Beijing Baidu Netcom Science Technology Co., Ltd

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

Beijing Baidu Netcom Science and Technology Co., Ltd.

Yuning Du

Yanjun Ma

Dianhai Yu

Qiwen Liu

Xiaoguang Hu

Ruoyu Guo

Shengyu Wei

Ran Bi

Chenxia Li

Qiao Zhao

Ying Zhou

Guoyi Liu

Cheng Cui

Tingquan Gao

Xueying Lyu

Shuilong Dong

Guang Cheng Li

Baohua Lai

Bin Lu

Yi Liu

Tian Wu

Shanshan Liu

Shufu Xie

Shumin Han

Weiwei Liu

Xianglong Meng

Kai Wei

Sensen He

Shao Zeng

Yehua Yang

Canxiang Yan

Xiaoting Yin

Provided is a lightweight model training method, an image processing method, a device and a medium. The lightweight model training method includes: acquiring first and second augmentation probabilities and a target weight adopted in an e-th iteration; performing data augmentation on a data set based on the first and second augmentation probabilities respectively, to obtain first and second data sets; obtaining a first output value of a student model and a second output value of a teacher model based on the first data set; obtaining a third output value and a fourth output value based on the second data set; determining a distillation loss function, a truth-value loss function and a target loss function; training the student model based on the target loss function; and determining a first augmentation probability or target weight to be adopted in an (e+1)-th iteration in a case of e is less than E.

Lightweight model training method, image processing method, electronic device, and storage medium

The present disclosure provides a method for training a feature extraction model, a method for classifying an image and related apparatuses, and relates to the field of artificial intelligence technology such as deep learning and image recognition. The scheme comprises: extracting an image feature of each sample image in a sample image set using a basic feature extraction module of an initial feature extraction model, to obtain an initial feature vector set; performing normalization processing on each initial feature vector in the initial feature vector set using a normalization processing module of the initial feature extraction model, to obtain each normalized feature vector; and guiding training for the initial feature extraction model through a preset high discriminative loss function, to obtain a target feature extraction model as a training result.

Method for training feature extraction model, method for classifying image, and related apparatuses

The present disclosure provides a vision processing and model training method, device, storage medium and program product. A specific implementation solution is as follows: establishing an image classification network with the same backbone network as the vision model, performing a self-monitoring training on the image classification network by using an unlabeled first data set; initializing a weight of a backbone network of the vision model according to a weight of a backbone network of the trained image classification network to obtain a pre-training model, the structure of the pre-training model being consistent with that of the vision model, and optimize the weight of the backbone network by using real data set in a current computer vision task scenario, so as to be more suitable for the current computer vision task; then, training the pre-training model by using a labeled second data set to obtain a trained vision model.

Vision processing and model training method, device, storage medium and program product

Provided are an image processing method and apparatus, a device, a medium and a program product. The image processing method includes: performing image augmentation on an original image to obtain at least one augmented image; performing subject detection on the original image and the at least one augmented image to obtain an original detection frame in the original image and an augmented detection frame in the at least one augmented image; determining whether the original detection frame and the augmented detection frame belong to the same subject; and in response to the original detection frame and the augmented detection frame belonging to the same subject, determining a target subject frame in the original image according to the augmented detection frame.

Image processing method and apparatus, device, medium and program product

An imaging exposure control method and apparatus, a device and a storage medium, which relate to the field of artificial intelligence technologies, such as machine learning technologies and intelligent imaging technologies, are disclosed. An implementation includes performing semantic segmentation on a preformed image to obtain semantic segmentation images of at least two semantic regions; estimating an exposure duration of each semantic region based on the semantic segmentation image and the preformed image; and controlling exposure of each semantic region during imaging based on the exposure duration of each semantic region.

Imaging exposure control method and apparatus, device and storage medium

There is provided a method and apparatus of training a model, a device, and a medium, which relate to artificial intelligence, and in particular to a deep learning and image processing technology. The method may include: determining a plurality of augmented sample sets associated with a plurality of original samples; determining a first constraint according to a first model based on the plurality of augmented sample sets; determining a second constraint according to the first model and a second model based on the plurality of augmented sample sets, wherein the second constraint is associated with a difference between outputs of the first model and the second model for one augmented sample, and the first model has a complexity lower than that of the second model; training the first model based on at least the first constraint and the second constraint, so as to obtain a trained first model.

