Beijing Baidu Netcom Science and Technology Co., Ltd.

Baidu Online Network Technology (beijing) Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Jingtuo Liu

Junyu Han

Kun Yao

Errui Ding

Yipeng Sun

Haifeng Wang

Gang Zhang

Tao He

Chengquan Zhang

Zhizhi Guo

Chen Zhao

Di Wang

Guang Cheng Li

Tian Wu

Jingdong Wang

Jiawei Gu

Zhibin Hong

Li Xu

Xiameng Qin

Jiabin He

Ruizhi Chen

Tianshu Hu

Ju Huang

Pengyuan Lv

Qunyi Xie

Yuanzhang Chang

Fuguo Zhu

Jianping Hao

Mengyi En

Renliang Weng

Xiongwei Zhu

Shengxian Zhu

Shihu Li

Xiangda Yan

Mengjun Cheng

Longchao Wang

Zihan Ni

Wenxian Liu

Shasha Yan

Zhengping Wei

A pre-training method for a Vision and Scene Text Aggregation model includes: acquiring a sample image-text pair; extracting a sample scene text from a sample image; inputting a sample text into a text encoding network to obtain a sample text feature; inputting the sample image and an initial sample aggregation feature into a visual encoding subnetwork and inputting the initial sample aggregation feature and the sample scene text into a scene encoding subnetwork to obtain a global image feature of the sample image and a learned sample aggregation feature; and pre-training the Vision and Scene Text Aggregation model according to the sample text feature, the global image feature of the sample image, and the learned sample aggregation feature.

Pre-training method, image and text retrieval method for a vision and scene text aggregation model, electronic device, and storage medium

A method for training a three-dimensional face reconstruction model includes inputting an acquired sample face image into a three-dimensional face reconstruction model to obtain a coordinate transformation parameter and a face parameter of the sample face image; determining the three-dimensional stylized face image of the sample face image according to the face parameter of the sample face image and the acquired stylized face map of the sample face image; transforming the three-dimensional stylized face image of the sample face image into a camera coordinate system based on the coordinate transformation parameter, and rendering the transformed three-dimensional stylized face image to obtain a rendered map; and training the three-dimensional face reconstruction model according to the rendered map and the stylized face map of the sample face image.

Method and apparatus for training a three-dimensional face reconstruction model and method and apparatus for generating a three-dimensional face image

The present disclosure discloses a method and apparatus for generating an image. A specific embodiment of the method comprises: acquiring at least two frames of facial images extracted from a target video; and inputting the at least two frames of facial images into a pre-trained generative model to generate a single facial image. The generative model updates a model parameter using a loss function in a training process, and the loss function is determined based on a probability of the single facial generative image being a real facial image and a similarity between the single facial generative image and a standard facial image. According to this embodiment, authenticity of the single facial image generated by the generative model may be enhanced, and then a quality of a facial image obtained based on the video is improved.

Method and apparatus for generating image

The disclosure provides a method and a device for processing information, an electronic device, and a storage medium, belonging to a field of artificial intelligence including computer vision, deep learning, and natural language processing. In the method, the computing device recognizes multiple text items in the image. The computing device classifies multiple text items into a first set of name text items and a second set of content text items based on semantics of the text items. The computing device performs a matching operation between the first set and the second set based on a layout of the text items in the image, and determines matched name-content text items. The matched name-content text items include a name text item in the first set and a content text item matching the name text item and in the second set. The computing device outputs the matched name-content text items.

Method and device for processing information, electronic device, and storage medium

A method and apparatus for processing an image, a device and a storage medium are provided. An implementation of the method includes: acquiring a template image, the template image including at least one region of interest; determining a first feature map corresponding to each region of interest in the template image; acquiring a target image; determining a second feature map of the target image; and determining at least one region of interest in the target image according to the first feature map and the second feature map.

