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    Training of neural network based natural language processing models using dense knowledge distillation
    2.
    发明授权
    Training of neural network based natural language processing models using dense knowledge distillation 有权

    公开(公告)号：US11651211B2

    公开(公告)日：2023-05-16

    申请号：US16717698

    申请日：2019-12-17

    Applicant: Adobe Inc.

    Inventor： Tuan Manh Lai ,  Trung Huu Bui ,  Quan Hung Tran

    IPC: G06N3/00 ,  G06N3/08 ,  G06F40/284 ,  G06N3/045 ,  G10L15/16 ,  G10L25/30

    CPC classification number: G06N3/08 ,  G06F40/284 ,  G06N3/045 ,  G10L15/16 ,  G10L25/30

    Abstract: Techniques for training a first neural network (NN) model using a pre-trained second NN model are disclosed. In an example, training data is input to the first and second models. The training data includes masked tokens and unmasked tokens. In response, the first model generates a first prediction associated with a masked token and a second prediction associated with an unmasked token, and the second model generates a third prediction associated with the masked token and a fourth prediction associated with the unmasked token. The first model is trained, based at least in part on the first, second, third, and fourth predictions. In another example, a prediction associated with a masked token, a prediction associated with an unmasked token, and a prediction associated with whether two sentences of training data are adjacent sentences are received from each of the first and second models. The first model is trained using the predictions.

    DECOMPOSITIONAL LEARNING FOR COLOR ATTRIBUTE PREDICTION
    3.
    发明申请
    DECOMPOSITIONAL LEARNING FOR COLOR ATTRIBUTE PREDICTION 有权

    公开(公告)号：US20220383031A1

    公开(公告)日：2022-12-01

    申请号：US17333583

    申请日：2021-05-28

    Applicant: Adobe INC.

    Inventor： Qiuyu Chen ,  Quan Hung Tran ,  Kushal Kafle ,  Trung Huu Bui ,  Franck Dernoncourt ,  Walter Chang

    IPC: G06K9/46 ,  G06K9/32 ,  G06F16/51 ,  G06F16/583 ,  G06F16/532 ,  G06F16/56

    Abstract: The present disclosure describes a model for large scale color prediction of objects identified in images. Embodiments of the present disclosure include an object detection network, an attention network, and a color classification network. The object detection network generates object features for an object in an image and may include a convolutional neural network (CNN), region proposal network, or a ResNet. The attention network generates an attention vector for the object based on the object features, wherein the attention network takes a query vector based on the object features, and a plurality of key vector and a plurality of value vectors corresponding to a plurality of colors as input. The color classification network generates a color attribute vector based on the attention vector, wherein the color attribute vector indicates a probability of the object including each of the plurality of colors.

    Semantic image manipulation using visual-semantic joint embeddings
    6.
    发明授权
    Semantic image manipulation using visual-semantic joint embeddings 有权

    公开(公告)号：US11574142B2

    公开(公告)日：2023-02-07

    申请号：US16943511

    申请日：2020-07-30

    Applicant: Adobe Inc.

    Inventor： Zhe Lin ,  Xihui Liu ,  Quan Hung Tran ,  Jianming Zhang ,  Handong Zhao

    IPC: G06K9/62 ,  G06T11/00 ,  G06V10/44

    Abstract: The technology described herein is directed to a reinforcement learning based framework for training a natural media agent to learn a rendering policy without human supervision or labeled datasets. The reinforcement learning based framework feeds the natural media agent a training dataset to implicitly learn the rendering policy by exploring a canvas and minimizing a loss function. Once trained, the natural media agent can be applied to any reference image to generate a series (or sequence) of continuous-valued primitive graphic actions, e.g., sequence of painting strokes, that when rendered by a synthetic rendering environment on a canvas, reproduce an identical or transformed version of the reference image subject to limitations of an action space and the learned rendering policy.

    Intent detection
    7.
    发明授权
    Intent detection 有权

    公开(公告)号：US12182524B2

    公开(公告)日：2024-12-31

    申请号：US17453562

    申请日：2021-11-04

    Applicant: ADOBE INC.

    Inventor： Jianguo Zhang ,  Trung Huu Bui ,  Seunghyun Yoon ,  Xiang Chen ,  Quan Hung Tran ,  Walter W. Chang

    IPC: G06F40/40 ,  G06F40/284 ,  G06F40/30 ,  G06V30/19

    Abstract: Systems and methods for natural language processing are described. One or more aspects of a method, apparatus, and non-transitory computer readable medium include receiving a text phrase; encoding the text phrase using an encoder to obtain a hidden representation of the text phrase, wherein the encoder is trained during a first training phrase using self-supervised learning based on a first contrastive loss and during a second training phrase using supervised learning based on a second contrastive learning loss; identifying an intent of the text phrase from a predetermined set of intent labels using a classification network, wherein the classification network is jointly trained with the encoder in the second training phase; and generating a response to the text phrase based on the intent.

    LONG-TAIL COLOR PREDICTION
    8.
    发明公开
    LONG-TAIL COLOR PREDICTION 审中-公开

    公开(公告)号：US20240037906A1

    公开(公告)日：2024-02-01

    申请号：US17814921

    申请日：2022-07-26

    Applicant: ADOBE INC.

    Inventor： Qiuyu Chen ,  Quan Hung Tran ,  Kushal Kafle ,  Trung Huu Bui ,  Franck Dernoncourt ,  Walter W. Chang

    IPC: G06V10/764 ,  G06V10/56 ,  G06V10/774

    CPC classification number: G06V10/764 ,  G06V10/56 ,  G06V10/774 ,  G06V2201/10

    Abstract: Systems and methods for color prediction are described. Embodiments of the present disclosure receive an image that includes an object including a color, generate a color vector based on the image using a color classification network, where the color vector includes a color value corresponding to each of a set of colors, generate a bias vector by comparing the color vector to teach of a set of center vectors, where each of the set of center vectors corresponds to a color of the set of colors, and generate an unbiased color vector based on the color vector and the bias vector, where the unbiased color vector indicates the color of the object.

    CONTRASTIVE CAPTIONING FOR IMAGE GROUPS
    10.
    发明申请
    CONTRASTIVE CAPTIONING FOR IMAGE GROUPS 有权

    公开(公告)号：US20220058390A1

    公开(公告)日：2022-02-24

    申请号：US16998876

    申请日：2020-08-20

    Applicant: Adobe Inc.

    Inventor： Quan Hung Tran ,  Long Thanh Mai ,  Zhe Lin ,  Zhuowan Li

    IPC: G06K9/00 ,  G06K9/62 ,  G06F40/205 ,  G06F16/535 ,  G06F16/55

    Abstract: A group captioning system includes computing hardware, software, and/or firmware components in support of the enhanced group captioning contemplated herein. In operation, the system generates a target embedding for a group of target images, as well as a reference embedding for a group of reference images. The system identifies information in-common between the group of target images and the group of reference images and removes the joint information from the target embedding and the reference embedding. The result is a contrastive group embedding that includes a contrastive target embedding and a contrastive reference embedding with which to construct a contrastive group embedding, which is then input to a model to obtain a group caption for the target group of images.

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