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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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公开(公告)号:US11874902B2
公开(公告)日:2024-01-16
申请号:US17160862
申请日:2021-01-28
Applicant: Adobe Inc.
Inventor: Pinkesh Badjatiya , Surgan Jandial , Pranit Chawla , Mausoom Sarkar , Ayush Chopra
IPC: G06F18/25 , G06N3/04 , G06F16/538 , G06F16/532 , G06F16/535 , G06F18/214
CPC classification number: G06F18/253 , G06F16/532 , G06F16/535 , G06F16/538 , G06F18/214 , G06F18/251 , G06N3/04
Abstract: Techniques are disclosed for text conditioned image searching. A methodology implementing the techniques according to an embodiment includes receiving a source image and a text query defining a target image attribute. The method also includes decomposing the source image into image content and style feature vectors and decomposing the text query into text content and style feature vectors, wherein image style is descriptive of image content and text style is descriptive of text content. The method further includes composing a global content feature vector based on the text content feature vector and the image content feature vector and composing a global style feature vector based on the text style feature vector and the image style feature vector. The method further includes identifying a target image that relates to the global content feature vector and the global style feature vector so that the target image relates to the target image attribute.
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公开(公告)号:US20220309093A1
公开(公告)日:2022-09-29
申请号:US17806922
申请日:2022-06-14
Applicant: Adobe Inc.
Inventor: Ayush Chopra , Mausoom Sarkar , Jonas Dahl , Hiresh Gupta , Balaji Krishnamurthy , Abhishek Sinha
IPC: G06F16/535 , G06K9/62 , G06F17/15 , G06N3/04 , G06F16/55
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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公开(公告)号:US20200372560A1
公开(公告)日:2020-11-26
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
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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公开(公告)号:US20220237406A1
公开(公告)日:2022-07-28
申请号:US17160862
申请日:2021-01-28
Applicant: Adobe Inc.
Inventor: Pinkesh Badjatiya , Surgan Jandial , Pranit Chawla , Mausoom Sarkar , Ayush Chopra
IPC: G06K9/62 , G06N3/04 , G06F16/532 , G06F16/535 , G06F16/538
Abstract: Techniques are disclosed for text conditioned image searching. A methodology implementing the techniques according to an embodiment includes receiving a source image and a text query defining a target image attribute. The method also includes decomposing the source image into image content and style feature vectors and decomposing the text query into text content and style feature vectors, wherein image style is descriptive of image content and text style is descriptive of text content. The method further includes composing a global content feature vector based on the text content feature vector and the image content feature vector and composing a global style feature vector based on the text style feature vector and the image style feature vector. The method further includes identifying a target image that relates to the global content feature vector and the global style feature vector so that the target image relates to the target image attribute.
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公开(公告)号:US20210256387A1
公开(公告)日:2021-08-19
申请号:US16793551
申请日:2020-02-18
Applicant: Adobe Inc.
Inventor: Ayush Chopra , Balaji Krishnamurthy , Mausoom Sarkar , Surgan Jandial
Abstract: Generating a machine learning model that is trained using retrospective loss is described. A retrospective loss system receives an untrained machine learning model and a task for training the model. The retrospective loss system initially trains the model over warm-up iterations using task-specific loss that is determined based on a difference between predictions output by the model during training on input data and a ground truth dataset for the input data. Following the warm-up training iterations, the retrospective loss system continues to train the model using retrospective loss, which is model-agnostic and constrains the model such that a subsequently output prediction is more similar to the ground truth dataset than the previously output prediction. After determining that the model's outputs are within a threshold similarity to the ground truth dataset, the model is output with its current parameters as a trained model.
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公开(公告)号:US20210142539A1
公开(公告)日:2021-05-13
申请号:US16679165
申请日:2019-11-09
Applicant: Adobe Inc.
