A video co-shooting method, an apparatus, an electronic device, and a computer-readable medium are provided, which involve the field of video processing technology. The method includes: receiving a co-shooting request input by a user based on a first video; in response to the co-shooting request, turning on a video capture apparatus, and acquiring a second video through the video capture apparatus; and fusing the first video with the second video to obtain a target video. In the embodiments of the present disclosure, a video capture apparatus is turned on according to a co-shooting request input by a user based on a first video, a second video is acquired through the video capture apparatus, and the first video is fused with the second video, so as to obtain a target video.

A text extraction method and a text extraction model training method are provided. The present disclosure relates to the technical field of artificial intelligence, in particular to the technical field of computer vision. An implementation of the method comprises: obtaining a visual encoding feature of a to-be-detected image; extracting a plurality of sets of multimodal features from the to-be-detected image, wherein each set of multimodal features includes position information of one detection frame extracted from the to-be-detected image, a detection feature in the detection frame and first text information in the detection frame; and obtaining second text information matched with a to-be-extracted attribute based on the visual encoding feature, the to-be-extracted attribute and the plurality of sets of multimodal features, wherein the to-be-extracted attribute is an attribute of text information needing to be extracted.

Various examples of the disclosure relate to aspects associated with training a machine-learned algorithm configured to count cells in a microscopy image or to determine a degree of confluence of the cells.

Embodiments described herein provide a system and method for extracting information. The system receives, via a communication interface, a dataset of a plurality of data samples. The system determines, in response to an input data sample from the dataset, a set of feature vectors via a plurality of pre-trained feature extractors, respectively. The system retrieves a set of memory bank vectors that correspond to the input data sample. The system, generates, via a plurality of Multi-Layer-Perceptrons (MLPs), a mapped set of representations in response to an input of the set of memory bank vectors, respectively. The system determines a loss objective between the set of feature vectors and the combination of the mapped set of representations and a network of layers in the MLP. The system updates, the parameters of the plurality of MLPs and the parameters of the memory bank vectors by minimizing the computed loss objective.

A method, apparatus, electronic device, and non-transitory computer-readable storage medium with multi-modal feature fusion are provided. The method includes generating three-dimensional (3D) feature information and two-dimensional (2D) feature information based on a color image and a depth image, generating fused feature information by fusing the 3D feature information and the 2D feature information based on an attention mechanism, and generating predicted image information by performing image processing based on the fused feature information.

Systems and methods for face recognition are provided. The systems may perform the methods to obtain a neural network comprising a first sub-neural network and a second sub-neural network; generate a plurality of preliminary feature vectors based on an image associated with a human face, the plurality of preliminary feature vectors comprising a color-based feature vector; obtain at least one input feature vector based on the plurality of preliminary feature vectors; generate a deep feature vector based on the at least one input feature vector using the first sub-neural network; and recognize the human face based on the deep feature vector.

A stripe pattern image collating device according to the example embodiment includes a feature extracting unit that extracts a feature point and a skeleton from a first stripe pattern image and a second stripe pattern image in which a stripe pattern is formed of ridges, and generates feature point data and skeleton data. A skeleton collating unit that collates two sets of pieces of the feature point data and pieces of the skeleton data that are extracted from each of the first stripe pattern image and the second stripe pattern image, and calculates a collation score. An image analyzing unit that analyzes the second stripe pattern image with respect to an area in which an opposite feature point pair of the first stripe pattern image exists, calculates an image analysis score, and corrects the collation score.

A real-time ground fusion system is based on binocular stereo vision and an intelligent terminal. The method for accomplish real-time ground fusion includes: S1 of obtaining a disparity map about a same road scenario, and converting a disparity map in a target region into a 3D point cloud; S2 of performing pose conversion on a current frame and a next frame adjacent to the current frame, and performing inverse conversion on a 3D point cloud of the current frame; and S3 of repeating S2 with each frame in the target region as the current frame, so as to achieve ground fusion. Through the conversion and fusion of adjacent frames, holes caused by the projection of the disparity map can be filled to assist driving and output accurate height data, thereby improving comfortableness.

A method for face recognition is disclosed. The method includes: obtaining an image to be recognized; extracting an image feature of the image to be recognized; obtaining a fused feature corresponding to a reference image of reference image; determining a similarity between the image feature of the image to be recognized and the fused feature corresponding to the reference image of the reference images to obtain a determination result of the similarity; and determining, based on the obtained determination result of the similarity, a face recognition result of the image to be recognized. It can be learned that recognition precision of a human face having a shielding object and a human face not having the shielding object may be ensured by means of this solution.

Provided is a method of verifying an authenticity of declaration information. The method includes: acquiring a machine-detected radiation image obtained by scanning a container loaded with an article; acquiring a declaration information for declaring the article in the container; performing an identification on an image information of the article in the machine-detected radiation image to obtain an image feature corresponding to the machine-detected radiation image; performing an identification on a text information of the article in the declaration information to obtain a text feature corresponding to the declaration information; screening a declaration category of the article in the container by taking the image feature as an input information and the text feature as an external introduction feature; and determining that the declaration information is in doubt when a declaration category of at least one article in the container does not belong to a declaration category in the declaration information.