Patents by Inventor Jianming Zhang

Jianming Zhang has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20210155603
    Abstract: Provided are compounds useful for treating of cancer and methods for treating of cancer, comprising administering to a subject in need thereof a compound described therein.
    Type: Application
    Filed: June 29, 2020
    Publication date: May 27, 2021
    Inventors: Samuel V. Agresta, Chong-Hui Gu, David Schenkein, Hua Yang, Liting Guo, Zhen Tang, Jianming Wang, Yanfeng Zhang, Yan Zhou
  • Patent number: 11016010
    Abstract: A triaxial rock mechanics test system for high-strain-rate cyclic dynamic loading is provided, including: a host loading module, a dynamic cyclic loading module, and a dynamic measurement module. The host loading module includes a loading frame, a triaxial pressure mechanism and an actuator that are provided inside the loading frame. The dynamic cyclic loading module includes a driving mechanism, a fixed mechanism and a mobile mechanism. The dynamic measurement module is configured to acquire axial pressure data, radial deformation data and axial deformation data of the rock sample during a test. In the triaxial rock mechanics test system for high-strain-rate cyclic dynamic loading of the present invention, an energetic rod is driven by an electrical explosion of a metal wire to generate a strong shock wave with controllable peak pressure, duration and waveform.
    Type: Grant
    Filed: February 19, 2021
    Date of Patent: May 25, 2021
    Assignee: INSTITUTE OF GEOLOGY AND GEOPHYSICS, CHINESE ACADEMY OF SCIENCES
    Inventors: Guanfang Li, Xiao Li, Shouding Li, Jianming He, Zhaobin Zhang, Tianqiao Mao, Bo Zheng, Yanfang Wu
  • Patent number: 10997692
    Abstract: A crop generation system determines multiple types of saliency data and multiple crop candidates for an image. Multiple region of interest (“ROI”) ensembles are generated, indicating locations of the salient content of the image. For each crop candidate, the crop generation system calculates an evaluation score. A set of crop candidates is selected based on the evaluation scores.
    Type: Grant
    Filed: August 22, 2019
    Date of Patent: May 4, 2021
    Assignee: Adobe Inc.
    Inventor: Jianming Zhang
  • Patent number: 10997464
    Abstract: Digital image layout training is described using wireframe rendering within a generative adversarial network (GAN) system. A GAN system is employed to train the generator module to refine digital image layouts. To do so, a wireframe rendering discriminator module rasterizes a refined digital training digital image layout received from a generator module into a wireframe digital image layout. The wireframe digital image layout is then compared with at least one ground truth digital image layout using a loss function as part of machine learning by the wireframe discriminator module. The generator module is then trained by backpropagating a result of the comparison.
    Type: Grant
    Filed: November 9, 2018
    Date of Patent: May 4, 2021
    Assignee: Adobe Inc.
    Inventors: Jimei Yang, Jianming Zhang, Aaron Phillip Hertzmann, Jianan Li
  • Publication number: 20210110589
    Abstract: Embodiments of the present invention are directed to facilitating region of interest preservation. In accordance with some embodiments of the present invention, a region of interest preservation score using adaptive margins is determined. The region of interest preservation score indicates an extent to which at least one region of interest is preserved in a candidate image crop associated with an image. A region of interest positioning score is determined that indicates an extent to which a position of the at least one region of interest is preserved in the candidate image crop associated with the image. The region of interest preservation score and/or the preserving score are used to select a set of one or more candidate image crops as image crop suggestions.
    Type: Application
    Filed: October 29, 2020
    Publication date: April 15, 2021
    Inventors: Jianming Zhang, Zhe Lin, Radomir Mech, Xiaohui Shen
  • Patent number: 10970599
    Abstract: Techniques are disclosed for characterizing and defining the location of a copy space in an image. A methodology implementing the techniques according to an embodiment includes applying a regression convolutional neural network (CNN) to an image. The regression CNN is configured to predict properties of the copy space such as size and type (natural or manufactured). The prediction is conditioned on a determination of the presence of the copy space in the image. The method further includes applying a segmentation CNN to the image. The segmentation CNN is configured to generate one or more pixel-level masks to define the location of copy spaces in the image, whether natural or manufactured, or to define the location of a background region of the image. The segmentation CNN may include a first stage comprising convolutional layers and a second stage comprising pairs of boundary refinement layers and bilinear up-sampling layers.
    Type: Grant
    Filed: November 15, 2018
    Date of Patent: April 6, 2021
    Assignee: ADOBE INC.
