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    Multi-modal differential search with real-time focus adaptation
    251.
    发明授权
    Multi-modal differential search with real-time focus adaptation 有权

    公开(公告)号：US11604822B2

    公开(公告)日：2023-03-14

    申请号：US16426369

    申请日：2019-05-30

    Applicant: Adobe Inc.

    Inventor： Pranav Vineet Aggarwal ,  Zhe Lin ,  Baldo Antonio Faieta ,  Saeid Motiian

    IPC: G06F7/00 ,  G06F16/583 ,  G06N3/084 ,  G06N20/00 ,  G06F16/538 ,  G06F16/532 ,  G06F16/33 ,  G06F3/04855

    Abstract: Multi-modal differential search with real-time focus adaptation techniques are described that overcome the challenges of conventional techniques in a variety of ways. In one example, a model is trained to support a visually guided machine-learning embedding space that supports visual intuition as to “what” is represented by text. The visually guided language embedding space supported by the model, once trained, may then be used to support visual intuition as part of a variety of functionality. In one such example, the visually guided language embedding space as implemented by the model may be leveraged as part of a multi-modal differential search to support search of digital images and other digital content with real-time focus adaptation which overcomes the challenges of conventional techniques.

    Resource-aware training for neural networks
    252.
    发明授权
    Resource-aware training for neural networks 有权

    公开(公告)号：US11551093B2

    公开(公告)日：2023-01-10

    申请号：US16254406

    申请日：2019-01-22

    Applicant: Adobe Inc.

    Inventor： Zhe Lin ,  Siyuan Qiao ,  Jianming Zhang

    IPC: G06K9/00 ,  G06N3/08 ,  G06N3/04 ,  G06K9/62

    Abstract: In implementations of resource-aware training for neural network, one or more computing devices of a system implement an architecture optimization module for monitoring parameter utilization while training a neural network. Dead neurons of the neural network are identified as having activation scales less than a threshold. Neurons with activation scales greater than or equal to the threshold are identified as survived neurons. The dead neurons are converted to reborn neurons by adding the dead neurons to layers of the neural network having the survived neurons. The reborn neurons are prevented from connecting to the survived neurons for training the reborn neurons.

    UTILIZING DEEP NEURAL NETWORKS TO AUTOMATICALLY SELECT INSTANCES OF DETECTED OBJECTS IN IMAGES
    254.
    发明申请
    UTILIZING DEEP NEURAL NETWORKS TO AUTOMATICALLY SELECT INSTANCES OF DETECTED OBJECTS IN IMAGES 有权

    公开(公告)号：US20220392046A1

    公开(公告)日：2022-12-08

    申请号：US17819845

    申请日：2022-08-15

    Applicant: Adobe Inc.

    Inventor： Scott Cohen ,  Zhe Lin ,  Mingyang Ling

    IPC: G06T7/00 ,  G06T7/70 ,  G06T7/90 ,  G06T11/60 ,  G06F16/583 ,  G06F40/205

    Abstract: The present disclosure relates to an object selection system that accurately detects and automatically selects target instances of user-requested objects (e.g., a query object instance) in a digital image. In one or more embodiments, the object selection system can analyze one or more user inputs to determine an optimal object attribute detection model from multiple specialized and generalized object attribute models. Additionally, the object selection system can utilize the selected object attribute model to detect and select one or more target instances of a query object in an image, where the image includes multiple instances of the query object.

    AUTOMATICALLY GENERATING AN IMAGE DATASET BASED ON OBJECT INSTANCE SIMILARITY
    255.
    发明申请
    AUTOMATICALLY GENERATING AN IMAGE DATASET BASED ON OBJECT INSTANCE SIMILARITY 有权

    公开(公告)号：US20220391633A1

    公开(公告)日：2022-12-08

    申请号：US17337194

    申请日：2021-06-02

    Applicant: Adobe Inc.

    Inventor： Midhun Harikumar ,  Zhe Lin ,  Shabnam Ghadar ,  Baldo Faieta

    IPC: G06K9/62 ,  G06N3/08

    Abstract: Methods, systems, and non-transitory computer readable media are disclosed for accurately and efficiently generating groups of images portraying semantically similar objects for utilization in building machine learning models. In particular, the disclosed system utilizes metadata and spatial statistics to extract semantically similar objects from a repository of digital images. In some embodiments, the disclosed system generates color embeddings and content embeddings for the identified objects. The disclosed system can further group similar objects together within a query space by utilizing a clustering algorithm to create object clusters and then refining and combining the object clusters within the query space. In some embodiments, the disclosed system utilizes one or more of the object clusters to build a machine learning model.

