Suzhou Metabrain Intelligent Technology Co., Ltd.

Inspur Suzhou Intelligent Technology Co., Ltd

Zhenhua Guo

Rengang Li

Baoyu Fan

Yaqian Zhao

Li Wang

Xiaochuan Li

Fang Cao

Liang Jin

Nan Wu

Runze Zhang

Zhiyong Qiu

Xiangyi Deng

Disclosed are a person Re-identification (Re-ID) method, system, and device, and a computer-readable storage medium. The method includes: acquiring a sample set to be trained (S); training a pre-constructed person Re-ID model by data re-sampling and cross-validation methods based on the sample set to be trained to obtain a trained person Re-ID model (S); and performing person Re-ID based on the trained person Re-ID model, wherein persons in any two groups are of different classes after the sample set to be trained is grouped according to the cross-validation method (S).

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

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

Disclosed is an image segmentation method, including: obtaining an original image set; performing feature extraction on the original image set by using a backbone network to obtain a feature map set; performing channel extraction fusion processing on the feature map set by using a channel extraction fusion model to obtain an enhanced feature map set; and segmenting the enhanced feature map set by using a preset convolutional neural network to obtain an image segmentation result. In addition, the present application also provides an image segmentation system and device, and a readable storage medium, which have the beneficial effects above.

Image segmentation method, system, and device, and 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

An nGraph-based graphics processing unit (GPU) backend distributed training method and system, a computer-readable storage medium, and an electronic device. The method includes: receiving a training request, and obtaining corresponding training data; obtaining a Nvidia® Collective multi-GPU Communication Library (NCCL) file by means of a system path of the NCCL file linked to an nGraph framework; invoking an NCCL communication interface configuration according to the training request to obtain a training model, the NCCL communication interface is an NCCL file-based communication operation interface located at a GPU backend of the nGraph framework; and performing GPU backend training on the training data using the training model. The present application can satisfy an urgent need of a user for performing neural network distributed training on the basis of an nGraph GPU backend, thus further improving the performance of deep learning network training.

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Jiangsu, China

Zhenhua Guo