    Inventors: Mingyang Ling, Alex Filipkowski, Zhe Lin, Jianming Zhang, Samarth Gulati
  • Publication number: 20210082118
    Abstract: Enhanced methods and systems for the semantic segmentation of images are described. A refined segmentation mask for a specified object visually depicted in a source image is generated based on a coarse and/or raw segmentation mask. The refined segmentation mask is generated via a refinement process applied to the coarse segmentation mask. The refinement process correct at least a portion of both type I and type II errors, as well as refine boundaries of the specified object, associated with the coarse segmentation mask. Thus, the refined segmentation mask provides a more accurate segmentation of the object than the coarse segmentation mask. A segmentation refinement model is employed to generate the refined segmentation mask based on the coarse segmentation mask. That is, the segmentation model is employed to refine the coarse segmentation mask to generate more accurate segmentations of the object. The refinement process is an iterative refinement process carried out via a trained neural network.
    Type: Application
    Filed: September 18, 2019
    Publication date: March 18, 2021
    Inventors: Jianming ZHANG, Zhe LIN
  • Publication number: 20210073644
    Abstract: A machine learning model compression system and related techniques are described herein. The machine learning model compression system can intelligently remove certain parameters of a machine learning model, without introducing a loss in performance of the machine learning model. Various parameters of a machine learning model can be removed during compression of the machine learning model, such as one or more channels of a single-branch or multi-branch neural network, one or more branches of a multi-branch neural network, certain weights of a channel of a single-branch or multi-branch neural network, and/or other parameters. In some cases, compression is performed only on certain selected layers or branches of the machine learning model. Candidate filters from the selected layers or branches can be removed from the machine learning model in a way that preserves local features of the machine learning model.
    Type: Application
    Filed: September 6, 2019
    Publication date: March 11, 2021
    Inventors: Zhe Lin, Yilin Wang, Siyuan Qiao, Jianming Zhang
  • Publication number: 20210065332
    Abstract: Systems and methods are described for dynamically fitting a digital image based on the saliency of the image and the aspect ratio of a frame are described. The systems and methods may provide for identifying an aspect ratio of the frame, selecting a salient region of the digital image based on the aspect ratio using a saliency prediction model, and fitting the digital image into the frame so that a boundary of the frame is aligned with a boundary of the salient region.
    Type: Application
    Filed: August 30, 2019
    Publication date: March 4, 2021
    Inventors: Amish Kumar Bedi, Sanyam Jain, Jianming Zhang
  • Publication number: 20210056663
    Abstract: A crop generation system determines multiple types of saliency data and multiple crop candidates for an image. Multiple region of interest (“ROI”) ensembles are generated, indicating locations of the salient content of the image. For each crop candidate, the crop generation system calculates an evaluation score. A set of crop candidates is selected based on the evaluation scores.
    Type: Application
    Filed: August 22, 2019
    Publication date: February 25, 2021
    Inventor: Jianming Zhang
  • Patent number: 10915798
    Abstract: Disclosed herein are embodiments of systems, methods, and products for a webly supervised training of a convolutional neural network (CNN) to predict emotion in images. A computer may query one or more image repositories using search keywords generated based on the tertiary emotion classes of Parrott's emotion wheel. The computer may filter images received in response to the query to generate a weakly labeled training dataset labels associated with the images that are noisy or wrong may be cleaned prior to training of the CNN. The computer may iteratively train the CNN leveraging the hierarchy of emotion classes by increasing the complexity of the labels (tags) for each iteration. Such curriculum guided training may generate a trained CNN that is more accurate than the conventionally trained neural networks.
    Type: Grant
    Filed: May 15, 2018
    Date of Patent: February 9, 2021
    Assignee: Adobe Inc.
    Inventors: Jianming Zhang, Rameswar Panda, Haoxiang Li, Joon-Young Lee, Xin Lu
  • Publication number: 20210027470
    Abstract: The present disclosure relates to utilizing a neural network having a two-stream encoder architecture to accurately generate composite digital images that realistically portray a foreground object from one digital image against a scene from another digital image. For example, the disclosed systems can utilize a foreground encoder of the neural network to identify features from a foreground image and further utilize a background encoder to identify features from a background image. The disclosed systems can then utilize a decoder to fuse the features together and generate a composite digital image. The disclosed systems can train the neural network utilizing an easy-to-hard data augmentation scheme implemented via self-teaching. The disclosed systems can further incorporate the neural network within an end-to-end framework for automation of the image composition process.
    Type: Application
    Filed: July 26, 2019
    Publication date: January 28, 2021
    Inventors: Zhe Lin, Jianming Zhang, He Zhang, Federico Perazzi
  • Patent number: 10867422
    Abstract: Embodiments of the present invention are directed to facilitating region of interest preservation. In accordance with some embodiments of the present invention, a region of interest preservation score using adaptive margins is determined. The region of interest preservation score indicates an extent to which at least one region of interest is preserved in a candidate image crop associated with an image. A region of interest positioning score is determined that indicates an extent to which a position of the at least one region of interest is preserved in the candidate image crop associated with the image. The region of interest preservation score and/or the preserving score are used to select a set of one or more candidate image crops as image crop suggestions.
