TEXT-CONDITIONED IMAGE SEARCH BASED ON TRANSFORMATION, AGGREGATION, AND COMPOSITION OF VISIO-LINGUISTIC FEATURES

    公开(公告)号:US20220245391A1

    公开(公告)日:2022-08-04

    申请号:US17160893

    申请日:2021-01-28

    Applicant: Adobe Inc.

    Abstract: Techniques are disclosed for text-conditioned image searching. A methodology implementing the techniques includes decomposing a source image into visual feature vectors associated with different levels of granularity. The method also includes decomposing a text query (defining a target image attribute) into feature vectors associated with different levels of granularity including a global text feature vector. The method further includes generating image-text embeddings based on the visual feature vectors and the text feature vectors to encode information from visual and textual features. The method further includes composing a visio-linguistic representation based on a hierarchical aggregation of the image-text embeddings to encode visual and textual information at multiple levels of granularity. The method further includes identifying a target image that includes the visio-linguistic representation and the global text feature vector, so that the target image relates to the target image attribute, and providing the target image as an image search result.

    FORM STRUCTURE EXTRACTION BY PREDICTING ASSOCIATIONS

    公开(公告)号:US20210397986A1

    公开(公告)日:2021-12-23

    申请号:US16904263

    申请日:2020-06-17

    Applicant: Adobe Inc.

    Abstract: Techniques described herein extract form structures from a static form to facilitate making that static form reflowable. A method described herein includes accessing low-level form elements extracted from a static form. The method includes determining, using a first set of prediction models, second-level form elements based on the low-level form elements. Each second-level form element includes a respective one or more low-level form elements. The method further includes determining, using a second set of prediction models, high-level form elements based on the second-level form elements and the low-level form elements. Each high-level form element includes a respective one or more second-level form elements or low-level form elements. The method further includes generating a reflowable form based on the static form by, for each high-level form element, linking together the respective one or more second-level form elements or low-level form elements.

    Form structure extraction network
    43.
    发明授权

    公开(公告)号:US10268883B2

    公开(公告)日:2019-04-23

    申请号:US15674100

    申请日:2017-08-10

    Applicant: Adobe Inc.

    Abstract: A method and system for detecting and extracting accurate and precise structure in documents. A high-resolution image of documents is segmented into a set of tiles. Each tile is processed by a convolutional network and subsequently by a set of recurrent networks for each row and column. A global-lookup process is disclosed that allows “future” information required for accurate assessment by the recurrent neural networks to be considered. Utilization of high-resolution image allows for precise and accurate feature extraction while segmentation into tiles facilitates the tractable processing of the high-resolution image within reasonable computational resource bounds.

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