公开(公告)号：US12079914B2

公开(公告)日：2024-09-03

申请号：US17914314

申请日：2020-04-23

Applicant: Intel Corporation

Inventor： Shandong Wang ,  Yangyuxuan Kang ,  Anbang Yao ,  Ming Lu ,  Yurong Chen

IPC: G06T13/40 ,  G06T7/246 ,  G06T7/70 ,  G06T17/00

CPC classification number: G06T13/40 ,  G06T7/70 ,  G06T7/251 ,  G06T17/00 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30196

Abstract: Techniques are disclosed for providing improved pose tracking of a subject using a 2D camera and generating a 3D image that recreates the pose of the subject. A 3D skeleton map is estimated from a 2D skeleton map of the subject using, for example, a neural network. A template 3D skeleton map is accessed or generated having bone segments that have lengths set using, for instance, anthropometry statistics based on a given height of the template 3D skeleton map. An improved 3D skeleton map is then produced by at least retargeting one or more of the plurality of bone segments of the estimated 3D skeleton map to more closely match the corresponding template bone segments of the template 3D skeleton map. The improved 3D skeleton map can then be animated in various ways (e.g., using various skins or graphics) to track corresponding movements of the subject.

公开(公告)号：US20240273873A1

公开(公告)日：2024-08-15

申请号：US18563305

申请日：2021-09-01

Applicant: Intel Corporation

Inventor： Dongqi Cai ,  Anbang Yao ,  Yurong Chen

IPC: G06V10/77 ,  G06V10/32 ,  G06V10/82

CPC classification number: G06V10/7715 ,  G06V10/32 ,  G06V10/82

Abstract: Techniques related to application of deep neural networks to video for video recognition and understanding are discussed. A feature map of a deep neural network for a current time stamp of input video is standardized to a standardized feature map and pooled to a feature vector. The feature vector and transform parameters for a prior time stamp are used to generate transform parameters for the current time stamp based on application of a meta temporal relay. The resultant current time stamp transform parameters, such as a hidden state and a cell state of the meta temporal relay, are used to transform the standardized feature map to a normalized feature map for use by a subsequent layer of the deep neural network.

公开(公告)号：US11790223B2

公开(公告)日：2023-10-17

申请号：US16475076

申请日：2017-04-07

Applicant: INTEL CORPORATION

Inventor： Libin Wang ,  Yiwen Guo ,  Anbang Yao ,  Dongqi Cai ,  Lin Xu ,  Ping Hu ,  Shandong Wang ,  Wenhua Cheng ,  Yurong Chen

IPC: G06N3/08 ,  G06N3/063 ,  G06T1/20 ,  G06F18/21 ,  G06F18/214 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06F18/217 ,  G06F18/2148 ,  G06N3/045 ,  G06N3/063 ,  G06T1/20

Abstract: Methods and systems are disclosed for boosting deep neural networks for deep learning. In one example, in a deep neural network including a first shallow network and a second shallow network, a first training sample is processed by the first shallow network using equal weights. A loss for the first shallow network is determined based on the processed training sample using equal weights. Weights for the second shallow network are adjusted based on the determined loss for the first shallow network. A second training sample is processed by the second shallow network using the adjusted weights. In another example, in a deep neural network including a first weak network and a second weak network, a first subset of training samples is processed by the first weak network using initialized weights. A classification error for the first weak network on the first subset of training samples is determined. The second weak network is boosted using the determined classification error of the first weak network with adjusted weights. A second subset of training samples is processed by the second weak network using the adjusted weights.

公开(公告)号：US20230274132A1

公开(公告)日：2023-08-31

申请号：US18005804

申请日：2020-08-26

Applicant: Intel Corporation

Inventor： Dongqi Cai ,  Anbang Yao ,  Yurong Chen

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Methods, apparatus, systems, and articles of manufacture to dynamically normalize data in neural networks are disclosed. An apparatus for use with a machine learning model includes at least one normalization calculator to generate a plurality of alternate normalized outputs associated with input data for the machine learning model. Different ones of the alternate normalized outputs based on different normalization techniques. The apparatus further includes a soft weighting engine to generate a plurality of soft weights based on the input data. The apparatus also includes a normalized output generator to generate a final normalized output based on the plurality of alternate normalized outputs and the plurality of soft weights.

公开(公告)号：US11704569B2

公开(公告)日：2023-07-18

申请号：US16615097

申请日：2018-05-23

Applicant: INTEL CORPORATION

Inventor： Yiwen Guo ,  Anbang Yao ,  Hao Zhao ,  Ming Lu ,  Yurong Chen

IPC: G06N3/08 ,  G06N3/082 ,  G06N3/042 ,  G06N3/044 ,  G06N5/01

CPC classification number: G06N3/082 ,  G06N3/042 ,  G06N3/044 ,  G06N5/01

Abstract: Methods and apparatus are disclosed for enhancing a binary weight neural network using a dependency tree. A method of enhancing a convolutional neural network (CNN) having binary weights includes constructing a tree for obtained binary tensors, the tree having a plurality of nodes beginning with a root node in each layer of the CNN. A convolution is calculated of an input feature map with an input binary tensor at the root node of the tree. A next node is searched from the root node of the tree and a convolution is calculated at the next node using a previous convolution result calculated at the root node of the tree. The searching of a next node from root node is repeated for all nodes from the root node of the tree, and a convolution is calculated at each next node using a previous convolution result.

