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

Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Baidu Online Network Technology Co., Ltd.

Errui Ding

Hao Sun

Shilei Wen

Xiao Xi Liu

Xubin Li

Jian Wang

Junyu Han

Chengquan Zhang

Xiao Tan

Haifeng Wang

Hongwu Zhang

Dongliang He

Zhizhen Chi

Mingyu Chen

Yingze Bao

Fu Li

Le Kang

Zeyu Liu

Xin Li

Tian Wu

Chengyue Zhang

Xiang Zhao

Zhigang Wang

Jingtuo Liu

Xiang Long

Kun Yao

Jiaming Liu

Wei Zhang

Chao Li

Jingdong Wang

Feng Zhou

Tianwei Lin

Yipeng Sun

Fan Yang

Bin Zhang

Ping Wang

Chen Zhao

Yuan Feng

Pengcheng Yuan

Henan Zhang

Xiameng Qin

Jingwei Liu

Ruizhi Chen

Shumin Han

Xiaodi Wang

Ying Xin

Zhichao Zhou

Mengyi En

Shufei Lin

Yingbo Xu

Zipeng Lu

Zuming Huang

Yukang Ding

Yang Hu

Hui Shen

Qiang Li

Yan Zhang

Di Wang

Yuan Gao

Xiaoyan Wang

Shanshan Liu

Xiaoqing Ye

Yulin Li

Zhongqin Wu

Han Hu

Jianfeng Zhu

Ju Huang

Pengyuan Lv

Qunyi Xie

Jiang Tao Wang

Yongfeng Zhong

Fuguo Zhu

Xiongwei Zhu

Liang Wu

Minyue Jiang

Xipeng Yang

Xiangbo Su

Yuchen Yuan

Yafeng Deng

Yuqiao Teng

Yuxuan Luo

Xuecen Shen

Long Gong

Mingyuan Mao

Shichao Zhao

Mengjun Cheng

Longchao Wang

Qiman Wu

Yangliu Xu

Meina Qiao

Haojie Liu

Ti Bai

Zihan Ni

Zhiyong Jin

Jiangfan Deng

Yi Fu

Fujian Wang

Xiao Liu

The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

Character recognition model training method and apparatus, character recognition method and apparatus, device and storage medium

Provided are a joint perception model training method, a joint perception method, a device, and a storage medium. The joint perception model training method includes: acquiring sample images and perception tags of the sample images; acquiring a preset joint perception model, where the joint perception model includes a feature extraction network and a joint perception network; performing feature extraction on the sample images through the feature extraction network to obtain target sample features; performing joint perception through the joint perception network according to the target sample features to obtain perception prediction results; and training the preset joint perception model according to the perception prediction results and the perception tags, where the joint perception includes executing at least two perception tasks.

Joint perception model training method, joint perception method, device, and storage medium

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 and apparatus for generating a virtual character, an electronic device and a computer readable storage medium are provided. The method includes: performing mesh simplification on an initial model of a virtual character to obtain a mesh-simplified model; obtaining a first target model by performing white model mapping rendering on an area of each material type on the mesh-simplified model, and obtaining a second target model by performing hyper-realistic rendering on the area of each material type on the mesh-simplified model; and establishing a bidirectional mapping between the first target model and the second target model, and obtaining a target virtual character through iterative updating of the bidirectional mapping.

Method and apparatus for generating virtual character, electronic device and readable 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

Embodiments of the present disclosure provide a method and apparatus for performing a structured extraction on a text, a device and a storage medium. The method may include: performing a text detection on an entity text image to obtain a position and content of a text line of the entity text image; extracting multivariate information of the text line based on the position and the content of the text line; performing a feature fusion on the multivariate information of the text line to obtain a multimodal fusion feature of the text line; performing category and relationship reasoning based on the multimodal fusion feature of the text line to obtain a category and a relationship probability matrix of the text line; and constructing structured information of the entity text image based on the category and the relationship probability matrix of the text line.

Method and apparatus for performing structured extraction on text, device and storage medium

A method, an apparatus, device and a storage medium for generating a target re-recognition model are provided. The method may include: acquiring a set of labeled samples, a set of unlabeled samples and an initialization model obtained through supervised training; performing feature extraction on each sample in the set of the unlabeled samples by using the initialization model; clustering features extracted from the set of the unlabeled samples by using a clustering algorithm; assigning, for each sample in the set of the unlabeled samples, a pseudo label to the sample according to a cluster corresponding to the sample in a feature space; and mixing a set of samples with a pseudo label and the set of the labeled samples as a set of training samples, and performing supervised training on the initialization model to obtain a target re-recognition model.

