A face detection method includes: acquiring a target image; invoking a face detection network, and processing the target image by using a feature extraction structure of the face detection network, to obtain original feature maps corresponding to the target image; the original feature maps having different resolutions; processing the original feature maps by using a feature enhancement structure of the face detection network, to obtain an enhanced feature map corresponding to each original feature map; the feature enhancement structure being obtained by searching a search space, and the search space used for searching the feature enhancement structure being determined based on a detection objective of the face detection network and a processing object of the feature enhancement structure; and processing the enhanced feature map by using a detection structure of the face detection network, to obtain a face detection result of the target image.

This application discloses an artificial intelligence-based image processing method, apparatus, device, and storage medium, and relates to the field of computer technology. The method includes: obtaining a slice image; dividing the slice image to obtain a plurality of image blocks; feeding the plurality of image blocks into a labeling model, extracting, by the labeling model, a pixel feature of the slice image based on the plurality of image blocks, determining a plurality of vertex positions of a polygonal region in the slice image based on the pixel feature, concatenating the plurality of vertex positions, and outputting label information of the slice image, the polygonal region being a region in which a target pathological tissue of interest is located.

The present invention discloses an object modeling and movement method. The method is applied to a mobile terminal, and the mobile terminal includes a color camera and a depth camera. The method includes: performing panoramic scanning on a target object by using the color camera and the depth camera, to obtain a 3D model of the target object; obtaining a target skeletal model; fusing the target skeletal model and the 3D model of the target object; obtaining a target movement manner; and controlling the target skeletal model in the target movement manner, to animate the 3D model of the target object in the target movement manner. This can implement integration from scanning, 3D reconstruction, skeletal rigging, to preset animation display for an object on one terminal, thereby implementing dynamization of a static object, and increasing interest in using the mobile terminal by a user.

The present disclosure relates to an information processing device, an information processing method, and a program for improving object recognition accuracy.

A feature amount calculation unit of a user's car calculates a feature amount for an object recognition result in stages in each of a plurality of hierarchies, an acquisition unit acquires a feature amount for an object recognition result calculated by a feature amount calculation unit of another car, and a recognition unit performs the object recognition on the basis of the feature amount calculated by the user's car and the acquired feature amount calculated by the another car. The present disclosure can be applied to a mobile body.

This application discloses a road constraint determining method and apparatus, applied to the intelligent driving field, and in particular, to a sensor in an advanced driver assistance system ADAS or an autonomous driving system, for example, radar and/or a photographing apparatus. In this method, a moving state of a target is determined based on detection information of the target; at least one road geometry of a road on which the target is located is determined based on the detection information of the target; and a road constraint of the target is determined based on the at least one road geometry and the moving state of the target. The road constraint includes at least one of a road direction constraint and a road width constraint. According to the solutions in this application, road constraint determining accuracy can be improved, and target tracking accuracy can be further improved.

Methods and systems are provided for implementing source separation techniques, and more specifically performing source separation on mixed source single-channel and multi-channel audio signals enhanced by inputting lip motion information from captured image data, including selecting a target speaker facial image from a plurality of facial images captured over a period of interest; computing a motion vector based on facial features of the target speaker facial image; and separating, based on at least the motion vector, audio corresponding to a constituent source from a mixed source audio signal captured over the period of interest. The mixed source audio signal may be captured from single-channel or multi-channel audio capture devices. Separating audio from the audio signal may be performed by a fusion learning model comprising a plurality of learning sub-models. Separating the audio from the audio signal may be performed by a blind source separation (“BSS”) learning model.

Provided are methods and apparatuses for determining a target object in an image based on an interactive input. A target object determining method acquires first feature information corresponding to an image and second feature information corresponding to an interactive input; and determines a target object corresponding to the interactive input from among objects in the image based on the first feature information and the second feature information.

The present disclosure discloses a video classification method, an electronic device and a storage medium, and relates to the field of computer technologies, and particularly to the field of artificial intelligence technologies, such as knowledge graph technologies, computer vision technologies, deep learning technologies, or the like. The video classification method includes: extracting a keyword in a video according to multi-modal information of the video; acquiring background knowledge corresponding to the keyword, and determining a text to be recognized according to the keyword and the background knowledge; and classifying the text to be recognized to obtain a class of the video.

Provided are a vehicle outside information acquiring unit to acquire vehicle outside information, a face information acquiring unit to acquire face information, a biological information acquiring unit to acquire biological information, a vehicle information acquiring unit to acquire vehicle information, a vehicle outside information feature amount extracting unit to extract a vehicle outside information feature amount on the basis of the vehicle outside information, a face information feature amount extracting unit to extract a face information feature amount in accordance with the vehicle outside information feature amount, a biological information feature amount extracting unit to extract a biological information feature amount in accordance with the vehicle outside information feature amount, a vehicle information feature amount extracting unit to extract a vehicle information feature amount in accordance with the vehicle outside information feature amount, and a cognitive function estimation unit to estimate whether a cognitive function of a driver is low on the basis of a machine learning model, the vehicle outside information feature amount, and at least one of the face information feature amount, the biological information feature amount, or the vehicle information feature amount.

The present disclosure provides an object detection method. The method includes: acquiring a scene image of a scene; acquiring a three-dimensional point cloud corresponding to the scene; segmenting the scene image into a plurality of sub-regions; merging the plurality of sub-regions according to the three-dimensional point cloud to generate a plurality of region proposals; and performing object detection on the plurality of region proposals to determine a target object to be detected in the scene image. In addition, the present disclosure also provides an object detection device, a terminal device, and a medium.