Abstract: Provided are a multi-modal model training method, apparatus and device, and a storage medium. The method includes the following steps: obtaining a training sample set, and training a multi-modal model for a plurality of rounds by successively using each of training sample pair in the training sample set: during use of any one of the training sample pairs for training, obtaining an image feature of a target visual sample firstly, and then determining whether back translation needs to be performed on a target original text; when back translation needs to be performed on the target original text, performing corresponding back translation to obtain a target back-translated text, and obtaining a text feature of the target back-translated text; and training the multi-modal model based on the image feature and the text feature.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 f

Abstract: 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 f

Abstract: An instruction execution method, apparatus and device, and a storage medium are provided. According to the method, normal execution of the instructions without an execution dependency relationship in the batch processing process is ensured, and meanwhile, ordered execution of the instructions with the execution dependency relationship is ensured, and then the correctness of instruction execution results is ensured. In addition, the present application further provides an instruction execution apparatus and device and a storage medium, and the beneficial effects are the same as described above.

Abstract: A computing method and apparatus for a convolutional neural network model. The method comprises: acquiring a computing model of a training task of a convolutional neural network model (S101); then splitting multiply-accumulate operation in a computing model of a training task of the convolutional neural network model into a plurality of multiply-add operation tasks (S102); confirming a computing device corresponding to each multiply-add operation task according to the correlation between a preset computing model and the computing device (S103); and finally, respectively computing each multiply-add operation task by utilizing the computing device corresponding to each multiply-add operation task (S104). The purposes of improving the flexibility of migration of a CNN model training task on different computing devices or cooperative computing of different processors and improving the computing speed are achieved.