Method and apparatus for processing image, device and storage medium

A text recognition method includes: acquiring an image including text information, the text information including M characters, M being a positive integer greater than 1; performing text recognition on the image to acquire character information about the M characters; recognizing reading direction information about each character in accordance with the character information about the M characters, the reading direction information being used to indicate a next character corresponding to a current character in a semantic reading order; and ranking the M characters in accordance with the reading direction information about the M characters to acquire a text recognition result of the text information.

Text recognition method and device, and electronic device

The present application discloses an image recognition method, apparatus, device, and a computer storage medium, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. The method includes: performing organ recognition on a human face image and marking positions of the human facial five sense organs in the human face image, obtaining a marked human face image; inputting the marked human face image into a backbone network model and performing feature extraction, obtaining defect features of the marked human face image outputted by different convolutional neural network levels of the backbone network model; and fusing the defect features of different levels that are located in a same area of the human face image, obtaining a defect recognition result of the human face image.

Image recognition method, apparatus, device, and computer storage medium

An image recognition method is provided, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. An implementation includes: performing five-sense-organ recognition on a preprocessed human face image and marking positions of the human facial five sense organs in the human face image, to obtain the marked human face image; determining human face images at multiple scales of the marked human face image, inputting the human face images of multiple scales into a backbone network model, and performing feature extraction, to obtain a wrinkle feature of the human face image at each of the multiple scales; and fusing the wrinkle feature at each scale that is located in a same area of the human face image, to obtain a wrinkle recognition result of the human face image.

Image recognition method and apparatus, device, and computer storage medium

A method, an electronic device and a storage medium for expression driving are disclosed. The method may include: performing facial key point detection on a driven character in a first image to obtain a first facial key point sequence; performing the following processing for each second image of a plurality of second images obtained successively: performing facial key point detection on a driving character in the second image to obtain a second facial key point sequence; obtaining a difference between the second facial key point sequence and an expressionless key point sequence which has been determined previously according to an analysis on the second facial key point sequence for a previous second image, and performing expression drive rendering on the driven character based on the difference and the first facial key point sequence. The technical solution may enhance flexibility, interactivity, accuracy etc.

Method, apparatus, electronic device and storage medium for expression driving

The present disclosure provides a method and apparatus for generating training data for human face recognition, a device and a computer storage medium, wherein the method comprises: inputting accessory-not-worn face images into a generative network, to obtain accessory-worn face images; using the accessory-worn face images as second training data for building the human face recognition model; wherein the generative network is a generative network in a generative adversarial network obtained by pre-training with first training data, the first training data including the accessory-not-worn face images and accessory-worn face images corresponding to a same user identifier. In the present disclosure, the accessory-worn face images obtained in a data augmentation manner greatly expand the amount of training data for building the human face recognition model, and thereby improve the recognition accuracy of the accessory-worn face images.

Method and apparatus for generating training data for human face recognition, device and computer storage medium

A method and an apparatus for generating an image are provided. A specific embodiment of the method comprises: acquiring a to-be-processed facial image, the image resolution of the to-be-processed facial image being lower than a preset first resolution threshold; and inputting the to-be-processed facial image into a pre-trained generativemodel to generate a processed facial image. The generative model updates a model parameter using a loss function in a training process, and the loss function is determined based on a probability of an image group being positive sample data, the image group composed of a facial sample image and a facial generative image. According to this embodiment, authenticity of the generated facial image is enhanced.

The present disclosure discloses a method and apparatus for human face image processing. A specific embodiment of the method comprises: locating facial feature points in a human face image, extracting an image of a human face region according to a range defined by the facial feature points, transforming a facial image of the source image according to a face shape of the target image, transforming the facial image of the source image according to a complexion distribution of a facial region of the target image, and obtaining a new human face by combining the facial image of the source image and a facial image of the target image. The embodiment achieves a facial image processing with higher similarity to the user in the image, of simple steps, small calculation and high real-time performance.

Method and apparatus for human face image processing

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Beijing, China

Jingtuo Liu