公开(公告)号：US20220101040A1

公开(公告)日：2022-03-31

申请号：US17460316

申请日：2021-08-30

Applicant: FUJITSU LIMITED

Inventor： Meng ZHANG ,  Rujie LIU

IPC: G06K9/62 ,  G06K9/00

Abstract: A device and a method for classification using a pre-trained classification model and a computer readable storage medium are provided. The device is configured to extract, for each of multiple images in a target image group to be classified, a feature of the image using a feature extraction layer of the pre-trained classification model; calculate, for each of the multiple images, a contribution of the image to a classification result of the target image group using a contribution calculation layer of the pre-trained classification model; aggregate extracted features of the multiple images based on calculated contributions of the multiple images, to obtain an aggregated feature as a feature of the target image group; and classify the target image group based on the feature of the target image group.

公开(公告)号：US20190392248A1

公开(公告)日：2019-12-26

申请号：US16450153

申请日：2019-06-24

Applicant: FUJITSU LIMITED

Inventor： Meng ZHANG ,  Rujie LIU

IPC: G06K9/62 ,  G06N3/08

Abstract: The present disclosure relates to an information processing method and an information processing apparatus. The information processing method according to the present disclosure performs training on a classification model by using a plurality of training samples, and comprises the steps of: adjusting a distribution of feature vectors of the plurality of training samples in a feature space based on a typical sample in the plurality of training samples; and performing training on the classification model by using the adjusted feature vectors of the plurality of training samples. Through the technology according to the present disclosure, it is possible to perform pre-adjustment on training samples before training, such that it is possible to reduce discrimination between training samples belonging to a same class and increase discrimination between training samples belonging to different classes in the training process. The classification model trained as such is capable of performing accurate classification on samples acquired under an extreme condition.

公开(公告)号：US20230196735A1

公开(公告)日：2023-06-22

申请号：US18047780

申请日：2022-10-19

Applicant: Fujitsu Limited

Inventor： Meng ZHANG ,  Rujie LIU

IPC: G06V10/774 ,  G06V10/82 ,  G06V10/776

CPC classification number: G06V10/774 ,  G06V10/82 ,  G06V10/776

Abstract: A method and an apparatus for training a neural network, an image recognition method and a computer readable storage medium are disclosed. The neural network includes a first model and a second model. The method for training a neural network includes: acquiring a second image from a first image, wherein a quality of the second image is lower than that of the first image; inputting the first image into the first model of the neural network, and inputting the second image into the second model of the neural network; calculating an attention map and a gradient map of the first model and an attention map and a gradient map of the second model; constructing a loss function based on a matrix of a dot product of the gradient map and the attention map of the first model and a matrix of a dot product of the gradient map and the attention map of the second model; and training the neural network by minimizing the loss function.