公开(公告)号：US20220292131A1

公开(公告)日：2022-09-15

申请号：US17826760

申请日：2022-05-27

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Ruibin BAI ,  Xiang WEI ,  Yipeng SUN ,  Kun YAO ,  Jingtuo LIU ,  Junyu HAN

IPC: G06F16/583 ,  G06V10/74 ,  G06V10/44 ,  G06F16/535

Abstract: A method, apparatus and system for retrieving an image is provided, the method comprises: detecting, in response to receiving a query request comprising a target image, a target subject from the target image; extracting a subject feature from the target subject if a confidence level of a detection box of the detected target subject is greater than a first threshold, the subject feature comprising an identical feature, a similar feature and a category; performing matching on the subject feature of the target image and a subject feature of a candidate image pre-stored in a database, to obtain a similarity score and an identicalness score of the candidate image; and selecting, according to the similarity score and the identicalness score, a predetermined number of candidate images as a search result for output.

公开(公告)号：US20220392205A1

公开(公告)日：2022-12-08

申请号：US17892669

申请日：2022-08-22

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Yipeng SUN ,  Rongqiao AN ,  Xiang WEI ,  Longchao WANG ,  Kun YAO ,  Junyu HAN ,  Jingtuo LIU ,  Errui DING

IPC: G06V10/80 ,  G06V10/77

Abstract: Embodiments of the present disclosure provide a method and apparatus for training an image recognition model based on a semantic enhancement, a method and apparatus for recognizing an image, an electronic device, and a computer readable storage medium. The method for training an image recognition model based on a semantic enhancement comprises: extracting, from an inputted first image being unannotated and having no textual description, a first feature representation of the first image; calculating a first loss function based on the first feature representation; extracting, from an inputted second image being unannotated and having an original textual description, a second feature representation of the second image; calculating a second loss function based on the second feature representation, and training an image recognition model based on a fusion of the first loss function and the second loss function.