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公开(公告)号：US20240378912A1
公开(公告)日：2024-11-14
申请号：US18316617
申请日：2023-05-12
Applicant: Adobe Inc.
Inventor： Mausoom Sarkar , Nikitha S R , Mayur Hemani , Rishabh Jain , Balaji Krishnamurthy
IPC: G06V20/70 , G06T3/40 , G06T7/11 , G06V10/46 , G06V10/764 , G06V10/77 , G06V10/774 , G06V10/82 , G06V40/16
Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that utilize a local implicit image function neural network to perform image segmentation with a continuous class label probability distribution. For example, the disclosed systems utilize a local-implicit-image-function (LIIF) network to learn a mapping from an image to its semantic label space. In some instances, the disclosed systems utilize an image encoder to generate an image vector representation from an image. Subsequently, in one or more implementations, the disclosed systems utilize the image vector representation with a LIIF network decoder that generates a continuous probability distribution in a label space for the image to create a semantic segmentation mask for the image. Moreover, in some embodiments, the disclosed systems utilize the LIIF-based segmentation network to generate segmentation masks at different resolutions without changes in an input resolution of the segmentation network.
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公开(公告)号：US11983946B2
公开(公告)日：2024-05-14
申请号：US17517434
申请日：2021-11-02
Applicant: Adobe Inc.
Inventor： Shripad Deshmukh , Milan Aggarwal , Mausoom Sarkar , Hiresh Gupta
IPC: G06V30/414 , G06F18/21 , G06N3/08 , G06V10/94 , G06V30/18 , G06V30/262
CPC classification number: G06V30/414 , G06F18/21 , G06N3/08 , G06V10/95 , G06V30/18 , G06V30/274
Abstract: In implementations of refining element associations for form structure extraction, a computing device implements a structure system to receive estimate data describing estimated associations of elements included in a form and a digital image depicting the form. An image patch is extracted from the digital image, and the image patch depicts a pair of elements of the elements included in the form. The structure system encodes an indication of whether the pair of elements have an association of the estimated associations. An indication is generated that the pair of elements have a particular association based at least partially on the encoded indication, bounding boxes of the pair of elements, and text depicted in the image patch.
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公开(公告)号：US11948358B2
公开(公告)日：2024-04-02
申请号：US17455126
申请日：2021-11-16
Applicant: ADOBE INC.
Inventor： Sumegh Roychowdhury , Sumedh A. Sontakke , Mausoom Sarkar , Nikaash Puri , Pinkesh Badjatiya , Milan Aggarwal
Abstract: Systems and methods for video processing are described. Embodiments of the present disclosure generate a plurality of image feature vectors corresponding to a plurality of frames of a video; generate a plurality of low-level event representation vectors based on the plurality of image feature vectors, wherein a number of the low-level event representation vectors is less than a number of the image feature vectors; generate a plurality of high-level event representation vectors based on the plurality of low-level event representation vectors, wherein a number of the high-level event representation vectors is less than the number of the low-level event representation vectors; and identify a plurality of high-level events occurring in the video based on the plurality of high-level event representation vectors.
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公开(公告)号：US11593552B2
公开(公告)日：2023-02-28
申请号：US15927686
申请日：2018-03-21
Applicant: Adobe Inc.
Inventor： Mausoom Sarkar
IPC: G06N3/08 , G06F40/30 , G06F40/174 , G06T7/10
Abstract: The present disclosure relates to generating fillable digital forms corresponding to paper forms using a form conversion neural network to determine low-level and high-level semantic characteristics of the paper forms. For example, one or more embodiments applies a digitized paper form to an encoder that outputs feature maps to a reconstruction decoder, a low-level semantic decoder, and one or more high-level semantic decoders. The reconstruction decoder generates a reconstructed layout of the digitized paper form. The low-level and high-level semantic decoders determine low-level and high-level semantic characteristics of each pixel of the digitized paper form, which provide a probability of the element type to which the pixel belongs. The semantic decoders then classify each pixel and generate corresponding semantic segmentation maps based on those probabilities. The system then generates a fillable digital form using the reconstructed layout and the semantic segmentation maps.
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公开(公告)号：US11386144B2
公开(公告)日：2022-07-12
申请号：US16564831
申请日：2019-09-09
Applicant: Adobe, Inc.
Inventor： Ayush Chopra , Mausoom Sarkar , Jonas Dahl , Hiresh Gupta , Balaji Krishnamurthy , Abhishek Sinha
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating tags for an object portrayed in a digital image based on predicted attributes of the object. For example, the disclosed systems can utilize interleaved neural network layers of alternating inception layers and dilated convolution layers to generate a localization feature vector. Based on the localization feature vector, the disclosed systems can generate attribute localization feature embeddings, for example, using some pooling layer such as a global average pooling layer. The disclosed systems can then apply the attribute localization feature embeddings to corresponding attribute group classifiers to generate tags based on predicted attributes. In particular, attribute group classifiers can predict attributes as associated with a query image (e.g., based on a scoring comparison with other potential attributes of an attribute group). Based on the generated tags, the disclosed systems can respond to tag queries and search queries.
