Abstract: A computer device extracts an image feature of an image that includes one or more characters to be recognized. The image feature includes a plurality of image feature vectors. The device uses an attention mechanism to compute and output attention weight values corresponding to the target number of characters, based on the image feature vectors, through parallel computing. Each of the attention weight values corresponds to one or more respective characters and represents an importance of the plurality of image feature vectors for the respective characters. The device obtains at least one character according to the plurality of image feature vectors and the target number of attention weight values. Therefore, in a character recognition process, with recognition based on the foregoing attention mechanism, a character in any shape can be effectively recognized by using a simple procedure, thereby avoiding a cyclic operation process and greatly improving operation efficiency.

Abstract: This application discloses an image processing method, device, and apparatus, and a storage medium. The method is performed by a computing device, the method including: receiving an input image; determining a context feature of the input image; determining a first feature set and a second feature set according to the context feature and based on a size of a target image and a location of the input image in the target image; adjusting the second feature set according to a first feature statistic of the first feature set, to obtain an adjusted second feature set; and generating the target image based on the adjusted second feature set and the first feature set.

Abstract: The present disclosure provides an image processing method and apparatus, and an electronic device, and relates to the field of artificial intelligence (AI). The method includes: obtaining an input image and extracting region features of image regions in the input image to obtain a first image feature; processing pixels in the first image feature according to a predetermined rule, and determining a second image feature according to the processed pixels; and determining, based on the second image feature and at least one word vector that is determined for the input image, word vectors corresponding to the region features of the image regions in the first image feature at different moments, predicting positions of the word vectors in a text description, and forming the text description corresponding to the input image according to the word vectors and the positions.

Abstract: This application relates to a point cloud segmentation method, a computer-readable storage medium, and a computer device. The method includes encoding a to-be-processed point cloud to obtain a shared feature, the shared feature referring to a feature shared at a semantic level and at an instance level; decoding the shared feature to obtain a semantic feature and an instance feature respectively; adapting the semantic feature to an instance feature space and fusing the semantic feature with the instance feature, to obtain a semantic-fused instance feature of the point cloud, the semantic-fused instance feature representing an instance feature fused with the semantic feature; dividing the semantic-fused instance feature of the point cloud, to obtain a semantic-fused instance feature of each point in the point cloud; and determining an instance category to which each point belongs according to the semantic-fused instance feature of each point.

Abstract: Embodiments of this disclosure include an image enhancement method and apparatus. The image enhancement may include obtaining an original image and performing synthesis processing on features of the original image to obtain a first illumination map corresponding to the original image. A resolution of the first illumination map may be lower than a resolution of the original image. The image enhancement may further include obtaining, based on the first illumination map, a mapping relationship between an image to an illumination map and performing mapping processing on the original image based on the mapping relationship to obtain a second illumination map. A resolution of the second illumination map may be equal to the resolution of the original image. The image enhancement may further include performing image enhancement processing on the original image according to the second illumination map to obtain a target image.

Abstract: A gesture language recognition method is provided. In the method, a first video is obtained. Gesture features are extracted from frames of images in the first video. Gesture change features are extracted from the frames of the images in the first video. Gesture language word information is extracted from fused features that are determined based on the gesture features and the gesture change features. The gesture language word information is combined into a gesture language sentence according to context information corresponding to the gesture language word information.

Abstract: Embodiments of this application disclose a training method using image processing model for processing blurry images. The method includes obtaining a sample pair comprising a clear image and a corresponding blurry image; the sharpness of the clear image being greater than a preset threshold, the sharpness of the blurry image being less than the preset threshold; activating the image processing model to perform sharpness restoration on the blurry image to obtain a restored image; and updating network parameters of a first network and network parameters of a second network in the image processing model according to the restored image and the clear image to obtain a trained image processing model; the network parameters of the first network and the network parameters of the second network meeting a selective sharing condition indicating whether the network parameters between the first network and the second network are shared or independent.

Abstract: A video caption generating method is provided to a computer device. The method includes encoding a target video by using an encoder of a video caption generating model, to obtain a target visual feature of the target video, decoding the target visual feature by using a basic decoder of the video caption generating model, to obtain a first selection probability corresponding to a candidate word, decoding the target visual feature by using an auxiliary decoder of the video caption generating model, to obtain a second selection probability corresponding to the candidate word, a memory structure of the auxiliary decoder including reference visual context information corresponding to the candidate word, determining a decoded word in the candidate word according to the first selection probability and the second selection probability, and generating a video caption according to decoded word.

Abstract: This application provides a target tracking method, including: obtaining a plurality of consecutive picture frames of a target video, and setting a tracked target region of an nth picture frame; performing a feature extraction operation on the tracked target region of the nth picture frame, to obtain a feature map of the tracked target region; calculating a weighted filter corresponding to the input feature map according to a correlation filter algorithm and a mean pooling constraint condition; calculating an output response of an (n+1)th picture frame by using the weighted filter and an input feature map of the (n+1)th picture frame in the plurality of consecutive picture frames, and determining a tracked target region of the (n+1)th picture frame according to the output response of the (n+1)th picture frame, until tracked target regions of all the consecutive picture frames are obtained. This application improves precision and effectiveness of target tracking.

Abstract: This application provides a method for generating a high-resolution picture performed by a computer device. The method includes: acquiring at least one deep neural network model; acquiring a low-resolution picture; determining a corresponding deep neural network model according to the low-resolution picture; and converting the low-resolution pictures into a high-resolution picture through the deep neural network model, the deep neural network model including a plurality of non-linear conversion convolution layers that alternately use different parameter matrices as convolution template parameters.

