-
公开(公告)号：US20240420458A1
公开(公告)日：2024-12-19
申请号：US18744418
申请日：2024-06-14
Applicant: Lemon Inc. , BEIJING ZITIAO NETWORK TECHNOLOGY CO., LTD. , INSTITUTE OF AUTOMATION CHINESE ACADEMY OF SCIENCES
Inventor： Xiaojie JIN , Sihan CHEN , Jiashi FENG , Xingjian HE , Handong LI , Jing LIU
Abstract: The disclosure provides a cross-modal data processing method and apparatus, a device, a storage medium, and a program product. The method comprises: obtaining first modal data to be processed; obtaining a first modal data feature by performing feature extraction based on the first modal data; and obtaining second modal data based on the first modal data feature and a cross-modal processing model, the first modal data and the second modal data having different modalities, wherein the cross-modal processing model needs to be pre-trained based on a concatenated training sample, and the concatenated training sample comprises a concatenated image sample and a corresponding concatenated text sample.
-
公开(公告)号：US20230328197A1
公开(公告)日：2023-10-12
申请号：US18332243
申请日：2023-06-09
Applicant: Lemon Inc.
Inventor： Yaxi GAO , Chenyu SUN , Xiao YANG , Zhili CHEN , Linjie LUO , Jing LIU , Hengkai GUO , Huaxia LI , Hwankyoo Shawn KIM , Jianchao YANG
IPC: G06T7/73 , G06T7/12 , G11B27/036 , H04N5/265 , G11B27/10
CPC classification number: H04N5/265 , G06T7/12 , G06T7/73 , G11B27/036 , G11B27/10 , G06T2200/24 , G06T2207/10021 , G06T2207/20101
Abstract: Embodiments of the present disclosure provide a display method and apparatus based on augmented reality, a device, and a storage medium, the method includes receiving a first video; acquiring a video material by segmenting a target object from the first video; acquiring and displaying a real scene image, where the real scene image is acquired by an image collection apparatus; and displaying the video material at a target position of the real scene image in an augmented manner and playing the video material. Since the video material is acquired by receiving the first video and segmenting the target object from the first video, the video material may be set according to the needs of the user.
-
3.发明申请公开(公告)号：US20230036366A1
公开(公告)日：2023-02-02
申请号：US17538938
申请日：2021-11-30
Applicant: LEMON INC.
Inventor： Jingna SUN , Weihong ZENG , Peibin CHEN , Xu WANG , Shen SANG , Jing LIU , Chunpong LAI
Abstract: The present disclosure relates to an image attribute classification method, apparatus, electronic device, medium, and program product. The present disclosure enables inputting the image to a feature extraction network to obtain a feature map after feature extraction and N times down-sampling, wherein at least one attribute of the image occupies a second rectangular position area in the feature map after N times down-sampling; calculating a mask function of the at least one attribute of the feature map after N times down-sampling based on the second rectangular position area; obtaining a feature corresponding to the at least one attribute by dot multiplying the feature map after N times down-sampling with the mask function; and inputting the obtained feature corresponding to the at least one attribute to the corresponding attribute classifier for attribute classification.
-
公开(公告)号：US20220375024A1
公开(公告)日：2022-11-24
申请号：US17321384
申请日：2021-05-14
Applicant: Lemon Inc.
Inventor： Linjie LUO , Guoxian SONG , Jing LIU , Wanchun MA
Abstract: Systems and method directed to an inversion-consistent transfer learning framework for generating portrait stylization using only limited exemplars. In examples, an input image is received and encoded using a variational autoencoder to generate a latent vector. The latent vector may be provided to a generative adversarial network (GAN) generator to generate a stylized image. In examples, the variational autoencoder is trained using a plurality of images while keeping the weights of a pre-trained GAN generator fixed, where the pre-trained GAN generator acts as a decoder for the encoder. In other examples, a multi-path attribute aware generator is trained using a plurality of exemplar images and learning transfer using the pre-trained GAN generator.
