Abstract: An audio device and working method thereof, and a VR device are provided, and pertain to the field of space audio technologies. The audio device includes: a sound chamber; at least one speaker located within the sound chamber; an acquisition unit, configured to respectively obtain audio data and a target ear spectral curve HRTF at a position of a virtual sound source corresponding to the audio data; a computing unit, configured to process the audio data based on the target HRTF, generate a target sound signal, and output the target sound signal to the speaker. The technical solutions of the present disclosure can simulate three-dimensional sound production in space and create a sense of spatial immersion.

Abstract: The embodiments of the present disclosure relate to the technical field of computers. Disclosed are a method and apparatus for information fusion, a method and apparatus for data communication, and an electronic device and a non-transitory computer-readable storage medium. The method for information fusion includes: in response to that a communication triggering condition is met, acquiring a local parameter of each of workers in a distributed training system, where the communication triggering condition is that all key nodes complete tasks of the current round of training; selecting N key nodes participating in the next round of training, and fusing local parameters of the N key nodes to obtain a global parameter; and sending the global parameter to each of the workers, and sending a training command to the key nodes to the key nodes execute tasks of next round of training based on the global parameter.

Abstract: A text error correction method and apparatus, and an electronic device and a medium. The text error correction method includes: performing image encoding on an acquired image to be analyzed, so as to obtain image features (S101); performing text encoding on acquired noisy text, so as to obtain text features (S102); performing feature comparison on the image features and the text features according to a set attention mechanism, so as to obtain an error correction signal (S103); and predicting an initial text label according to the error correction signal by using a trained decoder, so as to obtain error-corrected text information (S104).

Abstract: An image generating method and apparatus, and a device and a medium are disclosed. The method comprises: acquiring weakly correlated image-text data pairs, and creating an image-text data set according to the weakly correlated image-text data pairs, wherein the weakly correlated image-text data pairs are image-text data pairs in which images and texts have weak correlations (S11); training, by using the image-text data set, an image generation model which is preconstructed on the basis of an adversarial network, so as to obtain a trained image generation model, wherein the image generation model includes a generator for generating an image, and a discriminator for identifying the authenticity of the image and calculating a corresponding loss value (S12); and after when text data to be processed has been acquired, generating, by using the trained image generation model, an image corresponding to the said text data (S13).

Abstract: A method and apparatus for reconstructing semantic instance, a device and medium, which relate to the field of three-dimensional vision. The method comprises: processing an original image by using a first target detection network to obtain first feature information of a target object, and processing a three-dimensional point cloud by using a second target detection network to obtain second feature information; predicting a first coarse point cloud on the basis of the first feature information, and predicting a three-dimensional detection result on the basis of the first feature information and the second feature information, so as to obtain a second coarse point cloud; and obtaining an initial point cloud of the target object on the basis of the first coarse point cloud and the second coarse point cloud, and processing the initial point cloud by using preset shape generation network to obtain semantic instance reconstruction result of the target object.

Abstract: The person intention reasoning method includes: performing object detection on a to-be-reasoned image to obtain an object detection result; determining that an image portion corresponding to a detection bounding box of each person in the to-be-reasoned image is a to-be-reasoned sub-image of the corresponding person respectively, and acquiring a joint feature and an occlusion probability of a joint of the corresponding person; performing prediction and analysis on the joint feature of corresponding joint based on the occlusion probability to obtain a corresponding prediction feature, and performing correction based on the joint feature and the prediction feature of the joint of the corresponding person to obtain a corresponding correction feature; and performing person intention reasoning by using the object detection result and the correction feature of the joint of the corresponding person to obtain a corresponding person intention reasoning result.

Abstract: 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.

Abstract: An image processing method is provided. Since in the present invention, the first quantization threshold obtained by the saturated mapping method and the second quantization threshold obtained by the unsaturated mapping method are weighted, it is equivalent to fusing two quantization threshold values. The obtained optimal quantization threshold can be applied to most activations, to more effectively retain the effective information of the activations and use in subsequent image processing, thereby improving the accuracy of inference computations of the quantified deep neural network on low-bit-width hardware platforms. An image processing device and apparatus have the same beneficial effects as the above image processing method.

Abstract: 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.

Abstract: A data synchronization method and apparatus, and a device and a storage medium are disclosed.

Abstract: 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.

Abstract: A method for recognizing emotion, a training method, apparatuses, a device, a storage medium and a product. The method for recognizing emotion includes: acquiring to-be-recognized spiking sequences corresponding to video information; and recognizing the to-be-recognized spiking sequences by using a spiking neural network emotion recognition model, so as to obtain a corresponding emotion category.

Abstract: An image restoration method includes: acquiring an original low-light image to be restored; acquiring a pre-trained image processing model; and inputting the original low-light image into the image processing model, so that an optical feature extraction network in the image processing model extracts an illumination feature from the original low-light image, and an image feature extraction network extracts a target image feature from the original low-light image, and generating a target bright image based on the illumination feature and the target image feature. The optical feature extraction network and the image feature extraction network are respectively used to process an image feature of an original low-light image, so as to obtain an illumination feature and a target image feature, and the illumination feature is then fused with the target image feature for image restoration to obtain a bright image.

Abstract: 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 (S101); 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 (S102); 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 (S103).

Abstract: 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.

Abstract: 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.

Abstract: A method and apparatus for allocating a computing task of a neural network in heterogeneous resources, a computer device, and a storage medium. The method includes: acquiring task information of the computing task of the neural network and resource information of the heterogeneous resources; determining, according to the task information and the resource information, an allocation mode for allocating each subtask to the heterogeneous resources for execution and a task processing cost corresponding to each allocation mode; constructing a directed acyclic graph according to each allocation mode and each task processing cost; obtaining a value of a loss function corresponding to each allocation path according to the task processing cost corresponding to each subtask in an allocation path of the directed acyclic graph; and selecting a target allocation path according to the value of each loss function.

Abstract: A high-resolution image matching method and system are provided. The method includes performing regional fidelity down-sampling on an initial high-resolution image to obtain a multi-level low-resolution image, performing local matching on the obtained multi-level low-resolution images using a method with global probes to obtain a matching result of the low-resolution images, and performing reverse refinement on the obtained matching result of the low-resolution image using overall consistency of the image matching, to obtain the matching results of the high-resolution images at all levels until the matching results of the initial resolution images are obtained, so as to reduce the computational complexity of the image matching process and improve the accuracy of the matching result, and then the matching result of the high-resolution image is obtained through reverse refinement based on the overall consistency.

Abstract: The image processing apparatus comprises at least a first arithmetic unit and a second arithmetic unit. The first arithmetic unit and the second arithmetic unit are cascaded by means of a serial transceiver, and are configured to execute the image processing method in parallel.

Abstract: A neural network compression method whereby forward inference is performed on target data by using a target parameter sharing network to obtain an output feature map of the last convolutional module, a channel related feature is extracted from the output feature map, the extracted channel related feature and a target constraint condition are input into a target meta-generative network, and an optimal network architecture under the target constraint condition is predicted by using the target meta-generative network to obtain a compressed neural network model.