公开(公告)号：US11978280B2

公开(公告)日：2024-05-07

申请号：US17529192

申请日：2021-11-17

Applicant: Lemon Inc.

Inventor： Jingna Sun ,  Peibin Chen ,  Weihong Zeng ,  Xu Wang ,  Jing Liu ,  Chunpong Lai ,  Shen Sang

IPC: G06K9/46 ,  G06F18/21 ,  G06F18/2113 ,  G06F18/22 ,  G06F18/2431 ,  G06V40/16

CPC classification number: G06V40/172 ,  G06F18/2113 ,  G06F18/217 ,  G06F18/22 ,  G06F18/2431

Abstract: A method is provided for evaluating an effect of classifying a fuzzy attribute of an object, the fuzzy attribute referring to an attribute, a boundary between two similar ones of a plurality of categories of which is blurred, wherein the method includes: generating a similarity-based ranked confusion matrix, which comprises: based on similarities of K categories of the fuzzy attribute of the object, ranking the K categories, where K is an integer greater than or equal to 2, generating a K×K all-zero initialization matrix, wherein an abscissa and an ordinate of the initialization matrix respectively represent predicted values and true values of the similarity-based ranked categories of the fuzzy attribute, and based on the true values and the predicted values of the category of the fuzzy attribute for the multiple object samples, updating values of corresponding elements in the initialization matrix; and displaying the similarity-based ranked confusion matrix.

公开(公告)号：US11928183B2

公开(公告)日：2024-03-12

申请号：US17532537

申请日：2021-11-22

Applicant: LEMON INC.

Inventor： Jingna Sun ,  Weihong Zeng ,  Peibin Chen ,  Xu Wang ,  Chunpong Lai ,  Shen Sang ,  Jing Liu

IPC: G06F18/214 ,  G06F16/532 ,  G06F18/24 ,  G06V40/16

CPC classification number: G06F18/2155 ,  G06F16/532 ,  G06F18/24 ,  G06V40/168

Abstract: An image processing method includes acquiring a set of image samples for training an attribute recognition model, wherein the set of image samples includes a first subset of image samples with category labels and a second subset of image samples without category labels; training a sample prediction model using the first subset of image samples, and predicting categories of the image samples in the second subset of image samples using the trained sample prediction model; determining a category distribution of the set of image samples based on the category labels of the first subset of image samples and the predicted categories of the second subset of image samples; and acquiring a new image sample if the determined category distribution does not conform to the expected category distribution, and adding the acquired new image sample to the set of image samples.

公开(公告)号：US12106545B2

公开(公告)日：2024-10-01

申请号：US17534681

申请日：2021-11-24

Applicant: LEMON INC.

Inventor： Jingna Sun ,  Peibin Chen ,  Weihong Zeng ,  Xu Wang ,  Jing Liu ,  Chunpong Lai ,  Shen Sang

IPC: G06V10/764 ,  G06N3/08 ,  G06V10/72 ,  G06V10/82

CPC classification number: G06V10/764 ,  G06N3/08 ,  G06V10/72 ,  G06V10/82

Abstract: The present disclosure provides a training method and device for an image identifying model, and an image identifying method. The training method comprises: obtaining image samples of a plurality of categories; inputting image samples of each category into a feature extraction layer of the image identifying model to extract a feature vector of each image sample; calculating a statistical characteristic information of an actual distribution function corresponding to each category according to the feature vector of each image sample of the each category; establishing an augmented distribution function corresponding to the each category according to the statistical characteristic information; obtaining augmented sample features of the each category based on the augmented distribution function; and inputting feature vectors of the image samples and the augmented sample features into a classification layer of the image identifying model for supervised learning.