Beijing Baidu Netcom Science Technology Co., Ltd

Beijing Jingdong Century Trading Co., Ltd.

Beijing Wodong Tianjun Information Technology Co., Ltd.

Qiwen Liu

Dianhai Yu

Yanjun Ma

Xiaoguang Hu

Yuning Du

Ruoyu Guo

Shengyu Wei

Ran Bi

Qiao Zhao

Ying Zhou

Cheng Cui

Tingquan Gao

Xueying Lyu

Shuilong Dong

Bin Lu

Yi Liu

Baohua Lai

Chenxia Li

Weiwei Liu

Chunwei Yan

Qingqing Dang

Bingcheng Yang

Sensen He

Guanzhong Wang

Shao Zeng

Haoshuang Wang

Juncai Peng

Guanghua Yu

Yujuan Cheng

Can Wen

Xiaoting Yin

A method of processing data for a target model, an electronic device, and a storage medium, which relate to a field of deep learning. The method of processing data for a target model includes: acquiring the target model, wherein the target model includes at least one network layer, and each network layer of the at least one network layer includes a plurality of model parameters; for a target network layer in the at least one network layer, dividing the plurality of model parameters of the target network layer into a plurality of groups; and adjusting a data type of the model parameters in the plurality of groups from a first data type to a second data type respectively, so as to obtain a processed target model.

Method of processing data for target model, 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

A product management method and apparatus, a computer readable storage medium, and an electronic device, relating to the technical fields of computers and communications. The method comprises: acquiring, by means of a warehouse management system or a smart shelf, a product quantity in a display area and a maximum product display quantity in the display area, so as to obtain a product shortage quantity in the display area; acquiring, by means of the warehouse management system or the smart shelf, a product stock quantity in a warehouse; acquiring a product replenishment quantity according to the product shortage quantity in the display area and the product stock quantity in the warehouse; sending the product replenishment quantity to a personal digital assistant; and receiving stock keeping unit information input by a worker by means of the personal digital assistant, so as to acquire goods location information of the product, so that the worker completes the replenishment of the product according to the goods location information and the product replenishment quantity, wherein the product replenishment quantity is less than or equal to the product stock quantity in the warehouse.

Product management method and related device

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

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

A method for detecting an object in an image includes: obtaining an image to be detected; generating a plurality of feature maps based on the image to be detected by a plurality of feature extracting networks in a neural network model trained for object detection, in which the plurality of feature extracting networks are connected sequentially, and input data of a latter feature extracting network in the plurality of feature extracting networks is based on output data and input data of a previous feature extracting network; and generating an object detection result based on the plurality of feature maps by an object detecting network in the neural network model.

Method and device for detecting an object in an image

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Beijing, China

Qiwen Liu