Ning Zhang, 55Yorba Linda, CA

2022032, Oct 13, 2022

Jun 22, 2022

17/808274

- Santa Monica CA, US
Shao-Hua Sun - Los Angeles CA, US
Ning Zhang - Los Angeles CA, US

G06N 3/04
G06V 30/194
G06N 3/08

Disclosed is a feedback adversarial learning framework, a recurrent framework for generative adversarial networks that can be widely adapted to not only stabilize training but also generate higher quality images. In some aspects, a discriminator's spatial outputs are distilled to improve generation quality. The disclosed embodiments model the discriminator into the generator, and the generator learns from its mistakes over time. In some aspects, a discriminator architecture encourages the model to be locally and globally consistent.

2022029, Sep 15, 2022

May 26, 2022

17/825994

- Santa Monica CA, US
Linjie Luo - Los Angeles CA, US
Ning Zhang - Los Angeles CA, US
Jia Li - Marina Del Ray CA, US

G06T 7/33
G06V 10/25
G06V 10/44

Dense feature scale detection can be implemented using multiple convolutional neural networks trained on scale data to more accurately and efficiently match pixels between images. An input image can be used to generate multiple scaled images. The multiple scaled images are input into a feature net, which outputs feature data for the multiple scaled images. An attention net is used to generate an attention map from the input image. The attention map assigns emphasis as a soft distribution to different scales based on texture analysis. The feature data and the attention data can be combined through a multiplication process and then summed to generate dense features for comparison.

2023001, Jan 12, 2023

Apr 25, 2022

17/660462

- Santa Monica CA, US
Linjie Luo - Los Angeles CA, US
Xuecheng Nie - Singapore, SG
Ning Zhang - Los Angeles CA, US

G06T 7/246
G06T 7/73
G06V 20/40

Systems, devices, media and methods are presented for a human pose tracking framework. The human pose tracking framework may identify a message with video frames, generate, using a composite convolutional neural network, joint data representing joint locations of a human depicted in the video frames, the generating of the joint data by the composite convolutional neural network done by a deep convolutional neural network operating on one portion of the video frames, a shallow convolutional neural network operating on a another portion of the video frames, and tracking the joint locations using a one-shot learner neural network that is trained to track the joint locations based on a concatenation of feature maps and a convolutional pose machine. The human pose tracking framework may store, the joint locations, and cause presentation of a rendition of the joint locations on a user interface of a client device.

2023000, Jan 5, 2023

Jul 11, 2022

17/811822

- Santa Monica CA, US
Kevin Dechau Tang - Los Angeles CA, US
Ning Zhang - Los Angeles CA, US

G06F 40/169
G06F 3/0482
G06F 3/04842
G06T 11/60
H04W 4/80

A system according to various exemplary embodiments includes a processor and a user interface, communication module, and memory coupled to the processor. The memory stores instructions that, when executed by the processor, cause the system to: retrieve a digital image from a server using the communication module; present the digital image on a display of the user interface; receive edits to the digital image via the user interface; generate, based on the edits, a modified digital image, wherein generating the modified digital image includes transforming a format of the digital image to include a field containing an identifier associated with the modified digital image; and transmit the modified digital image to the server using the communication module.

2021025, Aug 19, 2021

May 3, 2021

17/306010

- Santa Monica CA, US
Leonardo Ribas Machado das Neves - Marina Del Rey CA, US
Vitor Rocha de Carvalho - San Diego CA, US
Ning Zhang - Los Angeles CA, US

G06F 40/295
G06F 40/30
G06N 3/08
G06N 20/00

A caption of a multimodal message (e.g., social media post) can be identified as a named entity using an entity recognition system. The entity recognition system can use a visual attention based mechanism to generate a visual context representation from an image and caption. The system can use the visual context representation to identify one or more terms of the caption as a named entity.

2021025, Aug 19, 2021

Apr 30, 2021

17/302361

- Santa Monica CA, US
Rahul Bhupendra Sheth - Los Angeies CA, US
Ning Zhang - Los Angeles CA, US

G06T 9/00
H04L 12/58
G06N 3/04

Disclosed are methods for encoding information in a graphic image. The information may be encoded so as to have a visual appearance that adopts a particular style, so that the encoded information is visually pleasing in the environment in which it is displayed. An encoder and decoder are trained during an integrated training process, where the encoder is tuned to minimize a loss when its encoded images are decoded. Similarly, the decoder is also trained to minimize loss when decoding the encoded images. Both the encoder and decoder may utilize a convolutional neural network in some aspects to analyze data and/or images. Once data is encoded, a style from a sample image is transferred to the encoded data. When decoding, the decoder may largely ignore the style aspects of the encoded data and decode based on a content portion of the data.

2021012, Apr 29, 2021

Nov 5, 2020

16/949594

- Santa Monica CA, US
Linjie Luo - Los Angeles CA, US
Xuecheng Nie - Singapore, SG
Ning Zhang - Los Angeles CA, US

G06T 7/246
G06T 7/73
G06K 9/00

Systems, devices, media and methods are presented for a human pose tracking framework. The human pose tracking framework may identify a message with video frames, generate, using a composite convolutional neural network, joint data representing joint locations of a human depicted in the video frames, the generating of the joint data by the composite convolutional neural network done by a deep convolutional neural network operating on one portion of the video frames, a shallow convolutional neural network operating on a another portion of the video frames, and tracking the joint locations using a one-shot learner neural network that is trained to track the joint locations based on a concatenation of feature maps and a convolutional pose machine. The human pose tracking framework may store, the joint locations, and cause presentation of a rendition of the joint locations on a user interface of a client device.

2021007, Mar 11, 2021

Nov 17, 2020

16/949856

- Santa Monica CA, US
Jianchao Yang - Los Angeles CA, US
Ning Zhang - Los Angeles CA, US
Jia Li - Marina Del Rey CA, US

G06K 9/62
G06K 9/46
G06K 9/52
G06K 9/66
G06T 3/40

Systems, devices, media, and methods are presented for identifying and categorically labeling objects within a set of images. The systems and methods receive an image depicting an object of interest, detect at least a portion of the object of interest within the image using a multilayer object model, determine context information, and identify the object of interest included in two or more bounding boxes.