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公开(公告)号：US20210241111A1
公开(公告)日：2021-08-05
申请号：US16782793
申请日：2020-02-05
Applicant: Adobe Inc.
Inventor： Shikun Liu , Zhe Lin , Yilin Wang , Jianming Zhang , Federico Perazzi
Abstract: The present disclosure relates to shaping the architecture of a neural network. For example, the disclosed systems can provide a neural network shaping mechanism for at least one sampling layer of a neural network. The neural network shaping mechanism can include a learnable scaling factor between a sampling rate of the at least one sampling layer and an additional sampling function. The disclosed systems can learn the scaling factor based on a dataset while jointly learning the network weights of the neural network. Based on the learned scaling factor, the disclosed systems can shape the architecture of the neural network by modifying the sampling rate of the at least one sampling layer.
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公开(公告)号：US20210232927A1
公开(公告)日：2021-07-29
申请号：US16751897
申请日：2020-01-24
Applicant: Adobe Inc.
Inventor： Zhe Lin , Yilin Wang , Siyuan Qiao , Jianming Zhang
Abstract: In some embodiments, an application receives a request to execute a convolutional neural network model. The application determines the computational complexity requirement for the neural network based on the computing resource available on the device. The application further determines the architecture of the convolutional neural network model by determining the locations of down-sampling layers within the convolutional neural network model based on the computational complexity requirement. The application reconfigures the architecture of the convolutional neural network model by moving the down-sampling layers to the determined locations and executes the convolutional neural network model to generate output results.
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公开(公告)号：US10997692B2
公开(公告)日：2021-05-04
申请号：US16548232
申请日：2019-08-22
Applicant: Adobe Inc.
Inventor： Jianming Zhang
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.
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公开(公告)号：US10970599B2
公开(公告)日：2021-04-06
申请号：US16191724
申请日：2018-11-15
Applicant: ADOBE INC.
Inventor： Mingyang Ling , Alex Filipkowski , Zhe Lin , Jianming Zhang , Samarth Gulati
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.
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公开(公告)号：US20210073644A1
公开(公告)日：2021-03-11
申请号：US16563226
申请日：2019-09-06
Applicant: Adobe Inc.
Inventor： Zhe Lin , Yilin Wang , Siyuan Qiao , Jianming Zhang
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.
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Patent Agency Ranking
公开(公告)号：US10755099B2
公开(公告)日：2020-08-25
申请号：US16189805
申请日：2018-11-13
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xiaohui Shen , Mingyang Ling , Jianming Zhang , Jason Wen Yong Kuen
Abstract: In implementations of object detection in images, object detectors are trained using heterogeneous training datasets. A first training dataset is used to train an image tagging network to determine an attention map of an input image for a target concept. A second training dataset is used to train a conditional detection network that accepts as conditional inputs the attention map and a word embedding of the target concept. Despite the conditional detection network being trained with a training dataset having a small number of seen classes (e.g., classes in a training dataset), it generalizes to novel, unseen classes by concept conditioning, since the target concept propagates through the conditional detection network via the conditional inputs, thus influencing classification and region proposal. Hence, classes of objects that can be detected are expanded, without the need to scale training databases to include additional classes.
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公开(公告)号：US10460214B2
公开(公告)日：2019-10-29
申请号：US15799395
申请日：2017-10-31
Applicant: Adobe Inc.
Inventor： Xin Lu , Zhe Lin , Xiaohui Shen , Jimei Yang , Jianming Zhang , Jen-Chan Jeff Chien , Chenxi Liu
Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for segmenting objects in digital visual media utilizing one or more salient content neural networks. In particular, in one or more embodiments, the disclosed systems and methods train one or more salient content neural networks to efficiently identify foreground pixels in digital visual media. Moreover, in one or more embodiments, the disclosed systems and methods provide a trained salient content neural network to a mobile device, allowing the mobile device to directly select salient objects in digital visual media utilizing a trained neural network. Furthermore, in one or more embodiments, the disclosed systems and methods train and provide multiple salient content neural networks, such that mobile devices can identify objects in real-time digital visual media feeds (utilizing a first salient content neural network) and identify objects in static digital images (utilizing a second salient content neural network).
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公开(公告)号：US12236640B2
公开(公告)日：2025-02-25
申请号：US17656796
申请日：2022-03-28
Applicant: ADOBE INC.
Inventor： Jianming Zhang , Linyi Jin , Kevin Matzen , Oliver Wang , Yannick Hold-Geoffroy
Abstract: Systems and methods for image dense field based view calibration are provided. In one embodiment, an input image is applied to a dense field machine learning model that generates a vertical vector dense field (VVF) and a latitude dense field (LDF) from the input image. The VVF comprises a vertical vector of a projected vanishing point direction for each of the pixels of the input image. The latitude dense field (LDF) comprises a projected latitude value for the pixels of the input image. A dense field map for the input image comprising the VVF and the LDF can be directly or indirectly used for a variety of image processing manipulations. The VVF and LDF can be optionally used to derive traditional camera calibration parameters from uncontrolled images that have undergone undocumented or unknown manipulations.
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公开(公告)号：US12223661B2
公开(公告)日：2025-02-11
申请号：US17735728
申请日：2022-05-03
Applicant: ADOBE INC.
Inventor： Lu Zhang , Jianming Zhang , Zhe Lin , Radomir Mech
Abstract: Systems and methods provide editing operations in a smart editing system that may generate a focal point within a mask of an object for each frame of a video segment and perform editing effects on the frames of the video segment to quickly provide users with natural video editing effects. An eye-gaze network may produce a hotspot map of predicted focal points in a video frame. These predicted focal points may then be used by a gaze-to-mask network to determine objects in the image and generate an object mask for each of the detected objects. This process may then be repeated to effectively track the trajectory of objects and object focal points in videos. Based on the determined trajectory of an object in a video clip and editing parameters, the editing engine may produce editing effects relative to an object for the video clip.
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公开(公告)号：US12223439B2
公开(公告)日：2025-02-11
申请号：US17190668
申请日：2021-03-03
Applicant: ADOBE INC.
Inventor： Xin Yuan , Zhe Lin , Jason Wen Yong Kuen , Jianming Zhang , Yilin Wang , Ajinkya Kale , Baldo Faieta
Abstract: Systems and methods for multi-modal representation learning are described. One or more embodiments provide a visual representation learning system trained using machine learning techniques. For example, some embodiments of the visual representation learning system are trained using cross-modal training tasks including a combination of intra-modal and inter-modal similarity preservation objectives. In some examples, the training tasks are based on contrastive learning techniques.
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