Method and apparatus for generating target re-recognition model and re-recognizing target

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

Provided are an image processing method and apparatus, a device, and a storage medium, relating to the technical field of image processing, in particular to the artificial intelligence fields such as computer vision and deep learning. The specific implementation scheme is as follows: inputting a to-be-processed image into an encoding network to obtain a basic image feature, wherein the encoding network includes at least two cascaded overlapping encoding sub-networks which perform encoding and fusion processing on input data at at least two resolutions; and inputting the basic image feature into a decoding network to obtain a target image feature for pixel point classification, wherein the decoding network includes at least one cascaded overlapping decoding sub-network to perform decoding and fusion processing on input data at at least two resolutions respectively.

Image processing method and apparatus, 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 human body identification method, an electronic device and a storage medium, related to the technical field of artificial intelligence such as computer vision and deep learning, are provided. The method includes: inputting an image to be identified into a human body detection model, to obtain a plurality of preselected detection boxes; identifying a plurality of key points from each of the preselected detection boxes respectively according to a human body key point detection model, and obtaining a key point score of each of the key points; determining a target detection box from each of the preselected detection boxes, according to a number of the key points whose key point scores meet a key point threshold; and inputting the target detection box into a human body key point classification model, to obtain a human body identification result for the image to be identified.

Human body identification method, electronic device and storage medium

Embodiments of the present disclosure provide a method and apparatus for detecting a target object in an image. The method includes: performing following prediction operations using a pre-trained neural network: detecting a target object in a two-dimensional image to determine a two-dimensional bounding box of the target object; and determining a relative position constraint relationship between the two-dimensional bounding box of the target object and a three-dimensional projection bounding box obtained by projecting a three-dimensional bounding box of the target object into the two-dimensional image; and the method further including: determining the three-dimensional projection bounding box of the target object, based on the two-dimensional bounding box of the target object and the relative position constraint relationship between the two-dimensional bounding box of the target object and the three-dimensional projection bounding box.

Method and apparatus for detecting target object in image

A method for detecting a surface defect, a method for training model, an apparatus, a device, and a medium, are provided. The method includes: inputting a surface image of the article for detection into a defect detection model to perform a defect detection, and acquiring a defect detection result output by the defect detection model; inputting a surface image of a defective article determined to be defective into an image discrimination model based on the defect detection result to determine whether the surface image of the defective article is defective, wherein the image discrimination model is a trained generative adversarial networks model, and the generative adversarial networks model is obtained by training using a surface image of a defect-free good article; and adjusting the defect detection result of the surface image of the defective article according to a determination result of the image discrimination model.

Method for detecting surface defect, method for training model, apparatus, device, and media

The present application discloses a vehicle re-identification method and apparatus, a device and a storage medium, which relates to the field of computer vision, intelligent search, deep learning and intelligent transportation. The specific implementation scheme is: receiving a re-identification request from a terminal device, the re-identification request including a first image of a first vehicle shot by a first camera and information of the first camera; acquiring a first feature of the first vehicle and a first head orientation of the first vehicle according to the first image; determining a second image of the first vehicle from images of multiple vehicles according to the first feature, multiple second features extracted based on the images of the multiple vehicles in an image database, the first head orientation of the first vehicle, and the information of the first camera; and transmitting the second image to the terminal device.

Vehicle re-identification method, apparatus, device and storage medium

A method includes screening, by a video-clip screening module in a video description model, a plurality of video proposal clips acquired from a video to be analyzed, to acquire a plurality of video clips suitable for description. The plural video proposal clips acquired from the video to be analyzed may be screened by the video-clip screening module to acquire the plural video clips suitable for description; and then, each video clip is described by a video-clip describing module, thus avoiding description of all the video proposal clips, only describing the screened video clips which have strong correlation with the video and are suitable for description, removing the interference of the description of the video clips which are not suitable for description in the description of the video, guaranteeing the accuracy of the final descriptions of the video clips, and improving the quality of the descriptions of the video clips.

Method and apparatus for detecting temporal action of video, electronic device and storage medium

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Beijing, China

Errui Ding