公开(公告)号：US11635943B2

公开(公告)日：2023-04-25

申请号：US16475080

申请日：2017-04-07

Applicant: Intel Corporation

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

IPC: G06F7/58 ,  G06N3/04 ,  G06N3/084

Abstract: Described herein are hardware acceleration of random number generation for machine learning and deep learning applications. An apparatus (700) includes a uniform random number generator (URNG) circuit (710) to generate uniform random numbers and an adder circuit (750) that is coupled to the URNG circuit (710). The adder circuit hardware (750) accelerates generation of Gaussian random numbers for machine learning.

公开(公告)号：US11106896B2

公开(公告)日：2021-08-31

申请号：US16958542

申请日：2018-03-26

Applicant: INTEL CORPORATION

Inventor： Ping Hu ,  Anbang Yao ,  Yurong Chen ,  Dongqi Cai ,  Shandong Wang

IPC: G06F3/01 ,  G06K9/00 ,  G06K9/62 ,  G06N3/08

Abstract: Methods and apparatus for multi-task recognition using neural networks are disclosed. An example apparatus includes a filter engine to generate a facial identifier feature map based on image data, the facial identifier feature map to identify a face within the image data. The example apparatus also includes a sibling semantic engine to process the facial identifier feature map to generate an attribute feature map associated with a facial attribute. The example apparatus also includes a task loss engine to calculate a probability factor for the attribute, the probability factor identifying the facial attribute. The example apparatus also includes a report generator to generate a report indicative of a classification of the facial attribute.

公开(公告)号：US20210004572A1

公开(公告)日：2021-01-07

申请号：US16958542

申请日：2018-03-26

Applicant: INTEL CORPORATION

Inventor： Ping Hu ,  Anbang Yao ,  Yurong Chen ,  Dongqi Cai ,  Shandong Wang

IPC: G06K9/00 ,  G06F3/01 ,  G06N3/08

Abstract: Methods and apparatus for multi-task recognition using neural networks are disclosed. An example apparatus includes a filter engine to generate a facial identifier feature map based on image data, the facial identifier feature map to identify a face within the image data. The example apparatus also includes a sibling semantic engine to process the facial identifier feature map to generate an attribute feature map associated with a facial attribute. The example apparatus also includes a task loss engine to calculate a probability factor for the attribute, the probability factor identifying the facial attribute. The example apparatus also includes a report generator to generate a report indicative of a classification of the facial attribute.

公开(公告)号：US20240296668A1

公开(公告)日：2024-09-05

申请号：US18572510

申请日：2021-09-10

Applicant: Intel Corporation

Inventor： Dongqi Cai ,  Yurong Chen ,  Anbang Yao

IPC: G06V10/82 ,  G06V10/94

CPC classification number: G06V10/82 ,  G06V10/955

Abstract: Technology to conduct image sequence/video analysis can include a processor, and a memory coupled to the processor, the memory storing a neural network, the neural network comprising a plurality of convolution layers, and a plurality of normalization layers arranged as a relay structure, wherein each normalization layer is coupled to and following a respective one of the plurality of convolution layers. The plurality of normalization layers can be arranged as a relay structure where a normalization layer for a layer (k) is coupled to and following a normalization layer for a preceding layer (k−1). The normalization layer for the layer (k) is coupled to the normalization layer for the preceding layer (k−1) via a hidden state signal and a cell state signal, each signal generated by the normalization layer for the preceding layer (k−1). Each normalization layer (k) can include a meta-gating unit (MGU) structure.

公开(公告)号：US11157727B2

公开(公告)日：2021-10-26

申请号：US16647722

申请日：2017-12-27

Applicant: INTEL CORPORATION

Inventor： Ping Hu ,  Anbang Yao ,  Jia Wei ,  Dongqi Cai ,  Yurong Chen

IPC: G06K9/62 ,  G06K9/00 ,  G06K9/66 ,  G06N3/08

Abstract: Techniques are provided for neural network based, human attribute recognition, guided by anatomical key-points and statistic correlation models. Attributes include characteristics that can be visibly identified or inferred from an image, such as gender, hairstyle, clothing style, etc. A methodology implementing the techniques according to an embodiment includes applying an attribute feature extraction (AFE) convolutional neural network (CNN) to an image of a human to generate attribute feature maps based on the image. The method further includes applying a key-point guided proposal (KPG) CNN to the image of the human to generate proposed hierarchical regions of the image based on associated anatomical key-points. The method further includes generating recognition probabilities for the human attributes using a CNN combination layer that incorporates the attribute feature maps, the proposed hierarchical regions, and statistical correlation models (SCMs) which provide correlations between the features of the attribute feature maps and the proposed hierarchical regions.