-
公开(公告)号：US20250005827A1
公开(公告)日：2025-01-02
申请号：US18687215
申请日：2022-07-15
Applicant: Lemon Inc.
Inventor： Shen SANG , Jing LIU , Chunpong LAI , Jingna SUN , Xu WANG , Weihong ZENG , Peibin CHEN
IPC: G06T11/60 , G06V10/774
Abstract: The present disclosure relates to an image generation method, apparatus, and device, and a medium. The method comprises: acquiring a first image, keeping a target attribute in the first image unchanged, and editing other attributes in the first image; on the basis of the target attribute and the edited other attributes, generating a second image, so as to obtain the second image having the target attribute unchanged and other attributes changed. Therefore, the effect of quick image generation and improved image diversification of FIG. 5 can be achieved, such that during model training, the balance of training samples is improved, so as to improve the performance of the model.
-
Patent Agency Ranking
6.发明申请公开(公告)号：US20230030740A1
公开(公告)日：2023-02-02
申请号：US17532480
申请日：2021-11-22
Applicant: LEMON INC.
Inventor： Jingna SUN , Peibin CHEN , Weihong ZENG , Xu WANG , Shen SANG , Jing LIU , Chunpong LAI
IPC: G06K9/62
Abstract: The present disclosure relates to an image annotating method, classification method and machine learning model training method, and to the field of computer technologies. The image annotating method includes: generating an image tag vector of image to be annotated, according to a plurality of attributes for image annotating and multiple tags corresponding to each of the attributes; annotating an image category to which the image to be annotated belongs, according to vector similarity between the image tag vector and an category tag vector of each of a plurality of image categories, the category tag vector being generated according to the multiple tags corresponding to each of the attributes.
-
7.发明公开公开(公告)号：US20240290023A1
公开(公告)日：2024-08-29
申请号：US18570571
申请日：2022-10-21
Applicant: Lemon Inc
Inventor： Weihong ZENG , Xu WANG , Jing LIU , Shen SANG , Haishan LIU
CPC classification number: G06T13/40 , G06T7/248 , G06T7/55 , G06T7/64 , G06T19/20 , G06V40/171 , G06T2207/30201 , G06T2219/2021
Abstract: An image processing method and apparatus, an electronic device, and a computer-readable storage medium are provided. The image processing method includes: in response to having detected a detection object, acquiring current feature information of the detection object; acquiring limit deformation information of the target feature, wherein the limit deformation information is obtained by calculating a target virtual sub-image when the target feature is in at least one limit state; determining movement information of a feature point in an initial virtual image based on the limit deformation information and the current feature information, wherein the initial virtual image is obtained by superimposing a plurality of virtual sub-images; and driving, according to the movement information, the feature point in the initial virtual image to move, so as to generate the current virtual image corresponding to the current state.
-
公开(公告)号：US20230325975A1
公开(公告)日：2023-10-12
申请号：US18208761
申请日：2023-06-12
Applicant: Lemon Inc.
Inventor： Tiancheng ZHI , Shen SANG , Jing LIU , Linjie LUO
CPC classification number: G06T3/4046 , G06T5/50 , G06T2207/20084 , G06T2207/20081 , G06T2207/20132 , G06T2207/20024
Abstract: A method for training an image processor having a neural network model is described. A first training set of images having a first image resolution is generated. A second training set of images having a second image resolution is generated. The second image resolution is larger than the first image resolution. The neural network model of the image processor is trained using the first training set of images during a first training session. The neural network model of the image processor is trained using the second training set of images during a second training session after the first training session.
-
公开(公告)号：US20230035131A1
公开(公告)日：2023-02-02
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 , G06V10/82 , G06V10/72 , G06N3/08
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.
-
公开(公告)号：US20230033303A1
公开(公告)日：2023-02-02
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
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.
-
-
-
-
-
-
-
-
-
Search Results
13
records
(took 0.014 seconds)
