Abstract: A semantic segmentation method performed by at least one computing device is provided. The method may include extracting a plurality of support features, which include a first-level feature and a second-level feature, from a support image; generating a plurality of prototypes for a class associated with the support image, based on the plurality of support features; and predicting a segmentation mask for a query image using the plurality of prototypes, wherein the plurality of prototypes comprise a first prototype generated based on the first-level feature and a second prototype generated based on the second-level feature.

Abstract: A method for training a deep learning model according to a disclosed embodiment is performed by a computing device comprising one or more processors and a memory in which one or more programs to be executed by the one or more processors are stored, and the method includes acquiring first training data and extracting training feature vectors from the first training data, classifying the first training data into a plurality of groups based on the extracted training feature vectors, extracting ground-truth feature vectors from ground truths labeled on the first training data, classifying the ground-truth feature vectors into a plurality of groups corresponding to groups of the training feature vectors, calculating group reference information for each group of the ground-truth feature vectors, and setting a quality weight for second training data using the group reference information for each group.

Abstract: A method of generating an integrated feature vector according to an embodiment is a method performed in a computing device including one or more processors and a memory for storing one or more programs executed by the one or more processors. The method includes receiving a plurality of images of an object; and generating the integrated feature vector including a feature vector of each of the plurality of images, wherein the plurality of images is generated in a plurality of environments different from each other.

Abstract: The present invention relates to a vehicle driving assistance device and a vehicle having same. The vehicle driving assistance device according to an embodiment of the present invention comprises: a stereo camera; an interface unit exchanging data with at least one device inside a vehicle; and a processor performing lane detection for a first region in front of the vehicle based on a stereo image received from the stereo camera, and deducing lanes for a second region in front of the vehicle based on map information from an interface unit. Accordingly, the lanes in front of a vehicle can be perceived based on a captured image.

Abstract: The present invention relates to a vehicle driving assistance device and a vehicle having same. The vehicle driving assistance device according to an embodiment of the present invention comprises: a stereo camera; an interface unit exchanging data with at least one device inside a vehicle; and a processor performing lane detection for a first region in front of the vehicle based on a stereo image received from the stereo camera, and deducing lanes for a second region in front of the vehicle based on map information from an interface unit. Accordingly, the lanes in front of a vehicle can be perceived based on a captured image.