Zhaowen Wang

20130132316, May 23, 2013

Jun 7, 2012

13/491108

Jinjun Wang - San Jose CA, US
Zhaowen Wang - Urbana IL, US
Jing Xiao - Cupertino CA, US

G06N 5/02

706 46

Embodiments of the present invention include systems and methods for improved state space modeling (SSM) comprising two added layers to model the substructure transition dynamics and action duration distribution. In embodiments, the first layer represents a substructure transition model that encodes the sparse and global temporal transition probability. In embodiments, the second layer models the action boundary characteristics by injecting discriminative information into a logistic duration model such that transition boundaries between successive actions can be located more accurately; thus, the second layer exploits discriminative information to discover action boundaries adaptively.

20220414314, Dec 29, 2022

Jun 29, 2021

17/362031

- San Jose CA, US
Zhaowen Wang - San Jose CA, US
Hailin Jin - San Jose CA, US
Matthew Fisher - San Francisco CA, US

G06F 40/109
G06T 11/20
G06N 3/04

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating scalable and semantically editable font representations utilizing a machine learning approach. For example, the disclosed systems generate a font representation code from a glyph utilizing a particular neural network architecture. For example, the disclosed systems utilize a glyph appearance propagation model and perform an iterative process to generate a font representation code from an initial glyph. Additionally, using a glyph appearance propagation model, the disclosed systems automatically propagate the appearance of the initial glyph from the font representation code to generate additional glyphs corresponding to respective glyph labels. In some embodiments, the disclosed systems propagate edits or other changes in appearance of a glyph to other glyphs within a glyph set (e.g., to match the appearance of the edited glyph).

20220319065, Oct 6, 2022

Mar 31, 2021

17/219391

- San Jose CA, US
Zhaowen Wang - San Jose CA, US
Zhifei Zhang - San Jose CA, US

G06T 11/00
G06T 5/00
G06T 11/60
G06K 9/34

This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract a texture from embedded text within a digital image utilizing kerning-adjusted glyphs. For example, the disclosed systems utilize text recognition and text segmentation to identify and segment glyphs from embedded text depicted in a digital image. Subsequently, in some implementations, the disclosed systems determine optimistic kerning values between consecutive glyphs and utilize the kerning values to reduce gaps between the consecutive glyphs. Furthermore, in one or more implementations, the disclosed systems generate a synthesized texture utilizing the kerning-value-adjusted glyphs by utilizing image inpainting on the textures corresponding to the kerning-value-adjusted glyphs. Moreover, in certain instances, the disclosed systems apply a target texture to a target digital text based on the generated synthesized texture.

20220295149, Sep 15, 2022

Mar 12, 2021

17/200691

- San Jose CA, US
Zhankui He - San Diego CA, US
Zhe Lin - Bellevue WA, US
Zhaowen Wang - San Jose CA, US
Ajinkya Gorakhnath Kale - San Jose CA, US

Adobe Inc. - San Jose CA

H04N 21/466
H04N 21/4722
H04N 21/45
G06N 3/08

A multimodal recommendation identification system analyzes data describing a sequence of past content item interactions to generate a recommendation for a content item for a user. An indication of the recommended content item is provided to a website hosting system or recommendation system so that the recommended content item is displayed or otherwise presented to the user. The multimodal recommendation identification system identifies a content item to recommend to the user by generating an encoding that encodes identifiers of the sequence of content items the user has interacted with and generating encodings that encode multimodal information for content items in the sequence of content items the user has interacted with. An aggregated information encoding for a user based on these encodings and a system analyzes the content item sequence encoding and interaction between the content item sequence encoding and the multiple modality encodings to generate the aggregated information encoding.

20220237682, Jul 28, 2022

Jan 27, 2021

17/159554

- SAN JOSE CA, US
Zhankui He - San Diego CA, US
Zhaowen Wang - San Jose CA, US
Zhe Lin - Bellevue WA, US
Ajinkya Kale - San Jose CA, US
Fengbin Chen - San Jose CA, US

G06Q 30/06
G06N 3/04
G06N 3/08
G06K 9/62

Systems and methods for item recommendation are described. Embodiments identify a sequence of items selected by a user, embed each item of the sequence of items to produce item embeddings having a reduced number of dimensions, predict a next item based on the item embeddings using a recommendation network, wherein the recommendation network includes a sequential encoder trained based at least in part on a sampled softmax classifier, and wherein predicting the next item represents a prediction that the user will interact with the next item, and provide a recommendation to the user, wherein the recommendation includes the next item.

20230110114, Apr 13, 2023

Oct 12, 2021

17/499611

- San Jose CA, US
Zhifei Zhang - San Jose CA, US
Matthew Fisher - San Francisco CA, US
Hailin Jin - San Jose CA, US
Zhaowen Wang - San Jose CA, US
Niloy J Mitra - London, GB

G06T 11/20
G06T 3/40

The present disclosure relates to systems, methods, and non-transitory computer-readable media for accurately and flexibly generating scalable fonts utilizing multi-implicit neural font representations. For instance, the disclosed systems combine deep learning with differentiable rasterization to generate a multi-implicit neural font representation of a glyph. For example, the disclosed systems utilize an implicit differentiable font neural network to determine a font style code for an input glyph as well as distance values for locations of the glyph to be rendered based on a glyph label and the font style code. Further, the disclosed systems rasterize the distance values utilizing a differentiable rasterization model and combines the rasterized distance values to generate a permutation-invariant version of the glyph corresponding glyph set.

20230070666, Mar 9, 2023

Sep 3, 2021

17/466711

- San Jose CA, US
- Prague, CZ
David FUTSCHIK - Liberec, CZ
Zhaowen WANG - San Jose CA, US
Elya SHECHTMAN - Seattle WA, US

G06T 5/50
G06K 9/62

Embodiments are disclosed for translating an image from a source visual domain to a target visual domain. In particular, in one or more embodiments, the disclosed systems and methods comprise a training process that includes receiving a training input including a pair of keyframes and an unpaired image. The pair of keyframes represent a visual translation from a first version of an image in a source visual domain to a second version of the image in a target visual domain. The one or more embodiments further include sending the pair of keyframes and the unpaired image to an image translation network to generate a first training image and a second training image. The one or more embodiments further include training the image translation network to translate images from the source visual domain to the target visual domain based on a calculated loss using the first and second training images.

20210342974, Nov 4, 2021

Apr 29, 2020

16/861688

- San Jose CA, US
Zhifei Zhang - San Jose CA, US
Jose Ignacio Echevarria Vallespi - San Francisco CA, US
Zhaowen Wang - San Jose CA, US
Stephen Diverdi - Berkeley CA, US

G06T 3/40
G06N 20/00

Systems and methods for texture hallucination with a large upscaling factor are described. Embodiments of the systems and methods may receive an input image and a reference image, extract an upscaled feature map from the input image, match the input image to a portion of the reference image, wherein a resolution of the reference image is higher than a resolution of the input image, concatenate the upscaled feature map with a reference feature map corresponding to the portion of the reference image to produce a concatenated feature map, and generate a reconstructed image based on the concatenated feature map using a machine learning model trained with a texture loss and a degradation loss, wherein the texture loss is based on a high frequency band filter, and the degradation loss is based on a downscaled version of the reconstructed image.