DETECTING OBJECTS USING A WEAKLY SUPERVISED MODEL

    公开(公告)号:US20200334487A1

    公开(公告)日:2020-10-22

    申请号:US16919383

    申请日:2020-07-02

    Applicant: Adobe Inc.

    Abstract: The present disclosure is directed toward systems and methods for detecting an object in an input image based on a target object keyword. For example, one or more embodiments described herein generate a heat map of the input image based on the target object keyword and generate various bounding boxes based on a pixel analysis of the heat map. One or more embodiments described herein then utilize the various bounding boxes to determine scores for generated object location proposals in order to provide a highest scoring object location proposal overlaid on the input image.

    IMAGE SEARCHING BY EMPLOYING LAYERED SEARCH CONSTRAINTS

    公开(公告)号:US20190163766A1

    公开(公告)日:2019-05-30

    申请号:US15824836

    申请日:2017-11-28

    Applicant: ADOBE INC.

    Abstract: Systems and methods for searching digital content, such as digital images, are disclosed. A method includes receiving a first search constraint and generating search results based on the first search constraint. A search constraint includes search values or criteria. The search results include a ranked set of digital images. A second search constraint and a weight value associated with the second search constraint are received. The search results are updated based on the second search constraint and the weight value. The updated search results are provided to a user. Updating the search results includes determining a ranking (or a re-ranking) for each item of content included in the search results based on the first search constraint, the second search constraint, and the weight value. Re-ranking the search results may further be based on a weight value associated with the first search constraint, such as a default or maximum weight value.

    MEDIA CONTENT ITEM PROCESSING BASED ON USER INPUTS

    公开(公告)号:US20250117126A1

    公开(公告)日:2025-04-10

    申请号:US18904517

    申请日:2024-10-02

    Applicant: ADOBE INC.

    Abstract: A method, apparatus, non-transitory computer readable medium, and system for media processing includes obtaining a variation parameter and a number of variations, identifying a first variation input and a second variation input for the variation parameter, and obtaining a first media content item and a second media content item based on the first variation input and the second variation input, respectively. The first media content item and the second media content item vary from each other with respect to the variation parameter. The method, apparatus, non-transitory computer readable medium, and system for media processing further includes displaying the first media content item and the second media content item in a grid comprising a grid size based on the number of variations.

    Finding similar persons in images
    26.
    发明授权

    公开(公告)号:US11915520B2

    公开(公告)日:2024-02-27

    申请号:US17902349

    申请日:2022-09-02

    Applicant: Adobe Inc.

    CPC classification number: G06V40/172 G06F18/00 G06F18/29 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.

    HIGH RESOLUTION CONDITIONAL FACE GENERATION
    28.
    发明公开

    公开(公告)号:US20230162407A1

    公开(公告)日:2023-05-25

    申请号:US17455796

    申请日:2021-11-19

    Applicant: ADOBE INC.

    CPC classification number: G06T11/00 G06K9/00288 G06K9/00268 G06N3/08

    Abstract: The present disclosure describes systems and methods for image processing. Embodiments of the present disclosure include an image processing apparatus configured to generate modified images (e.g., synthetic faces) by conditionally changing attributes or landmarks of an input image. A machine learning model of the image processing apparatus encodes the input image to obtain a joint conditional vector that represents attributes and landmarks of the input image in a vector space. The joint conditional vector is then modified, according to the techniques described herein, to form a latent vector used to generate a modified image. In some cases, the machine learning model is trained using a generative adversarial network (GAN) with a normalization technique, followed by joint training of a landmark embedding and attribute embedding (e.g., to reduce inference time).

    Image segmentation using text embedding

    公开(公告)号:US11615567B2

    公开(公告)日:2023-03-28

    申请号:US16952008

    申请日:2020-11-18

    Applicant: Adobe Inc.

    Abstract: A non-transitory computer-readable medium includes program code that is stored thereon. The program code is executable by one or more processing devices for performing operations including generating, by a model that includes trainable components, a learned image representation of a target image. The operations further include generating, by a text embedding model, a text embedding of a text query. The text embedding and the learned image representation of the target image are in a same embedding space. Additionally, the operations include generating a class activation map of the target image by, at least, convolving the learned image representation of the target image with the text embedding of the text query. Moreover, the operations include generating an object-segmented image using the class activation map of the target image.

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