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Patent Agency Ranking
公开(公告)号：US10755199B2
公开(公告)日：2020-08-25
申请号：US15608517
申请日：2017-05-30
Applicant: Adobe Inc.
Inventor： Mausoom Sarkar , Balaji Krishnamurthy , Abhishek Sinha , Aahitagni Mukherjee
Abstract: An introspection network is a machine-learned neural network that accelerates training of other neural networks. The introspection network receives a weight history for each of a plurality of weights from a current training step for a target neural network. A weight history includes at least four values for the weight that are obtained during training of the target neural network up to the current step. The introspection network then provides, for each of the plurality of weights, a respective predicted value, based on the weight history. The predicted value for a weight represents a value for the weight in a future training step for the target neural network. Thus, the predicted value represents a jump in the training steps of the target neural network, which reduces the training time of the target neural network. The introspection network then sets each of the plurality of weights to its respective predicted value.
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公开(公告)号：US20190294661A1
公开(公告)日：2019-09-26
申请号：US15927686
申请日：2018-03-21
Applicant: Adobe Inc.
Inventor： Mausoom Sarkar
Abstract: The present disclosure relates to generating fillable digital forms corresponding to paper forms using a form conversion neural network to determine low-level and high-level semantic characteristics of the paper forms. For example, one or more embodiments applies a digitized paper form to an encoder that outputs feature maps to a reconstruction decoder, a low-level semantic decoder, and one or more high-level semantic decoders. The reconstruction decoder generates a reconstructed layout of the digitized paper form. The low-level and high-level semantic decoders determine low-level and high-level semantic characteristics of each pixel of the digitized paper form, which provide a probability of the element type to which the pixel belongs. The semantic decoders then classify each pixel and generate corresponding semantic segmentation maps based on those probabilities. The system then generates a fillable digital form using the reconstructed layout and the semantic segmentation maps.
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公开(公告)号：US20240362941A1
公开(公告)日：2024-10-31
申请号：US18140143
申请日：2023-04-27
Applicant: Adobe Inc.
Inventor： Silky Singh , Surgan Jandial , Shripad Vilasrao Deshmukh , Milan Aggarwal , Mausoom Sarkar , Balaji Krishnamurthy , Arneh Jain , Abhinav Java
IPC: G06V30/262 , G06V30/14 , G06V30/19 , G06V30/414
CPC classification number: G06V30/274 , G06V30/1444 , G06V30/19147 , G06V30/414
Abstract: A corrective noise system receives an electronic version of a fillable form generated by a segmentation network and receives a correction to a segmentation error in the electronic version of the fillable form. The corrective noise system is trained to generate noise that represents the correction and superimpose the noise on the fillable form. The corrective noise system is further trained to identify regions in a corpus of forms that are semantically similar to a region that was subject to the correction. The generated noise is propagated to the semantically similar regions in the corpus of forms and the noisy corpus of forms is provided as input to the segmentation network. The noise causes the segmentation network to accurately identify fillable regions in the corpus of forms and output a segmented version of the corpus of forms having improved fidelity without retraining or otherwise modifying the segmentation network.
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公开(公告)号：US20240330351A1
公开(公告)日：2024-10-03
申请号：US18190686
申请日：2023-03-27
Applicant: Adobe Inc.
Inventor： Abhinav Java , Surgan Jandial , Shripad Vilasrao Deshmukh , Milan Aggarwal , Mausoom Sarkar , Balaji Krishnamurthy , Arneh Jain
IPC: G06F16/383 , G06F16/332 , G06V30/19 , G06V30/412
CPC classification number: G06F16/383 , G06F16/332 , G06V30/19147 , G06V30/412
Abstract: Form structure similarity detection techniques are described. A content processing system, for instance, receives a query snippet that depicts a query form structure. The content processing system generates a query layout string that includes semantic indicators to represent the query form structure and generates candidate layout strings that represent form structures from a target document. The content processing system calculates similarity scores between the query layout string and the candidate layout strings. Based on the similarity scores, the content processing system generates a target snippet for display that depicts a form structure that is structurally similar to the query form structure. The content processing system is further operable to generate a training dataset that includes image pairs of snippets depicting form structures that are structurally similar. The content processing system utilizes the training dataset to train a machine learning model to perform form structure similarity matching.
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公开(公告)号：US11972466B2
公开(公告)日：2024-04-30
申请号：US16417373
申请日：2019-05-20
Applicant: ADOBE INC.
Inventor： Jonas Dahl , Mausoom Sarkar , Hiresh Gupta , Balaji Krishnamurthy , Ayush Chopra , Abhishek Sinha
IPC: G06Q30/0601 , G06F16/583
CPC classification number: G06Q30/0625 , G06F16/5854
Abstract: A search system provides search results with images of products based on associations of primary products and secondary products from product image sets. The search system analyzes a product image set containing multiple images to determine a primary product and secondary products. Information associating the primary and secondary products are stored in a search index. When the search system receives a query image containing a search product, the search index is queried using the search product to identify search result images based on associations of products in the search index, and the result images are provided as a response to the query image.
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