A method and apparatus of processing an image and a vehicle including the same includes obtaining image information; inputting a ground truth; inputting the image information to an object recognition model to output recognition information including a position and a type of an object included in the image information; generating misrecognition data based on the output recognition information and the ground truth; generating a weak pattern map of the object recognition model by processing the misrecognition data; and classifying a weak database based on the weak pattern map and at least one piece of pre-stored image information.

A method and apparatus of processing an image and a vehicle including the same includes obtaining image information; inputting a ground truth; inputting the image information to an object recognition model to output recognition information including a position and a type of an object included in the image information; generating misrecognition data based on the output recognition information and the ground truth; generating a weak pattern map of the object recognition model by processing the misrecognition data; and classifying a weak database based on the weak pattern map and at least one piece of pre-stored image information.

A method for generating training data for a trainable method for a system including sensor(s) for detecting at least one subarea of the surroundings around the system. The method includes: a) obtaining first and second detections having at least one known relative ratio between the detections and/or the sensors that carried out the detections; b) determining a portion of the particular content of the detections, and assigning a piece of information concerning the determined content to the detection in question, c) projecting assigned piece of information from one of the detections and/or from a content representation associated with same into at least one other of the detections and/or into a content representation associated with the other detection, d) checking a subarea of at least one of the detections and/or of at least one of the content representations for possible inconsistencies in the detection content.

A method for generating training data for a trainable method for a system including sensor(s) for detecting at least one subarea of the surroundings around the system. The method includes: a) obtaining first and second detections having at least one known relative ratio between the detections and/or the sensors that carried out the detections; b) determining a portion of the particular content of the detections, and assigning a piece of information concerning the determined content to the detection in question, c) projecting assigned piece of information from one of the detections and/or from a content representation associated with same into at least one other of the detections and/or into a content representation associated with the other detection, d) checking a subarea of at least one of the detections and/or of at least one of the content representations for possible inconsistencies in the detection content.

Aspects of the present disclosure involve a system for presenting AR items. The system performs operations including: receiving an image that includes a depiction of a first real-world body part in a real-world environment; applying a machine learning technique to the image to generate a plurality of dense outputs each associated with a respective pixel of a plurality of pixels in the image; applying a first task-specific decoder to the plurality of dense outputs to identify a pixel corresponding to a center of the first real-world body part; applying a second task-specific decoder using the identified pixel to retrieve a 3D rotation, translation and scale of first real-world body part from the plurality of dense outputs; modifying an AR object based on the 3D rotation, translation, and scale of first real-world body part; and modifying the image to include a depiction of the modified AR object.

Aspects of the present disclosure involve a system for presenting AR items. The system performs operations including: receiving an image that includes a depiction of a first real-world body part in a real-world environment; applying a machine learning technique to the image to generate a plurality of dense outputs each associated with a respective pixel of a plurality of pixels in the image; applying a first task-specific decoder to the plurality of dense outputs to identify a pixel corresponding to a center of the first real-world body part; applying a second task-specific decoder using the identified pixel to retrieve a 3D rotation, translation and scale of first real-world body part from the plurality of dense outputs; modifying an AR object based on the 3D rotation, translation, and scale of first real-world body part; and modifying the image to include a depiction of the modified AR object.

Systems and methods for machine learning are described. The systems and methods include receiving target training data including a training image and ground truth label data for the training image, generating source network features for the training image using a source network trained on source training data, generating target network features for the training image using a target network, generating at least one attention map for training the target network based on the source network features and the target network features using a guided attention transfer network, and updating parameters of the target network based on the attention map and the ground truth label data.

Systems and methods for machine learning are described. The systems and methods include receiving target training data including a training image and ground truth label data for the training image, generating source network features for the training image using a source network trained on source training data, generating target network features for the training image using a target network, generating at least one attention map for training the target network based on the source network features and the target network features using a guided attention transfer network, and updating parameters of the target network based on the attention map and the ground truth label data.

A learning device 10 includes a first learning unit 20. The first learning unit 20 includes a first supervised learning unit 22 and a first self-supervised learning unit 24. The first supervised learning unit 22 learns a first object detection network 30 using learning data 40 so as to reduce a first loss between an output of the first object detection network 30 for detecting an object from target image data and supervised data 40B. Using image data 40A and self-supervised data 40C generated from the image data 40A, the first self-supervised learning unit 24 learns the first object detection network 30 so as to reduce a second loss of a feature amount of a corresponding candidate area P between the image data 40A and the self-supervised data 40C, the second loss being derived by the first object detection network 30.

A learning device 10 includes a first learning unit 20. The first learning unit 20 includes a first supervised learning unit 22 and a first self-supervised learning unit 24. The first supervised learning unit 22 learns a first object detection network 30 using learning data 40 so as to reduce a first loss between an output of the first object detection network 30 for detecting an object from target image data and supervised data 40B. Using image data 40A and self-supervised data 40C generated from the image data 40A, the first self-supervised learning unit 24 learns the first object detection network 30 so as to reduce a second loss of a feature amount of a corresponding candidate area P between the image data 40A and the self-supervised data 40C, the second loss being derived by the first object detection network 30.