公开(公告)号：US20200226362A1

公开(公告)日：2020-07-16

申请号：US16647722

申请日：2017-12-27

Applicant: INTEL CORPORATION

Inventor： Ping Hu ,  Anbang Yao ,  Jia Wei ,  Dongqi Cai ,  Yurong Chen

IPC: G06K9/00 ,  G06K9/66 ,  G06K9/62 ,  G06N3/08

Abstract: Techniques are provided for neural network based, human attribute recognition, guided by anatomical key-points and statistic correlation models. Attributes include characteristics that can be visibly identified or inferred from an image, such as gender, hairstyle, clothing style, etc. A methodology implementing the techniques according to an embodiment includes applying an attribute feature extraction (AFE) convolutional neural network (CNN) to an image of a human to generate attribute feature maps based on the image. The method further includes applying a key-point guided proposal (KPG) CNN to the image of the human to generate proposed hierarchical regions of the image based on associated anatomical key-points. The method further includes generating recognition probabilities for the human attributes using a CNN combination layer that incorporates the attribute feature maps, the proposed hierarchical regions, and statistical correlation models (SCMs) which provide correlations between the features of the attribute feature maps and the proposed hierarchical regions.

公开(公告)号：US12217163B2

公开(公告)日：2025-02-04

申请号：US18371934

申请日：2023-09-22

Applicant: Intel Corporation

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

IPC: G06K9/62 ,  G06F18/21 ,  G06F18/213 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V10/94 ,  G06V20/00

Abstract: Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps. The weighted feature maps are stored in the LSTM. A Q value is calculated for different actions based on the weighted feature maps stored in the LSTM.

公开(公告)号：US20240086693A1

公开(公告)日：2024-03-14

申请号：US18371934

申请日：2023-09-22

Applicant: Intel Corporation

Inventor： Yiwen GUO ,  Yuqing Hou ,  Anbang YAO ,  Dongqi Cai ,  Lin Xu ,  Ping Hu ,  Shandong Wang ,  Wenhua Cheng ,  Yurong Chen ,  Libin Wang

IPC: G06N3/063 ,  G06F18/21 ,  G06F18/213 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V10/94 ,  G06V20/00

CPC classification number: G06N3/063 ,  G06F18/213 ,  G06F18/2148 ,  G06F18/217 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V10/94 ,  G06V10/955 ,  G06V20/00

Abstract: Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps. The weighted feature maps are stored in the LSTM. A Q value is calculated for different actions based on the weighted feature maps stored in the LSTM.

公开(公告)号：US11803739B2

公开(公告)日：2023-10-31

申请号：US17584216

申请日：2022-01-25

Applicant: Intel Corporation

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

IPC: G06K9/62 ,  G06N3/063 ,  G06N3/08 ,  G06V10/94 ,  G06F18/21 ,  G06F18/213 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045 ,  G06V10/764 ,  G06V10/82 ,  G06V10/44 ,  G06V20/00

CPC classification number: G06N3/063 ,  G06F18/213 ,  G06F18/217 ,  G06F18/2148 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V10/94 ,  G06V10/955 ,  G06V20/00

Abstract: Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps. The weighted feature maps are stored in the LSTM. A Q value is calculated for different actions based on the weighted feature maps stored in the LSTM.

公开(公告)号：US20230290134A1

公开(公告)日：2023-09-14

申请号：US18019450

申请日：2020-09-25

Applicant: Intel Corporation

Inventor： Ping Hu ,  Anbang Yao ,  Xiaolong Liu ,  Yurong Chen ,  Dongqi Cai

IPC: G06V10/82 ,  G06N3/0464 ,  G06V40/16 ,  G06V10/77

CPC classification number: G06V10/82 ,  G06N3/0464 ,  G06V40/171 ,  G06V10/7715

Abstract: A method and system of multiple facial attributes recognition using highly efficient neural networks.