公开(公告)号：US20250068891A1

公开(公告)日：2025-02-27

申请号：US18724510

申请日：2022-02-18

Applicant: Intel Corporation

Inventor： Dongqi CAI ,  Anbang YAO ,  Chao LI ,  Yurong CHEN ,  Wenjian SHAO

IPC: G06N3/0464

Abstract: Methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement dynamic triplet convolution for convolutional neural networks are disclosed. An example apparatus disclosed herein for a convolutional neural network is to calculate one or more scalar kernels based on an input feature map applied to a layer of the convolutional neural network, ones of the one or more scalar kernels corresponding to respective dimensions of a static multidimensional convolutional filter associated with the layer of the convolutional neural network. The disclosed example apparatus is also to scale elements of the static multidimensional convolutional filter along a first one of the dimensions based on a first one of the one or more scalar kernels corresponding to the first one of the dimensions to determine a dynamic multidimensional convolutional filter associated with the layer of the convolutional neural network.

公开(公告)号：US20230368493A1

公开(公告)日：2023-11-16

申请号：US18030024

申请日：2020-11-13

Applicant: Intel Corporation

Inventor： Yuqing HOU ,  Xiaolong LIU ,  Anbang YAO ,  Yurong CHEN

IPC: G06V10/764 ,  G06V10/82 ,  G06V10/776

CPC classification number: G06V10/764 ,  G06V10/82 ,  G06V10/776

Abstract: A method and system of image hashing object detection for image processing are provided. The method comprises the following steps: obtaining image head class input data and image tail class input data differentiated from the head class input data and respectively of two images each of an object to be classified; respectively inputting the head and tail class input data into two separate parallel representation neural networks being trained to respectively generate head and tail features, wherein the representation neural networks share at least some representation weights used to form the head and tail features; inputting the head and tail features into at least one classifier neural network to generate class-related data; generating a class-balanced loss of at least one of the classes of the class-related data comprising factoring an effective number of samples of individual classes; and rebalancing an output sample distribution among the classes at the representation neural networks, classifier neural networks, or both by using the class-balanced loss.

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：US20180137383A1

公开(公告)日：2018-05-17

申请号：US15573631

申请日：2015-06-26

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Yurong CHEN

IPC: G06K9/32 ,  G06K9/00 ,  G06K9/62

Abstract: Combinatorial shape regression is described as a technique for face alignment and facial landmark detection in images. As described stages of regression may be built for multiple ferns for a facial landmark detection system. In one example a regression is performed on a training set of images using face shapes, using facial component groups, and using individual face point pairs to learn shape increments for each respective image in the set of images. A fern is built based on this regression. Additional regressions are performed for building additional ferns. The ferns are then combined to build the facial landmark detection system.

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：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：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：US20170286759A1

公开(公告)日：2017-10-05

申请号：US15512503

申请日：2014-10-23

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Yurong CHEN

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/46

CPC classification number: G06K9/00308 ,  G06K9/00241 ,  G06K9/00281 ,  G06K9/00315 ,  G06K9/4638 ,  G06K9/6234

Abstract: A system, article, and method to provide facial expression recognition using linear relationships within landmark subsets.

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.