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：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：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：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：US20230410496A1

公开(公告)日：2023-12-21

申请号：US18252164

申请日：2020-12-23

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Bo LIU ,  Ming LU ,  Feng CHEN ,  Yurong CHEN

IPC: G06V10/82

CPC classification number: G06V10/82

Abstract: Omni-scale convolution for convolutional neural networks is disclosed. An example of an apparatus includes one or more processors to process data, including processing for a convolutional neural network (CNN); and a memory to store data, including CNN data, wherein processing of input data by the CNN includes implementing omni-scale convolution in one or more convolutional layers of the CNN, implementation of the omni-scale convolution into a convolutional layer of the one or more convolutional layers including at least applying multiple dilation rates in a plurality of kernels of a kernel lattice of the convolutional layer, and applying a cyclic pattern for the multiple dilation rates in the plurality of kernels of the convolutional layer.

公开(公告)号：US20200082198A1

公开(公告)日：2020-03-12

申请号：US16609732

申请日：2018-05-22

Applicant: INTEL CORPORATION

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

IPC: G06K9/46 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

Abstract: Methods and apparatus for discriminative 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.