公开(公告)号：US10204090B2

公开(公告)日：2019-02-12

申请号：US15651822

申请日：2017-07-17

Applicant: OATH INC.

Inventor： Jia Li ,  Xiangnan Kong

IPC: G06F17/20 ,  G06F17/24 ,  G06F17/30 ,  G06K9/00 ,  G06K9/62

Abstract: System, method and architecture for providing improved visual recognition by modeling visual content, semantic content and an implicit social network representing individuals depicted in a collection of content, such as visual images, photographs, etc., which network may be determined based on co-occurrences of individuals represented by the content, and/or other data linking the individuals. In accordance with one or more embodiments, using images as an example, a relationship structure may comprise an implicit structure, or network, determined from co-occurrences of individuals in the images. A kernel jointly modeling content, semantic and social network information may be built and used in automatic image annotation and/or determination of relationships between individuals, for example.

公开(公告)号：US10325220B2

公开(公告)日：2019-06-18

申请号：US14543133

申请日：2014-11-17

Applicant: OATH INC.

Inventor： Jia Li ,  Yi Chang ,  Xiangnan Kong

IPC: G06N7/02 ,  G06N7/04 ,  G06N7/06 ,  G06N7/08 ,  G06N20/00

Abstract: At least one label prediction model is trained, or learned, using training data that may comprise training instances that may be missing one or more labels. The at least one label prediction model may be used in identifying a content item's ground-truth label set comprising an indicator for each label in the label set indicating whether or not the label is applicable to the content item.