,  G06N3/08 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V10/94 ,  G06V20/10 ,  G06V20/40 ,  G06V20/70

CPC classification number: G06N3/063 ,  G06F18/214 ,  G06N3/04 ,  G06N3/08 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V10/955 ,  G06V20/10 ,  G06V20/41 ,  G06V20/70

Abstract: Methods and apparatus for discrimitive semantic transfer and physics-inspired optimization in deep learning are disclosed. A computation training method for a convolutional neural network (CNN) includes receiving a sequence of training images in the CNN of a first stage to describe objects of a cluttered scene as a semantic segmentation mask. The semantic segmentation mask is received in a semantic segmentation network of a second stage to produce semantic features. Using weights from the first stage as feature extractors and weights from the second stage as classifiers, edges of the cluttered scene are identified using the semantic features.

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：US20210158033A1

公开(公告)日：2021-05-27

申请号：US17057020

申请日：2018-10-02

Applicant: Intel Corporation

Inventor： Chen LING ,  Ming LU ,  Qiang LI ,  Wenlong LI ,  Xiaofeng TONG ,  Yikai FANG ,  Yumeng WANG

IPC: G06K9/00

Abstract: A video capture and processing system includes a memory configured to store a pose database. The pose database includes poses that indicate a start or stoppage in an event. The system also includes a processor operatively coupled to the memory. The processor is configured to generate a pose of an individual in a video frame of captured video of the event. The pose can be three-dimensional pose or a two-dimensional pose. The processor is also configured to determine, based on the pose database, whether the pose of the individual indicates a start or a stoppage in the event. The processor is further configured to control an upload of video of the event based on the determination of whether the pose indicates the start or the stoppage in the event.