公开(公告)号：US11776036B2

公开(公告)日：2023-10-03

申请号：US15957556

申请日：2018-04-19

Applicant: Adobe Inc.

Inventor： Tuan Manh Lai ,  Trung Bui ,  Sheng Li ,  Quan Hung Tran ,  Hung Bui

IPC: G06Q30/0601 ,  G06N3/08 ,  G06F16/951 ,  G06F16/583 ,  G06V10/764

CPC classification number: G06Q30/0631 ,  G06F16/583 ,  G06F16/951 ,  G06N3/08 ,  G06V10/764

Abstract: The present description relates to systems, methods, and non-transitory computer readable media for generating digital responses to digital queries by utilizing a classification model and query-specific analysis models. For example, the described systems can train a classification model to generate query classifications corresponding to product queries, conversational queries, and/or recommendation/purchase queries. Moreover, the described systems can apply the classification model to select pertinent models for particular queries. For example, upon classifying a product query, the described systems can utilize a neural ranking model (trained based on a set of training product specifications and training queries) to generate relevance scores for product specifications associated with a digital query. The described systems can further compare generated relevance scores to select a product specification and generate a digital response that includes the pertinent product specification to provide for display to a client device.

公开(公告)号：US20220309762A1

公开(公告)日：2022-09-29

申请号：US17805289

申请日：2022-06-03

Applicant: Adobe Inc.

Inventor： Handong Zhao ,  Zhe Lin ,  Sheng Li ,  Mingyang Ling ,  Jiuxiang Gu

IPC: G06V10/26 ,  G06N3/08 ,  G06K9/62 ,  G06N3/04 ,  G06V10/426

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating semantic scene graphs for digital images using an external knowledgebase for feature refinement. For example, the disclosed system can determine object proposals and subgraph proposals for a digital image to indicate candidate relationships between objects in the digital image. The disclosed system can then extract relationships from an external knowledgebase for refining features of the object proposals and the subgraph proposals. Additionally, the disclosed system can generate a semantic scene graph for the digital image based on the refined features of the object/subgraph proposals. Furthermore, the disclosed system can update/train a semantic scene graph generation network based on the generated semantic scene graph. The disclosed system can also reconstruct the image using object labels based on the refined features to further update/train the semantic scene graph generation network.

公开(公告)号：US20200327446A1

公开(公告)日：2020-10-15

申请号：US16380566

申请日：2019-04-10

Applicant: Adobe Inc.

Inventor： Xiaowei Jia ,  Sheng Li ,  Handong Zhao ,  Sungchul Kim

IPC: G06N20/00

Abstract: Techniques are disclosed for the generation of adversarial training data through sequence perturbation, for a deep learning network to perform event sequence analysis. A methodology implementing the techniques according to an embodiment includes applying a long short-term memory attention model to an input data sequence to generate discriminative sequence periods and attention weights associated with the discriminative sequence periods. The attention weights are generated to indicate the relative importance of data in those discriminative sequence periods. The method further includes generating perturbed data sequences based on the discriminative sequence periods and the attention weights. The generation of the perturbed data sequences employs selective filtering or conservative adversarial training, to preserve perceptual similarity between the input data sequence and the perturbed data sequences. The input data sequence may be created by vectorizing a temporal input data stream comprising words, symbols, and the like, into a multidimensional vectorized numerical data sequence format.