    Labeling techniques for a modified panoptic labeling neural network
    256.
    发明授权
    Labeling techniques for a modified panoptic labeling neural network 有权

    公开(公告)号：US11507777B2

    公开(公告)日：2022-11-22

    申请号：US15930539

    申请日：2020-05-13

    Applicant: Adobe Inc.

    Inventor： Sohrab Amirghodsi ,  Zhe Lin ,  Yilin Wang ,  Tianshu Yu ,  Connelly Barnes ,  Elya Shechtman

    IPC: G06K9/62 ,  G06V10/75 ,  G06F17/18 ,  G06N3/08 ,  G06N20/00

    Abstract: A panoptic labeling system includes a modified panoptic labeling neural network (“modified PLNN”) that is trained to generate labels for pixels in an input image. The panoptic labeling system generates modified training images by combining training images with mask instances from annotated images. The modified PLNN determines a set of labels representing categories of objects depicted in the modified training images. The modified PLNN also determines a subset of the labels representing categories of objects depicted in the input image. For each mask pixel in a modified training image, the modified PLNN calculates a probability indicating whether the mask pixel has the same label as an object pixel. The modified PLNN generates a mask label for each mask pixel, based on the probability. The panoptic labeling system provides the mask label to, for example, a digital graphics editing system that uses the labels to complete an infill operation.

    Distractor classifier
    258.
    发明授权
    Distractor classifier 有权

    公开(公告)号：US11462040B2

    公开(公告)日：2022-10-04

    申请号：US17082479

    申请日：2020-10-28

    Applicant: ADOBE INC.

    Inventor： Zhe Lin ,  Luis Figueroa ,  Zhihong Ding ,  Scott Cohen

    IPC: G06V40/10 ,  G06N3/08 ,  G06K9/62 ,  G06T3/40 ,  G06V10/50

    Abstract: A distractor detector includes a heatmap network and a distractor classifier. The heatmap network operates on an input image to generate a heatmap for a main subject, a heatmap for a distractor, and optionally a heatmap for the background. Each object is cropped within the input image to generate a corresponding cropped image. Regions within the heatmaps that correspond to the objects are identified, and each of the regions is cropped within each of the heatmaps to generate cropped heatmaps. The distractor classifier then operates on the cropped images and the cropped heatmaps to classify each of the objects as being either a main subject or a distractor.

    GENERATING REFINED SEGMENTATIONS MASKS VIA METICULOUS OBJECT SEGMENTATION
    259.
    发明申请
    GENERATING REFINED SEGMENTATIONS MASKS VIA METICULOUS OBJECT SEGMENTATION 有权

    公开(公告)号：US20220292684A1

    公开(公告)日：2022-09-15

    申请号：US17200525

    申请日：2021-03-12

    Applicant: Adobe Inc.

    Inventor： Yilin Wang ,  Chenglin Yang ,  Jianming Zhang ,  He Zhang ,  Zhe Lin

    IPC: G06T7/11 ,  G06K9/62 ,  G06T3/40 ,  G06N3/04 ,  G06N3/08

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilizes a neural network having a hierarchy of hierarchical point-wise refining blocks to generate refined segmentation masks for high-resolution digital visual media items. For example, in one or more embodiments, the disclosed systems utilize a segmentation refinement neural network having an encoder and a recursive decoder to generate the refined segmentation masks. The recursive decoder includes a deconvolution branch for generating feature maps and a refinement branch for generating and refining segmentation masks. In particular, in some cases, the refinement branch includes a hierarchy of hierarchical point-wise refining blocks that recursively refine a segmentation mask generated for a digital visual media item. In some cases, the disclosed systems utilize a segmentation refinement neural network that includes a low-resolution network and a high-resolution network, each including an encoder and a recursive decoder, to generate the refined segmentation masks.

    Finding similar persons in images
    260.
    发明授权
    Finding similar persons in images 有权

    公开(公告)号：US11436865B1

    公开(公告)日：2022-09-06

    申请号：US17207178

    申请日：2021-03-19

    Applicant: Adobe Inc.

    Inventor： Saeid Motiian ,  Zhe Lin ,  Shabnam Ghadar ,  Baldo Faieta

    IPC: G06V40/16 ,  G06K9/62 ,  G06V30/194 ,  G06V40/10

    Abstract: Embodiments are disclosed for finding similar persons in images. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an image query, the image query including an input image that includes a representation of a person, generating a first cropped image including a representation of the person's face and a second cropped image including a representation of the person's body, generating an image embedding for the input image by combining a face embedding corresponding to the first cropped image and a body embedding corresponding to the second cropped image, and querying an image repository in embedding space by comparing the image embedding to a plurality of image embeddings associated with a plurality of images in the image repository to obtain one or more images based on similarity to the input image in the embedding space.

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