Method and apparatus of training model, device, medium, and program product

The present disclosure provides an image recognition method and apparatus, an electronic device and a readable storage medium, and relates to the field of artificial intelligence technologies, such as image processing and deep learning technologies. The image recognition method includes: acquiring a to-be-recognized image, and determining a to-be-recognized subject in the to-be-recognized image; extracting a subject feature of the to-be-recognized subject, and obtaining a target feature according to the subject feature; determining a target candidate feature in a plurality of candidate features using the target feature; and taking a class corresponding to the target candidate feature as a recognition result of the to-be-recognized subject. With the present disclosure, different image recognition requirements may be met, and a speed and accuracy of image recognition may be improved.

Image recognition method and apparatus, electronic device and readable storage medium

A method and apparatus is provided for generating and applying a deep learning model based on a deep learning framework, and relates to the field of computers. A specific implementation solution includes that a basic operating environment is established on a target device, where the basic operating environment is used for providing environment preparation for an overall generation process of a deep learning model; a basic function of the deep learning model is generated in the basic operating environment according to at least one of a service requirement and a hardware requirement, to obtain a first processing result; an extended function of the deep learning model is generated in the basic operating environment based on the first processing result, to obtain a second processing result; and a preset test script is used to perform function test on the second processing result, to output a test result.

Method and apparatus for generating and applying deep learning model based on deep learning framework

The present disclosure provides a method for generating a backbone network, an apparatus for generating a backbone network, a device, and a storage medium. The method includes: acquiring a set of a training image, a set of an inference image, and a set of an initial backbone network; training and inferring, for each initial backbone network in the set of the initial backbone network, the initial backbone network by using the set of the training image and the set of the inference image, to obtain an inference time and an inference accuracy of a trained backbone network in an inference process; determining a basic backbone network based on the inference time and the inference accuracy of the trained backbone network in the inference process; and obtaining a target backbone network based on the basic backbone network and a preset target network.

Method for generating backbone network, apparatus for generating backbone network, device, and storage medium

Embodiments of the present disclosure provide a method and device for labelling a point of interest, a computer device, and a storage medium. The method includes the following. Image data to be labelled is obtained. The image data includes an image to be labelled and a collection location of the image to be labelled. Feature extraction is performed on the image to be labelled to obtain a first image feature of the image to be labelled. A first reference image corresponding to the image to be labelled is determined based on a similarity between the first image feature and a second image feature corresponding to each reference image in an image library. The point of interest of the image to be labelled is labelled based on a category of the first reference image and the collection location of the image to be labelled.

Method and device for labeling point of interest

The present disclosure provides a watermark image processing method and apparatus, a device and a computer readable storage medium. In the embodiments of the present disclosure, it is feasible to obtain at least one similar image approximate to the watermark image according to the watermark image including the watermark, and obtain a replaceable image of each similar image of said at least one similar image in the watermark area, according to a watermark area where the watermark is located in the watermark image so that it is possible to obtain a carrier image not including the watermark, according to the watermark image and the replaceable image of said each similar image in the watermark area. Since the replaceable image of the similar image in the watermark area is employed to obtain the carrier image not including the watermark, the valid content in the watermark image covered by the watermark is restored and thereby the reliability of the image is improved.

Watermark image processing method and apparatus, device and computer readable storage medium

The present disclosure provides a method and for acquiring text data of a trademark image, a computer device and a non-transitory computer readable storage medium. The method includes the followings. A trademark database including one or more mappings among trademark feature information, trademark description information and trademark text information is established. A to-be-processed image including image description information is acquired. Trademark feature information corresponding to the to-be-processed image is determined. The trademark text information corresponding to the trademark feature information as the text data of the trademark image corresponding to the to-be-processed image according to the one or more mappings in the trademark database when the trademark description information corresponding to the trademark feature information corresponding to the to-be-processed image is contained in the image description information corresponding to the to-be-processed image.

Method and device for acquiring text data from trademark image, computer device and storage medium

A method for recognizing a picture, a method and an apparatus for labelling a picture, and a storage medium. The method for recognizing a picture comprises: acquiring a clothes picture labelled with a positioning key point and a clothes wearing region (S); extracting, based on the locations of the clothes wearing region and the positioning key point, at least one picture region representing a feature region from the clothes picture (S); and respectively acquiring feature information representing the feature region based on a region feature recognition model corresponding to the feature region (S). By labelling the positioning key point, a picture representing a clothes content in the clothes picture is positioned, so that the interference of a background content in the clothes picture is reduced. On this basis, a picture region representing a feature region is extracted from a clothes picture based on a clothes wearing region and a labelled positioning key point, and then feature information on the feature region is acquired based on a corresponding region feature recognition model, thereby enhancing the accuracy and precision of recognizing the feature information on clothes in the clothes picture.

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Beijing, China

Yuning Du