公开(公告)号：US11372914B2

公开(公告)日：2022-06-28

申请号：US15936117

申请日：2018-03-26

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yokesh Kumar ,  Kuang-Huei Lee ,  Houdong Hu ,  Li Huang ,  Arun Sacheti ,  Meenaz Merchant ,  Linjun Yang ,  Tianjun Xiao ,  Saurajit Mukherjee

IPC: G06F16/583 ,  G06F16/58 ,  G06F16/51 ,  G06F16/538 ,  G06N5/02 ,  G06F16/9535 ,  G06N20/00

Abstract: The description relates to diversified hybrid image annotation for annotating images. One implementation includes generating first image annotations for a query image using a retrieval-based image annotation technique. Second image annotations can be generated for the query image using a model-based image annotation technique. The first and second image annotations can be integrated to generate a diversified hybrid image annotation result for the query image.

公开(公告)号：US11947589B2

公开(公告)日：2024-04-02

申请号：US17710761

申请日：2022-03-31

Applicant: Microsoft Technology Licensing, LLC

Inventor： Li Huang ,  Rui Xia ,  Zhiting Chen ,  Kun Wu ,  Meenaz Merchant ,  Kamal Ginotra ,  Arun K. Sacheti ,  Chu Wang ,  Andrew Lawrence Stewart ,  Hanmu Zuo ,  Saurajit Mukherjee

IPC: G06F16/50 ,  G06F16/532 ,  G06F16/535 ,  G06F16/56

CPC classification number: G06F16/535 ,  G06F16/532 ,  G06F16/56

Abstract: Systems and methods directed to returning personalized image-based search results are described. In examples, a query including an image may be received, and a personalized item embedding may be generated based on the image and user profile information associated with a user. Further, a plurality of candidate images may be obtained based on the personalized item embedding. The candidate images may then be ranked according to a predicted level of user engagement for a user, and then diversified to ensure visual diversity among the ranked images. A portion of the diversified images may then be returned in response to an image-based search.

公开(公告)号：US20190180146A1

公开(公告)日：2019-06-13

申请号：US15840823

申请日：2017-12-13

Applicant: Microsoft Technology Licensing, LLC

Inventor： Arun Sacheti ,  FNU Yokesh Kumar ,  Saurajit Mukherjee ,  Nikesh Srivastava ,  Yan Wang ,  Kuang-Huei Lee ,  Surendra Ulabala

IPC: G06K9/62 ,  G06F17/30

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today. Processing described herein, including implementation of an exemplary ensemble data model, may be exposed as a web service that is standalone or integrated within other applications/services to enhance processing efficiency and productivity applications/services such as productivity applications/services.

公开(公告)号：US10607118B2

公开(公告)日：2020-03-31

申请号：US15840823

申请日：2017-12-13

Applicant: Microsoft Technology Licensing, LLC

Inventor： Arun Sacheti ,  FNU Yokesh Kumar ,  Saurajit Mukherjee ,  Nikesh Srivastava ,  Yan Wang ,  Kuang-Huei Lee ,  Surendra Ulabala

IPC: G06K9/62 ,  G06F16/532 ,  G06F16/583 ,  G06K9/00

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today. Processing described herein, including implementation of an exemplary ensemble data model, may be exposed as a web service that is standalone or integrated within other applications/services to enhance processing efficiency and productivity applications/services such as productivity applications/services.

公开(公告)号：US10997468B2

公开(公告)日：2021-05-04

申请号：US16799528

申请日：2020-02-24

Applicant: Microsoft Technology Licensing, LLC

Inventor： Arun Sacheti ,  Fnu Yokesh Kumar ,  Saurajit Mukherjee ,  Nikesh Srivastava ,  Yan Wang ,  Kuang-Huei Lee ,  Surendra Ulabala

IPC: G06K9/62 ,  G06F16/532 ,  G06F16/583 ,  G06K9/00

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today. Processing described herein, including implementation of an exemplary ensemble data model, may be exposed as a web service that is standalone or integrated within other applications/services to enhance processing efficiency and productivity applications/services such as productivity applications/services.