Suzhou Metabrain Intelligent Technology Co., Ltd.

Inspur Suzhou Intelligent Technology Co., Ltd

Baoyu Fan

Yaqian Zhao

Rengang Li

Li Wang

Zhenhua Guo

Xiaochuan Li

Runze Zhang

Xiangyi Deng

A natural language processing method and apparatus, a device, and a readable storage medium, where the method includes: obtaining a target sentence to be processed, and determining each entity in the target sentence (S); for each entity in the target sentence, in response to the entity being present in a preset entity set, determining extended information for the entity, and adding the determined extended information after a location of the entity in the target sentence, to obtain an updated target sentence (S); and inputting the updated target sentence to a bidirectional encoder representations from transformer (BERT) model, such that the BERT model performs a natural language processing task, where in a process in which the BERT model performs the natural language processing task, an attention score between extended information of any entity in the target sentence and another entity in the target sentence is adjusted to zero (S).

Natural language processing method and apparatus, device, and readable storage medium using a three dimensional tensor to determine a relation between entities to aid in a question-answering task

The present application relates to the technical field of artificial intelligence, and discloses a visual positioning method and apparatus, a device, and a medium. The method includes: performing feature splicing on an image encoding feature and a text encoding feature; performing feature fusion on spliced encoding features to obtain a first fused encoding feature; performing noise correction on the first fused encoding feature and the text encoding feature on the basis of a preset cross-attention mechanism to obtain a corrected fused feature and a corrected text encoding feature, and performing feature fusion on the spliced encoding feature and the corrected text encoding feature to obtain a second fused encoding feature; and correcting a preset frame feature using a target encoding feature on the basis of the corrected fused feature and the second fused encoding feature to predict a regional position coordinate of a target visual object.

Visual positioning method and apparatus, device, and medium

Embodiments of the present application disclose a natural language processing method and apparatus, a device, and a readable storage medium. The method includes: obtaining a target sentence to be processed, and determining each first entity in the target sentence; for each first entity, in response to the first entity being present in a preset entity set, determining, in the preset entity set, a second entity in maximum correlation with the first entity, generating extended information based on the determined second entity, and adding the extended information after a location of the first entity in the target sentence, to obtain an updated target sentence, where the second entity is any entity in the preset entity set other than the first entity; and inputting the updated target sentence to a bidirectional encoder representations from transformer (BERT) model, such that the BERT model performs a natural language processing task.

Natural language processing method and apparatus, device, and readable storage medium

Disclosed in the present disclosure are an image-text mutual retrieval model training method and apparatus, an image-text mutual retrieval method, and a device, applied to the technical field of retrieval. The image-text mutual retrieval model training method includes: acquiring training data pairs; inputting the training data pair in an initial model, and extracting text coding features of text training data and image coding features of image training data by using a text coding module and an image coding module in the initial model, respectively; calculating a training loss based on the text coding features and the image coding features, and performing parameter adjustment on the initial model based on the training loss; and in response to the training loss meeting a convergence condition, determining the initial model after the parameter adjustment as an image-text mutual retrieval model.

Method and apparatus for training an image-text mutual retrieval model, image-text mutual retrieval method, and device

The present application discloses a person re-identification method, system and device, and a computer-readable storage medium. The method includes: obtaining a first type of person image without a label; making label information for the first type of person image; training a target person re-identification network on the basis of the first type of person image and the label information to obtain a first trained target person re-identification network; extracting a region of interest in the first type of person image; and training, on the basis of the first type of person image and the region of interest, the first trained target person re-identification network to obtain a second trained target person re-identification network, and performing person re-identification on the basis of the target person re-identification network.

Person re-identification method, system and device, and computer-readable storage medium

Provided in the present application are a disk processing method and system, and an electronic device. The method comprises: when disk alarm information is detected, marking a corresponding alarm disk as a faulty disk; detecting the state of a disk group corresponding to the faulty disk; if the state of the disk group is degraded, marking the faulty disk as an isolation disk, and generating alarm information; if the state of the disk group is healthy, determining whether there is a redundant disk group; if there is no redundant disk group, operating the faulty disk according to a first preset rule, and generating alarm information; and if there is a redundant disk group, detecting the state of the redundant disk group, if the state of the redundant disk group is healthy, marking the faulty disk as the isolation disk, and generating alarm information, otherwise, operating the faulty disk according to a second preset rule, and generating alarm information.

Pedestrian re-identification method, system and device, and computer-readable storage medium

A difference description statement generation method includes: performing feature concatenation on an image encoding feature and a text encoding feature; inputting a concatenated encoding feature to a preset image-text alignment unit constructed based on a preset self-attention mechanism to obtain a concatenated alignment feature; processing, by using a preset noise monitoring unit constructed based on the preset self-attention mechanism and a preset cross-attention mechanism, the text encoding feature, and an image alignment feature and a text alignment feature that are obtained by splitting the concatenated alignment feature respectively, to extract a difference signal; and generating a difference description statement based on the difference signal by using a preset difference description generation algorithm.

Difference description statement generation method and apparatus, device and medium

The present application discloses an image recognition method and apparatus, and a device and a readable storage medium. The method includes: obtaining a target image to be recognized; inputting the target image into a trained feature extraction model for feature extraction to obtain image features; and using the image features to recognize the target image to obtain a recognition result. In the present application, under the condition that the parameter quantity and the calculation amount of the feature extraction model do not need to be increased, the feature extraction model has relatively good feature extraction performance by means of isomorphic branch extraction and feature mutual mining, namely knowledge collaboration assistive training, and more accurate image recognition may be completed on the basis of the image features extracted by the feature extraction model.

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

A person re-identification method and apparatus, a device, and a readable storage medium. A homogeneous training network of an initial person re-identification network is trained by means of an objective function such as a knowledge synergy for dynamic classification probability loss function to obtain a final person re-identification network carrying more accurate final weight parameters, and a person re-identification task is performed by means of the final person re-identification network. In this way, the accuracy and performance of the person re-identification network to process the person re-identification task may be improved, the storage space in a device may be reduced, more beneficial to the storage and deployment of the portable device, and the amount of calculation of performing the person re-identification task may be reduced, thereby increasing the processing rate of the person re-identification task.

Person re-identification method and apparatus, device, and readable storage medium

The present application discloses a person re-identification method and apparatus based on a deep learning network, a device, and a medium. The method includes: obtaining an initial person re-identification network; creating a homogeneous training network corresponding to the initial person re-identification network, where the homogeneous training network comprises a plurality of homogeneous branches with a same network structure; training the homogeneous training network by using a target loss function, and determining a final weight parameter of each network layer in the homogeneous training network; and loading the final weight parameter by using the initial person re-identification network to obtain a final person re-identification network, to perform a person re-identification task by using the final person re-identification network.

Person re-identification method and apparatus based on deep learning network, device, and medium

A method includes: setting, in a main training network, an auxiliary training network having the same architecture as the main training network, performing data enhancement on an original image to obtain an enhanced image; inputting the original image into the main training network, inputting the enhanced image into the auxiliary training network; determining whether an intersection-over-union value of a second prediction frame generated by the auxiliary training network and a target frame is greater than an intersection-over-union value of a first prediction frame generated by the main training network and the target frame; and in response to the intersection-over-union value of the second prediction frame and the target frame being greater than the intersection-over-union value of the first prediction frame and the target frame, replacing the intersection-over-union value of the first prediction frame and the target frame with the intersection-over-union value of the second prediction frame and the target frame.

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Jiangsu, China

Baoyu Fan