公开(公告)号：US11663249B2

公开(公告)日：2023-05-30

申请号：US16650853

申请日：2018-01-30

Applicant: INTEL CORPORATION

Inventor： Zhou Su ,  Jianguo Li ,  Yinpeng Dong ,  Yurong Chen

IPC: G06F16/33 ,  G06F16/332 ,  G06N3/049 ,  G06N5/025 ,  G06N3/045

CPC classification number: G06F16/3329 ,  G06N3/045 ,  G06N3/049 ,  G06N5/025

Abstract: An example apparatus for visual question answering includes a receiver to receive an input image and a question. The apparatus also includes an encoder to encode the input image and the question into a query representation including visual attention features. The apparatus includes a knowledge spotter to retrieve a knowledge entry from a visual knowledge base pre-built on a set of question-answer pairs. The apparatus further includes a joint embedder to jointly embed the visual attention features and the knowledge entry to generate visual-knowledge features. The apparatus also further includes an answer generator to generate an answer based on the query representation and the visual-knowledge features.

公开(公告)号：US11594010B2

公开(公告)日：2023-02-28

申请号：US17510013

申请日：2021-10-25

Applicant: Intel Corporation

Inventor： Libin Wang ,  Anbang Yao ,  Jianguo Li ,  Yurong Chen

IPC: G06K9/62 ,  G06V10/44 ,  G06N3/04

Abstract: An example apparatus for semantic image segmentation includes a receiver to receive an image to be segmented. The apparatus also includes a gated dense pyramid network including a plurality of gated dense pyramid (GDP) blocks to be trained to generate semantic labels for respective pixels in the received image. The apparatus further includes a generator to generate a segmented image based on the generated semantic labels.

公开(公告)号：US11551335B2

公开(公告)日：2023-01-10

申请号：US16474848

申请日：2017-04-07

Applicant: Intel Corporation

Inventor： Lin Xu ,  Liu Yang ,  Anbang Yao ,  Dongqi Cai ,  Libin Wang ,  Ping Hu ,  Shandong Wang ,  Wenhua Cheng ,  Yiwen Guo ,  Yurong Chen

IPC: G06T5/00 ,  G06N20/10 ,  G06F9/50 ,  G06N3/08 ,  G06N5/04 ,  H04N9/07 ,  H04N9/64

Abstract: Methods and systems are disclosed using camera devices for deep channel and Convolutional Neural Network (CNN) images and formats. In one example, image values are captured by a color sensor array in an image capturing device or camera. The image values provide color channel data. The captured image values by the color sensor array are input to a CNN having at least one CNN layer. The CNN provides CNN channel data for each layer. The color channel data and CNN channel data is to form a deep channel image that stored in a memory. In another example, image values are captured by sensor array. The captured image values by the sensor array are input a CNN having a first CNN layer. An output is generated at the first CNN layer using the captured image values by the color sensor array. The output of the first CNN layer is stored as a feature map of the captured image.

公开(公告)号：US20220207359A1

公开(公告)日：2022-06-30

申请号：US17485406

申请日：2021-09-25

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Dongqi Cai ,  Yurong Chen ,  Wenjian Shao ,  Feng Chen

IPC: G06N3/08 ,  G06T3/40 ,  G06T3/00 ,  G06N3/04

Abstract: Embodiments are generally directed to methods and apparatuses for dynamic normalization and relay in a neural network. An embodiment of an apparatus for dynamic normalization and relay in a neural network including a hyper normalization layer comprises: a compute engine to: generate a hidden state and a cell state for the hyper normalization layer based on an input feature map for the hyper normalization layer as well as a previous hidden state and a previous cell state; and normalize the input feature map in the hyper normalization layer with the hidden state and the cell state for the hyper normalization layer.

公开(公告)号：US11341368B2

公开(公告)日：2022-05-24

申请号：US16475079

申请日：2017-04-07

Applicant: INTEL CORPORATION

Inventor： Anbang Yao ,  Shandong Wang ,  Wenhua Cheng ,  Dongqi Cai ,  Libin Wang ,  Lin Xu ,  Ping Hu ,  Yiwen Guo ,  Liu Yang ,  Yuqing Hou ,  Zhou Su ,  Yurong Chen

IPC: G06K9/62 ,  G06N3/08 ,  G06T1/20 ,  G06V10/94

Abstract: Methods and systems for advanced and augmented training of deep neural networks (DNNs) using synthetic data and innovative generative networks. A method includes training a DNN using synthetic data, training a plurality of DNNs using context data, associating features of the DNNs trained using context data with features of the DNN trained with synthetic data, and generating an augmented DNN using the associated features.