Abstract: This application relates to a target object detection method and apparatus, a non-transitory computer-readable storage medium, and a computer device. The method includes: obtaining a to-be-detected image; inputting the to-be-detected image into a target object detection model; generating, by the target object detection model, a prediction diagram corresponding to the to-be-detected image, the prediction diagram describing a relation degree to which pixels of the to-be-detected image belong to a target detection object; and performing region segmentation on the prediction diagram to obtain a target detection object region. In addition, a method and an apparatus for training an object detection model into the target object detection model, a non-transitory computer-readable storage medium, and a computer device are also provided.

Abstract: A method for obtaining a binocular panoramic image is performed at an electronic apparatus, including: obtaining first and second panoramic images acquired by two panoramic cameras; obtaining at least one group of a first pixel located in the first panoramic image and a second pixel located in the second panoramic image; calculating a distance between the first pixel and the second pixel in each group, and obtaining depth information corresponding to the two panoramic image according to the distance between the first pixel and the second pixel; converting the first panoramic image and the second panoramic image into a first monocular panoramic image and a second monocular panoramic image, respectively in accordance with the corresponding depth information and a preset pupil distance between a first eye and a second eye; and combining a display of the first monocular panoramic image and the second monocular panoramic image in corresponding display regions.

Abstract: This disclosure relates to an account filtering method and apparatus.

Abstract: Aspects of the disclosure provide an information processing apparatus that includes interface circuitry and processing circuitry. The interface circuitry is configured to obtain a text authored by a person. The processing circuitry is configured to analyze the text to obtain measurements of language features of the person, input the measurements of the language features into an evaluation model that is trained to predict a score as a function of the language features, determine a specific score for the person based on the evaluation model and output the specific score of the person for predicting a behavior of the person.

Abstract: This application relates to a target object detection method and apparatus, a non-transitory computer-readable storage medium, and a computer device. The method includes: obtaining a to-be-detected image; inputting the to-be-detected image into a target object detection model; generating, by the target object detection model, a prediction diagram corresponding to the to-be-detected image, the prediction diagram describing a relation degree to which pixels of the to-be-detected image belong to a target detection object; and performing region segmentation on the prediction diagram to obtain a target detection object region. In addition, a method and an apparatus for training an object detection model into the target object detection model, a non-transitory computer-readable storage medium, and a computer device are also provided.

Abstract: A method for obtaining a binocular panoramic image is performed at an electronic apparatus, including: obtaining first and second panoramic images acquired by two panoramic cameras; obtaining at least one group of a first pixel located in the first panoramic image and a second pixel located in the second panoramic image; calculating a distance between the first pixel and the second pixel in each group, and obtaining depth information corresponding to the two panoramic image according to the distance between the first pixel and the second pixel; converting the first panoramic image and the second panoramic image into a first monocular panoramic image and a second monocular panoramic image, respectively in accordance with the corresponding depth information and a preset pupil distance between a first eye and a second eye; and combining a display of the first monocular panoramic image and the second monocular panoramic image in corresponding display regions.

Abstract: A method for obtaining a binocular panoramic image is performed at an electronic apparatus. The method includes: obtaining first and second panoramic images acquired by two panoramic cameras; obtaining at least one group of a first pixel located in the first panoramic image and a second pixel located in the second panoramic image; calculating a distance between the first pixel and the second pixel in each group, and obtaining depth information corresponding to the two panoramic image according to the distance between the first pixel and the second pixel; obtaining a second monocular panoramic image through mapping by using one of the two panoramic images as a first monocular panoramic image, in combination with the corresponding depth information and a preset pupil distance between a first eye and a second eye; and displaying the first monocular panoramic image and the second monocular panoramic image in corresponding display regions.

Abstract: This disclosure relates to an account filtering method and apparatus.

Abstract: An image classification method is provided. The method includes: inputting a to-be-classified image into a plurality of neural network models; obtaining data output by multiple non-input layers specified by each neural network model to generate a plurality of image features corresponding to the plurality of neural network models; respectively inputting the plurality of corresponding image features into linear classifiers, each of the linear classifiers being trained by one of the plurality of neural network models for determining whether an image belongs to a preset class; obtaining, using each neural network model, a corresponding probability that the to-be-classified image comprises an object image of the preset class; and determining, according to each obtained probability, whether the to-be-classified image includes the object image of the preset class.

Abstract: Embodiments of this disclosure belong to the field of computer technologies and disclose a method and an apparatus for recognizing a character string in an image.

Abstract: A hand motion identification method includes: obtaining a to-be-identified video; performing area-locating and tracking of a hand for the to-be-identified video, and extracting a red-green-blue (RGB) video and a depth information video of the hand; detecting the RGB video and the depth information video of the hand, to obtain a feature point; representing the feature point by using a 3D Mesh motion scale-invariant feature transform (MoSIFT) feature descriptor; and comparing the 3D Mesh MoSIFT feature descriptor of the feature point with a 3D Mesh MoSIFT feature descriptor in a positive sample obtained through beforehand training, to obtain a hand motion category in the to-be-identified video.

