The present disclosure provides a method for automatic control of a vehicle and a method for training lane change intention prediction network. The method includes receiving a plurality of types of vehicle traveling information of a target vehicle; inputting the plurality of types of vehicle traveling information of the target vehicle into a lane change intention prediction network, the lane change intention prediction network comprising a plurality of sub-networks; performing, through the sub-networks, feature extraction on the types of vehicle traveling information respectively, and outputting feature extraction results; performing feature fusion on the feature extraction results outputted by the sub-networks, and predicting a lane change intention of the target vehicle according to a feature fusion result; and updating an autonomous driving route of a current vehicle according to the lane change intention of the target vehicle.

A method and device for image processing, an electronic device and a storage medium are disclosed. The method includes: acquiring an image sequence to be processed; obtaining a target image sequence section by determining, in the image sequence to be processed, an image sequence section where a target image is located; and determining an image region corresponding to at least one image feature class in the target image sequence section by segmenting the target image in the target image sequence section.

A method and device for updating data, an electronic device and a storage medium are provided. The method includes that: a first image of a target object is acquired, and a first image feature of the first image is acquired; a second image feature is acquired from a local face database; similarity comparison is performed between the first image feature and the second image feature to obtain a comparison result; responsive to that the comparison result is greater than a feature update threshold, a difference feature between the first image feature and the second image feature is acquired, and the difference feature is taken as a dynamic update feature; and the second image feature is adaptively updated according to the dynamic update feature to obtain updated feature data of the target object.

Methods, apparatuses, electronic devices, and storage media for makeup look processing are provided. In one aspect, a method includes: displaying a collected face image; in response to a selection of a makeup look from one or more makeup looks, identifying a face part area matching the makeup look from the face image and providing an instruction for a making-up content for the face part area; detecting, from an updated collected face image, pixel change information of the face part area; and determining whether the pixel change information of the face part area meets a makeup effect condition for the first makeup look.

The disclosure relates to a communication method and system for converging a 5.sup.th-Generation (5G) communication system for supporting higher data rates beyond a 4.sup.th-Generation (4G) system with a technology for IoT. The disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. An image processing method and apparatus, electronic device and computer readable storage medium, which belong to image processing field are provided. The method and apparatus, electronic device and computer readable storage medium include segmenting an image to be processed to obtain a target region in the image to be processed, and performing style transfer on the target region. The solution provided may effectively improve effects of image processing, and better meet requirements of practical application.

A method for processing images, including: acquiring an original image and auxiliary information of the original image; acquiring an object feature map by inputting the original image into a main path of a first visual task processing model, and acquiring an auxiliary feature map by inputting the auxiliary information into a branch path of the first visual task processing model; and acquiring a response map of the original image by fusing the object feature map and the auxiliary feature map and inputting fused object feature map and auxiliary feature map into the main path of the first visual task processing model.

A method for retrieving footprint images is provided, comprising: pre-training models; cleaning footprint data and conducting expansion pre-processing by using the pre-trained models, dividing the footprint data into multiple data sets; adjusting full connection layers and classification layers of the models; training the models again by using the data sets through the parameters of the pre-trained models; saving the models trained twice, removing the classification layer, executing a feature extraction for images in an image library and a retrieval library to form a feature index library; connecting the features extracted by three models to form fused features, establishing a fused feature vector index library; extracting the features of the images in the image library to be retrieved in advance, and establishing a feature vector library; calculating distances in the retrieval library and the image library when a single footprint image is inputted, thereby outputting the image with the highest similarity.

A method is disclosed. The method includes obtaining an object for prediction and a plurality of candidate scenes by a computer device; inputting the object for prediction and a current candidate scene to a distance measurement model, the distance measurement model calculates a feature vector corresponding to the current candidate scene based on a trained scene feature subnetwork, and outputs a distance from the object for prediction to the current candidate scene based on the object for prediction and the feature vector corresponding to the current candidate scene, model parameters of the distance measurement model including a parameter determined by a trained object feature subnetwork; obtaining distances from the object for prediction to the plurality of candidate scenes based on the distance measurement model; determining a target scene corresponding to the object for prediction based on the distances from the object for prediction to the plurality of candidate scenes.

The present disclosure relates to a method and apparatus for processing a multimedia signal. More specifically, the present disclosure relates to a method comprising obtaining at least one video object from the multimedia signal and at least one audio object from the multimedia signal, extracting video feature information for the at least one video object and audio feature information for the at least one audio object, and determining a correlation between the at least one video object and the at least one audio object through an object matching engine based on the video feature information and the audio feature information, and an apparatus therefor.

Machine learning based models recognize objects in images. Specific features of the object are extracted from the image using machine learning based models. The specific features extracted from the image assist deep learning based models in identifying subtypes of a type of object. The system recognizes the objects and collections of objects and determines whether the arrangement of objects violates any predetermined policies. For example, a policy may specify relative positions of different types of objects, height above ground at which certain types of objects are placed, or an expected number of certain types of objects in a collection.