Abstract: The present disclosure provides an image processing method. An image to be classified is input into a feature extraction model to generate N dimensional features. Dimension fusion is performed on M features of the N dimensional features to obtain M dimension fusion features. The image to be classified is processed based on M dimension fusion features and remaining features of the N dimensional features other than the M features.

Abstract: The present application discloses a vehicle re-identification method and apparatus, a device and a storage medium, which relates to the field of computer vision, intelligent search, deep learning and intelligent transportation. The specific implementation scheme is: receiving a re-identification request from a terminal device, the re-identification request including a first image of a first vehicle shot by a first camera and information of the first camera; acquiring a first feature of the first vehicle and a first head orientation of the first vehicle according to the first image; determining a second image of the first vehicle from images of multiple vehicles according to the first feature, multiple second features extracted based on the images of the multiple vehicles in an image database, the first head orientation of the first vehicle, and the information of the first camera; and transmitting the second image to the terminal device.

Abstract: A method, an electronic device and a storage medium are provided. The method may include: acquiring a to-be-inspected image; inputting the to-be-inspected image into a pre-established vehicle detection model to obtain a vehicle detection result, where the vehicle detection result includes category information, coordinate information, coordinate reliabilities, and coordinate error information of detection boxes, and the vehicle detection model is configured for characterizing a corresponding relationship between images and vehicle detection results; selecting, based on the coordinate reliabilities of the detection boxes, a detection box from the vehicle detection result for use as a to-be-processed detection box; and generating, based on coordinate information and coordinate error information of the to-be-processed detection box, coordinate information of a processed detection box.

Abstract: A method for generating a depth map, an electronic device and a storage medium. The method includes: obtaining a point cloud map and a visual image of a scene; generating a first depth value of each pixel in the visual image based on the point cloud map and the visual image; determining a three-dimensional coordinate location of each pixel in a world coordinate system based on a coordinate location and the first depth value of each pixel in the visual image; generating a second depth value of each pixel by inputting the three-dimensional coordinate location and pixel information of each pixel into a depth correction model; and generating the depth map of the scene based on the second depth value of each pixel.

Abstract: The present application discloses a method and an apparatus for vehicle re-identification, a training method, an electronic device and a storage medium, relating to the field of artificial intelligence, in particular, to technologies of computer vision, deep learning and intelligent transport. A specific implementation is: acquiring a picture of a target vehicle to be re-identified, determining a target two-dimensional image of the target vehicle based on the picture and a preset initial three-dimensional model, the initial three-dimensional model being generated based on sample three-dimensional information of a sample vehicle, and re-identifying the target two-dimensional image to generate and output an identification result.

Abstract: The present application discloses a method for training an image depth estimation model, a method and apparatus for processing image depth information, an automatic driving vehicle, an electronic device, a program product, a storage medium, which includes: inputting a sample environmental image, sample environmental point cloud data and sample edge information of the sample environmental image into a to-be-trained model; and determining initial depth information of each of pixel points in the sample environmental image and a feature relationship between each of the pixel points and a corresponding neighboring pixel point of each of the pixel points through the to-be-trained model, and optimizing the initial depth information of each of the pixel points according to the feature relationship to obtain optimized depth information of each of the pixel points, and adjusting a parameter of the to-be-trained model according to the optimized depth information to obtain the image depth estimation model.

Abstract: The present disclosure provides an image processing method. An image to be classified is input into a feature extraction model to generate N dimensional features. Dimension fusion is performed on M features of the N dimensional features to obtain M dimension fusion features. The image to be classified is processed based on M dimension fusion features and remaining features of the N dimensional features other than the M features.

Abstract: The present application discloses a vehicle re-identification method and apparatus, a device and a storage medium, which relates to the field of computer vision, intelligent search, deep learning and intelligent transportation. The specific implementation scheme is: receiving a re-identification request from a terminal device, the re-identification request including a first image of a first vehicle shot by a first camera and information of the first camera; acquiring a first feature of the first vehicle and a first head orientation of the first vehicle according to the first image; determining a second image of the first vehicle from images of multiple vehicles according to the first feature, multiple second features extracted based on the images of the multiple vehicles in an image database, the first head orientation of the first vehicle, and the information of the first camera; and transmitting the second image to the terminal device.