Abstract: An information processing method and apparatus, the method including: training a Deep Neural Network (DNN) by using an evaluation object seed, an evaluation term seed and an evaluation relationship seed (101); at a first input layer, connecting vectors corresponding to a candidate evaluation object, a candidate evaluation term and a candidate evaluation relationship to obtain a first input vector (102); at a first hidden layer, compressing the first input vector to obtain a first middle vector, and at a first output layer, decoding the first middle vector to obtain a first output vector (103); and determining a first output vector whose decoding error value is less than a decoding error value threshold, and determining a candidate evaluation object, a candidate evaluation term and a candidate evaluation relationship corresponding to the determined first output vector as first opinion information (104).

Abstract: The present disclosure provides a three-dimensional facial recognition method and system. The method includes: performing pose estimation on an input binocular vision image pair by using a three-dimensional facial reference model, to obtain a pose parameter and a virtual image pair of the three-dimensional facial reference model with respect to the binocular vision image pair; reconstructing a facial depth image of the binocular vision image pair by using the virtual image pair as prior information; detecting, according to the pose parameter, a local grid scale-invariant feature descriptor corresponding to an interest point in the facial depth image; and generating a recognition result of the binocular vision image pair according to the detected local grid scale-invariant feature descriptor and training data having attached category annotations. The present disclosure can reduce computational costs and required storage space.

Abstract: It is described an information processing method and device. It is received a request for service data from a client device. In response to the request for service data, it is sent at least two probing packets which contain the service data to the client device on a forward path which is from a server to the client device. It is obtained timing information, which includes: a time stamp corresponding to the service data, a time stamp corresponding to the request for the service data, and time stamps corresponding to at least two backward-path packets sent by the client device on a backward path, the backward path being from the client device to the server. It is determined according to the timing information a one-way path metric.

Abstract: A method for obtaining a binocular panoramic image is performed at an electronic apparatus. The method includes: obtaining first and second panoramic images acquired by two panoramic cameras; obtaining at least one group of a first pixel located in the first panoramic image and a second pixel located in the second panoramic image; calculating a distance between the first pixel and the second pixel in each group, and obtaining depth information corresponding to the two panoramic image according to the distance between the first pixel and the second pixel; obtaining a second monocular panoramic image through mapping by using one of the two panoramic images as a first monocular panoramic image, in combination with the corresponding depth information and a preset pupil distance between a first eye and a second eye; and displaying the first monocular panoramic image and the second monocular panoramic image in corresponding display regions.

Abstract: A method for extracting a characteristic of a three-dimensional face image includes: performing face area division, to obtain a group of face areas; projecting each face area onto a corresponding regional bounding sphere; obtaining an indication of the corresponding face area according to the regional bounding sphere, and recording the indication as a regional bounding spherical descriptor of the face area; calculating a weight of the regional bounding spherical descriptor of the face area for each face area; and obtaining a characteristic of a three-dimensional face image according to the indication of the face area and the corresponding weight.

Abstract: The present disclosure provides a three-dimensional facial recognition method and system. The method includes: performing pose estimation on an input binocular vision image pair by using a three-dimensional facial reference model, to obtain a pose parameter and a virtual image pair of the three-dimensional facial reference model with respect to the binocular vision image pair; reconstructing a facial depth image of the binocular vision image pair by using the virtual image pair as prior information; detecting, according to the pose parameter, a local grid scale-invariant feature descriptor corresponding to an interest point in the facial depth image; and generating a recognition result of the binocular vision image pair according to the detected local grid scale-invariant feature descriptor and training data having attached category annotations. The present disclosure can reduce computational costs and required storage space.

Abstract: Aspects of the disclosure provide an information processing apparatus that includes interface circuitry and processing circuitry. The interface circuitry is configured to obtain a text authored by a person. The processing circuitry is configured to analyze the text to obtain measurements of language features of the person, input the measurements of the language features into an evaluation model that is trained to predict a score as a function of the language features, determine a specific score for the person based on the evaluation model and output the specific score of the person for predicting a behavior of the person.

Abstract: Embodiments of this disclosure belong to the field of computer technologies and disclose a method and an apparatus for recognizing a character string in an image.

Abstract: An image classification method is provided. The method includes: inputting a to-be-classified image into a plurality of neural network models; obtaining data output by multiple non-input layers specified by each neural network model to generate a plurality of image features corresponding to the plurality of neural network models; respectively inputting the plurality of corresponding image features into linear classifiers, each of the linear classifiers being trained by one of the plurality of neural network models for determining whether an image belongs to a preset class; obtaining, using each neural network model, a corresponding probability that the to-be-classified image comprises an object image of the preset class; and determining, according to each obtained probability, whether the to-be-classified image includes the object image of the preset class.