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公开(公告)号：US11537851B2
公开(公告)日：2022-12-27
申请号：US16475075
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 , Yurong Chen
Abstract: Methods and systems are disclosed using improved training and learning for deep neural networks. In one example, a deep neural network includes a plurality of layers, and each layer has a plurality of nodes. The nodes of each L layer in the plurality of layers are randomly connected to nodes of an L+1 layer. The nodes of each L+1 layer are connected to nodes in a subsequent L layer in a one-to-one manner. Parameters related to the nodes of each L layer are fixed. Parameters related to the nodes of each L+1 layers are updated. In another example, inputs for the input layer and labels for the output layer of a deep neural network are determined related to a first sample. A similarity between different pairs of inputs and labels is estimated using a Gaussian regression process.
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公开(公告)号：US20220222492A1
公开(公告)日：2022-07-14
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
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.
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93.发明申请公开(公告)号：US20220207656A1
公开(公告)日：2022-06-30
申请号：US17483074
申请日：2021-09-23
Applicant: Intel Corporation
Inventor： Anbang Yao , Ming Lu , Yikai Wang , Yurong Chen , Attila Tamas Afra , Sungye Kim , Karthik Vaidyanathan
Abstract: Embodiments are generally directed to a Conditional Kernel Prediction Network (CKPN) for image and video de-noising and other related image and video processing applications. Disclosed is an embodiment of a method for de-noising an image or video frame by a convolutional neural network implemented on a compute engine, the image including a plurality of pixels, the method comprising: for each of the plurality of pixels of the image, generating a convolutional kernel having a plurality of kernel weights for the pixel, the plurality of kernel weights respectively corresponding to pixels within a region surrounding the pixel; adjusting the plurality of kernel weights of the convolutional kernel for the pixel based on convolutional kernels generated respectively for the corresponding pixels within the region surrounding the pixel; and filtering the pixel with the adjusted plurality of kernel weights and pixel values of the corresponding pixels within the region surrounding the pixel to obtain a de-noised pixel.
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公开(公告)号：US11263490B2
公开(公告)日：2022-03-01
申请号：US16475078
申请日：2017-04-07
Applicant: INTEL CORPORATION
Inventor： Yiwen Guo , Yuqing Hou , Anbang Yao , Dongqi Cai , Lin Xu , Ping Hu , Shandong Wang , Wenhua Cheng , Yurong Chen , Libin Wang
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.
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公开(公告)号：US11263489B2
公开(公告)日：2022-03-01
申请号：US16616533
申请日：2017-06-29
Applicant: INTEL CORPORATION
Inventor： Yurong Chen , Jianguo Li , Zhou Su , Zhiqiang Shen
IPC: G06K9/00 , G06K9/62 , G06F40/169 , G06N3/08
Abstract: Techniques and apparatus for generating dense natural language descriptions for video content are described. In one embodiment, for example, an apparatus may include at least one memory and logic, at least a portion of the logic comprised in hardware coupled to the at least one memory, the logic to receive a source video comprising a plurality of frames, determine a plurality of regions for each of the plurality of frames, generate at least one region-sequence connecting the determined plurality of regions, apply a language model to the at least one region-sequence to generate description information comprising a description of at least a portion of content of the source video. Other embodiments are described and claimed.
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Patent Agency Ranking
公开(公告)号：US11244191B2
公开(公告)日：2022-02-08
申请号：US16070483
申请日：2016-02-17
Applicant: INTEL CORPORATION
Inventor： Anbang Yao , Tao Kong , Yurong Chen
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.
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97.发明授权公开(公告)号：US11188794B2
公开(公告)日：2021-11-30
申请号：US16630419
申请日：2017-08-10
Applicant: INTEL CORPORATION
Inventor： Anbang Yao , Tao Kong , Ming Lu , Yiwen Guo , Yurong Chen
Abstract: A convolutional neural network framework is described that uses reverse connection and obviousness priors for object detection. A method includes performing a plurality of layers of convolutions and reverse connections on a received image to generate a plurality of feature maps, determining an objectness confidence for candidate bounding boxes based on outputs of an objectness prior, determining a joint loss function for each candidate bounding box by combining an objectness loss, a bounding box regression loss and a classification loss, calculating network gradients over positive boxes and negative boxes, updating network parameters within candidate bounding boxes using the joint loss function, repeating performing the convolutions through to updating network parameters until the training converges, and outputting network parameters for object detection based on the training images.
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公开(公告)号：US11151361B2
公开(公告)日：2021-10-19
申请号：US16471106
申请日：2017-01-20
Applicant: INTEL CORPORATION
Inventor： Anbang Yao , Dongqi Cai , Ping Hu , Shandong Wang , Yurong Chen
Abstract: An apparatus for dynamic emotion recognition in unconstrained scenarios is described herein. The apparatus comprises a controller to pre-process image data and a phase-convolution mechanism to build lower levels of a CNN such that the filters form pairs in phase. The apparatus also comprises a phase-residual mechanism configured to build middle layers of the CNN via plurality of residual functions and an inception-residual mechanism to build top layers of the CNN by introducing multi-scale feature extraction. Further, the apparatus comprises a fully connected mechanism to classify extracted features.
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公开(公告)号：US11132575B2
公开(公告)日：2021-09-28
申请号：US16710827
申请日：2019-12-11
Applicant: INTEL CORPORATION
Inventor： Anbang Yao , Yurong Chen
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.
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公开(公告)号：US20210201078A1
公开(公告)日：2021-07-01
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 , Yuging Hou , Zhou Su , Yurong Chen
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.
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