    Type: Grant
    Filed: June 12, 2017
    Date of Patent: December 15, 2020
    Assignee: ADOBE Inc.
    Inventors: Jianming Zhang, Zhe Lin, Radomir Mech, Xiaohui Shen
  • Publication number: 20200381401
    Abstract: A semiconductor device is disclosed including a die stack including a number of dies aligned with each other with respect to an axis, and a top die that is offset along the axis the to prevent die cracking.
    Type: Application
    Filed: March 10, 2020
    Publication date: December 3, 2020
    Applicant: WESTERN DIGITAL TECHNOLOGIES, INC.
    Inventors: Junrong Yan, Jianming Zhang, Min Zhao, Kailei Zhang, Chee Keong Chin, Kim Lee Bock
  • Patent number: 10846870
    Abstract: Joint training technique for depth map generation implemented by depth prediction system as part of a computing device is described. The depth prediction system is configured to generate a candidate feature map from features extracted from training digital images, generate a candidate segmentation map and a candidate depth map from the generated candidate feature map, and jointly train portions of the depth prediction system using a loss function. Consequently, depth prediction system is able to generate a depth map that identifies depths of objects using ordinal depth information and accurately delineates object boundaries within a single digital image.
    Type: Grant
    Filed: November 29, 2018
    Date of Patent: November 24, 2020
    Assignee: Adobe Inc.
    Inventors: Jianming Zhang, Zhe Lin, Xiaohui Shen, Oliver Wang, Lijun Wang
  • Publication number: 20200364914
    Abstract: Various methods and systems are provided for image-management operations that includes generating adaptive image armatures based on an alignment between composition lines of a reference armature and a position of an object in an image. In operation, a reference armature for an image is accessed. The reference armature includes a plurality of composition lines that define a frame of reference for image composition. An alignment map is determined using the reference armature. The alignment map includes alignment information that indicates alignment between the composition lines of the reference armature and the position of the object in the image. Based on the alignment map, an adaptive image armature is determined. The adaptive image armature includes a subset of the composition lines of the reference armature. The adaptive image armature is displayed.
    Type: Application
    Filed: May 17, 2019
    Publication date: November 19, 2020
    Inventors: Radomir Mech, Jose Ignacio Echevarria Vallespi, Jingwan Lu, Jianming Zhang, Jane Little E
  • Publication number: 20200347068
    Abstract: The present disclosure provides novel heteroaryl compounds of formula (IV). Such compounds are useful for the treatment of cancers.
    Type: Application
    Filed: July 20, 2020
    Publication date: November 5, 2020
    Applicants: Dana-Farber Cancer Institute, Inc., The Scripps Research Institute
    Inventors: Nathanael S. GRAY, Jianming ZHANG, Barun OKRAM, Xianming DENG, Jae Won CHANG, Amy WOJCIECHOWSKI
  • Publication number: 20200334501
    Abstract: Systems and methods are described for object detection within a digital image using a hierarchical softmax function. The method may include applying a first softmax function of a softmax hierarchy on a digital image based on a first set of object classes that are children of a root node of a class hierarchy, then apply a second (and subsequent) softmax functions to the digital image based on a second (and subsequent) set of object classes, where the second (and subsequent) object classes are children nodes of an object class from the first (or parent) object classes. The methods may then include generating an object recognition output using a convolutional neural network (CNN) based at least in part on applying the first and second (and subsequent) softmax functions. In some cases, the hierarchical softmax function is the loss function for the CNN.
    Type: Application
    Filed: April 18, 2019
    Publication date: October 22, 2020
    Inventors: ZHE LIN, MINGYANG LING, JIANMING ZHANG, JASON KUEN, FEDERICO PERAZZI, BRETT BUTTERFIELD, BALDO FAIETA
  • Patent number: 10810707
    Abstract: Techniques of generating depth-of-field blur effects on digital images by digital effect generation system of a computing device are described. The digital effect generation system is configured to generate depth-of-field blur effects on objects based on focal depth value that defines a depth plane in the digital image and a aperture value that defines an intensity of blur effect applied to the digital image. The digital effect generation system is also configured to improve the accuracy with which depth-of-field blur effects are generated by performing up-sampling operations and implementing a unique focal loss algorithm that minimizes the focal loss within digital images effectively.
    Type: Grant
    Filed: November 29, 2018
    Date of Patent: October 20, 2020
    Assignee: Adobe Inc.
    Inventors: Jianming Zhang, Zhe Lin, Xiaohui Shen, Oliver Wang, Lijun Wang
  • Patent number: D911092
    Type: Grant
    Filed: January 25, 2019
    Date of Patent: February 23, 2021
    Inventor: Jianming Zhang