公开(公告)号：US20200167654A1

公开(公告)日：2020-05-28

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 ,  G06F16/22 ,  G06N5/00 ,  G06N3/04

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：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.

公开(公告)号：US20190073553A1

公开(公告)日：2019-03-07

申请号：US16070483

申请日：2016-02-17

Applicant: INTEL CORPORATION

Inventor： Anbang YAO ,  Tao KONG ,  Yurong CHEN

IPC: G06K9/46 ,  G06N3/04 ,  G06K9/62 ,  G06K9/32 ,  G06N3/08

Abstract: Region proposal is described for image regions that include objects of interest. Feature maps from multiple layers of a convolutional neural network model are used. In one example a digital image is received and buffered. Layers of convolution are performed on the image to generate feature maps. The feature maps are reshaped to a single size. The reshaped feature maps are grouped by sequential concatenation to form a combined feature map. Region proposals are generated using the combined feature map by scoring bounding box regions of the image. Objects are detected and classified objects in the proposed regions using the feature maps.

公开(公告)号：US20180018524A1

公开(公告)日：2018-01-18

申请号：US15300490

申请日：2015-12-16

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Ruoyan WANG ,  Yurong CHEN

IPC: G06K9/00 ,  G06K9/66 ,  G06K9/46

CPC classification number: G06K9/00771 ,  G06K9/00791 ,  G06K9/4628 ,  G06K9/6292 ,  G06K9/66 ,  G06K9/6857

Abstract: A fully convolutional pyramid network and method for object (e.g., pedestrian) detection are disclosed. In one embodiment, the object detection system is a pedestrian detection system that comprises: a multi-scale image generator to generate a set of images from an input image, the set of images being versions of the input image at different scales; a human body-specific fully convolutional network (FCN) model operable to generate a set of detection results for each image in the set of images that is indicative of objects that are potentially of human bodies; and a post processor to combine sets of detection results generated by the FCN model for the set of images into an output image with each object location determined as potentially being a human body being marked.

公开(公告)号：US20170308742A1

公开(公告)日：2017-10-26

申请号：US15116894

申请日：2015-09-16

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Junchao SHAO ,  Yurong CHEN

IPC: G06K9/00

CPC classification number: G06K9/00308 ,  G06K9/00 ,  G06K9/00221 ,  G06K9/00281 ,  G06K9/00302

Abstract: Facial expressions are recognized using relations determined by class-to-class comparisons. In one example, descriptors are determined for each of a plurality of facial expression classes. Pair-wise facial expression class-to-class tasks are defined. A set of discriminative image patches are learned for each task using labelled training images. Each image patch is a portion of an image. Differences in the learned image patches in each training image are determined for each task. A relation graph is defined for each image for each task using the differences. A final descriptor is determined for each image by stacking and concatenating the relation graphs for each task. Finally, the final descriptors of the images of the are fed into a training algorithm to learn a final facial expression model.