Inventor: Kumar Ayush , Surgan Jandial , Abhijeet Kumar , Mayur Hemani , Balaji Krishnamurthy , Ayush Chopra
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a virtual try-on digital image utilizing a unified neural network framework. For example, the disclosed systems can utilize a coarse-to-fine warping process to generate a warped version of a product digital image to fit a model digital image. In addition, the disclosed systems can utilize a texture transfer process to generate a corrected segmentation mask indicating portions of a model digital image to replace with a warped product digital image. The disclosed systems can further generate a virtual try-on digital image based on a warped product digital image, a model digital image, and a corrected segmentation mask. In some embodiments, the disclosed systems can train one or more neural networks to generate accurate outputs for various stages of generating a virtual try-on digital image.
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公开(公告)号:US20210073671A1
公开(公告)日:2021-03-11
申请号:US16564531
申请日:2019-09-09
Applicant: Adobe, Inc.
Inventor: Nikaash Puri , Balaji Krishnamurthy , Ayush Chopra
Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for generating combined feature embeddings for minority class upsampling in training machine learning models with imbalanced training samples. For example, the disclosed systems can select training sample values from a set of training samples and a combination ratio value from a continuous probability distribution. Additionally, the disclosed systems can generate a combined synthetic training sample value by modifying the selected training sample values using the combination ratio value and combining the modified training sample values. Moreover, the disclosed systems can generate a combined synthetic ground truth label based on the combination ratio value. In addition, the disclosed systems can utilize the combined synthetic training sample value and the combined synthetic ground truth label to generate a combined synthetic training sample and utilize the combined synthetic training sample to train a machine learning model.
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9.
公开(公告)号:US11907816B2
公开(公告)日:2024-02-20
申请号:US17892878
申请日:2022-08-22
Applicant: Adobe Inc.
Inventor: Pinkesh Badjatiya , Nikaash Puri , Ayush Chopra , Anubha Kabra
IPC: G06N20/00 , G06N20/10 , G06F18/2431 , G06F18/211 , G06F18/214 , G06F18/2453
CPC classification number: G06N20/00 , G06F18/211 , G06F18/214 , G06F18/2431 , G06F18/2453 , G06N20/10
Abstract: A data classification system is trained to classify input data into multiple classes. The system is initially trained by adjusting weights within the system based on a set of training data that includes multiple tuples, each being a training instance and corresponding training label. Two training instances, one from a minority class and one from a majority class, are selected from the set of training data based on entropies for the training instances. A synthetic training instance is generated by combining the two selected training instances and a corresponding training label is generated. A tuple including the synthetic training instance and the synthetic training label is added to the set of training data, resulting in an augmented training data set. One or more such synthetic training instances can be added to the augmented training data set and the system is then re-trained on the augmented training data set.
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公开(公告)号:US20230316379A1
公开(公告)日:2023-10-05
申请号:US18186528
申请日:2023-03-20
Applicant: Adobe Inc.
Inventor: Kumar AYUSH , Ayush Chopra , Patel U. Govind , Balaji Krishnamurthy , Anirudh Singhal
IPC: G06Q30/0601 , G06N3/088 , G06F18/214 , G06F18/21 , G06N3/045 , G06V10/764 , G06V10/82 , G06V10/44 , G06V20/00
CPC classification number: G06Q30/0631 , G06F18/214 , G06F18/2193 , G06N3/045 , G06N3/088 , G06V10/454 , G06V10/764 , G06V10/82 , G06V20/00
Abstract: Systems, methods, and computer storage media are disclosed for predicting visual compatibility between a bundle of catalog items (e.g., a partial outfit) and a candidate catalog item to add to the bundle. Visual compatibility prediction may be jointly conditioned on item type, context, and style by determining a first compatibility score jointly conditioned on type (e.g., category) and context, determining a second compatibility score conditioned on outfit style, and combining the first and second compatibility scores into a unified visual compatibility score. A unified visual compatibility score may be determined for each of a plurality of candidate items, and the candidate item with the highest unified visual compatibility score may be selected to add to the bundle (e.g., fill the in blank for the partial outfit).
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