Abstract: Embodiments of this application disclose a method and an apparatus for reconstructing a three-dimensional model of a human body, and a storage medium. The method includes obtaining a two-dimensional image of a human body part to be reconstructed; mapping the two-dimensional image to a voxel space, to obtain a voxelized result of the two-dimensional image; obtaining three-dimensional morphable model parameters corresponding to the two-dimensional image based on calculating the voxelized result using a preset three-dimensional parameter regression network; and reconstructing a three-dimensional model of the human body part based on the three-dimensional morphable model parameters.

Abstract: The present disclosure provides an image processing method and apparatus, and an electronic device, and relates to the field of artificial intelligence (AI). The method includes: obtaining an input image and extracting region features of image regions in the input image to obtain a first image feature; processing pixels in the first image feature according to a predetermined rule, and determining a second image feature according to the processed pixels; and determining, based on the second image feature and at least one word vector that is determined for the input image, word vectors corresponding to the region features of the image regions in the first image feature at different moments, predicting positions of the word vectors in a text description, and forming the text description corresponding to the input image according to the word vectors and the positions.

Abstract: Embodiments of the present disclosure disclose a face model processing method performed at an electronic device. The method includes the following steps: obtaining a three-dimensional face model corresponding to a user picture, and selecting a sample oral cavity model in an oral cavity model library for the three-dimensional face model; registering the sample oral cavity model into the three-dimensional face model by using an oral cavity position parameter in the three-dimensional face model; performing form adjustment on an oral cavity form of the registered sample oral cavity model by using an expression parameter of the three-dimensional face model to generate a target oral cavity model; and generating, based on the three-dimensional face model and the target oral cavity model, a three-dimensional face expression model corresponding to the user picture.

Abstract: A computer device extracts an image feature of an image that includes one or more characters to be recognized. The image feature includes a plurality of image feature vectors. The device uses an attention mechanism to compute and output attention weight values corresponding to the target number of characters, based on the image feature vectors, through parallel computing. Each of the attention weight values corresponds to one or more respective characters and represents an importance of the plurality of image feature vectors for the respective characters. The device obtains at least one character according to the plurality of image feature vectors and the target number of attention weight values. Therefore, in a character recognition process, with recognition based on the foregoing attention mechanism, a character in any shape can be effectively recognized by using a simple procedure, thereby avoiding a cyclic operation process and greatly improving operation efficiency.

Abstract: A gesture language recognition method is provided. In the method, a first video is obtained. Gesture features are extracted from frames of images in the first video. Gesture change features are extracted from the frames of the images in the first video. Gesture language word information is extracted from fused features that are determined based on the gesture features and the gesture change features. The gesture language word information is combined into a gesture language sentence according to context information corresponding to the gesture language word information.

Abstract: A data processing method is provided. In the data processing method, target sequence data is obtained. The target sequence data includes N groups of data sorted in chronological order. Processing is performed, according to an ith group of data in the N groups of data, processing results of a target neural network model for the ith group of data, and a processing result of the target neural network model for a jth piece of data in an (i+1)th group of data, a (j+1)th piece of data in the (i+1)th group of data by using the target neural network model, to obtain a processing result of the target neural network model for the (j+1)th piece of data in the (i+1)th group of data, i being greater than or equal to 1 and less than N, and j being greater than or equal to 1 and less than Q.

Abstract: This application relates to a point cloud segmentation method, a computer-readable storage medium, and a computer device. The method includes encoding a to-be-processed point cloud to obtain a shared feature, the shared feature referring to a feature shared at a semantic level and at an instance level; decoding the shared feature to obtain a semantic feature and an instance feature respectively; adapting the semantic feature to an instance feature space and fusing the semantic feature with the instance feature, to obtain a semantic-fused instance feature of the point cloud, the semantic-fused instance feature representing an instance feature fused with the semantic feature; dividing the semantic-fused instance feature of the point cloud, to obtain a semantic-fused instance feature of each point in the point cloud; and determining an instance category to which each point belongs according to the semantic-fused instance feature of each point.

Abstract: This application discloses an image processing method, device, and apparatus, and a storage medium. The method is performed by a computing device, the method including: receiving an input image; determining a context feature of the input image; determining a first feature set and a second feature set according to the context feature and based on a size of a target image and a location of the input image in the target image; adjusting the second feature set according to a first feature statistic of the first feature set, to obtain an adjusted second feature set; and generating the target image based on the adjusted second feature set and the first feature set.

Abstract: This application provides a target tracking method, including: obtaining a plurality of consecutive picture frames of a target video, and setting a tracked target region of an nth picture frame; performing a feature extraction operation on the tracked target region of the nth picture frame, to obtain a feature map of the tracked target region; calculating a weighted filter corresponding to the input feature map according to a correlation filter algorithm and a mean pooling constraint condition; calculating an output response of an (n+1)th picture frame by using the weighted filter and an input feature map of the (n+1)th picture frame in the plurality of consecutive picture frames, and determining a tracked target region of the (n+1)th picture frame according to the output response of the (n+1)th picture frame, until tracked target regions of all the consecutive picture frames are obtained. This application improves precision and effectiveness of target tracking.

Abstract: Embodiments of this application disclose a training method using image processing model for processing blurry images. The method includes obtaining a sample pair comprising a clear image and a corresponding blurry image; the sharpness of the clear image being greater than a preset threshold, the sharpness of the blurry image being less than the preset threshold; activating the image processing model to perform sharpness restoration on the blurry image to obtain a restored image; and updating network parameters of a first network and network parameters of a second network in the image processing model according to the restored image and the clear image to obtain a trained image processing model; the network parameters of the first network and the network parameters of the second network meeting a selective sharing condition indicating whether the network parameters between the first network and the second network are shared or independent.