A video recommendation method is provided, including: inputting a video to a first feature extraction network, performing feature extraction on at least one consecutive video frame in the video, and outputting a video feature of the video; inputting user data of a user to a second feature extraction network, performing feature extraction on the discrete user data, and outputting a user feature of the user; performing feature fusion based on the video feature and the user feature, and obtaining a recommendation probability of recommending the video to the user; and determining, according to the recommendation probability, whether to recommend the video to the user.

Disclosed is a content-based image retrieval (CBIR) system and related methods that serve as a diagnostic aid for diagnosing whether a dermoscopic image correlates to a skin cancer type. Systems and methods according to aspects of the invention use as a reference a set of images of pathologically confirmed benign or malignant past cases from a collection of different classes that are of high similarity to the unknown new case in question, along with their diagnostic profiles. Systems and methods according to aspects of the invention predict what class of skin cancer is associated with a particular patient skin lesion, and may be employed as a diagnostic aid for general practitioners and dermatologists.

An apparatus and method for compensating for an error of a vehicle sensor for enhancing performance for identifying the same object are provided. The apparatus includes a rotation angle error calculator that calculates a rotation angle error between sensor object information and sensor fusion object information. A position error calculator calculates a longitudinal and lateral position error between the sensor object information and the sensor fusion object information. A sensor error compensator calculates a sensor error based on the calculated rotation angle and a position error. In calculating the rotation angle error, the sensor error compensator corrects an error of the sensor object information based on the rotation angle error, and compensates for the sensor error based on the longitudinal and lateral position error between the corrected sensor object information and the sensor fusion object information.

A multimodal perception system for an autonomous vehicle includes a first sensor that is one of a video, RADAR, LIDAR, or ultrasound sensor, and a controller. The controller may be configured to, receive a first signal from a first sensor, a second signal from a second sensor, and a third signal from a third sensor, extract a first feature vector from the first signal, extract a second feature vector from the second signal, extract a third feature vector from the third signal, determine an odd-one-out vector from the first, second, and third feature vectors via an odd-one-out network of a machine learning network, based on inconsistent modality prediction, fuse the first, second, and third feature vectors and odd-one-out vector into a fused feature vector, output the fused feature vector, and control the autonomous vehicle based on the fused feature vector.

A system and a method for super-resolution image processing in remote sensing are disclosed. One or more sets of multi-temporal images with an input resolution and one or more first target images with a first output resolution are generated from one or more data sources. The first output resolution is higher than the input resolution. Each set of multi-temporal images is processed to improve an image match in the corresponding set of multi-temporal images. The one or more sets of multi-temporal images are associated with the one or more first target images to generate a training dataset. A deep learning model is trained using the training dataset. The deep learning model is provided for subsequent super-resolution image processing.

A face verification method and apparatus is disclosed. The face verification method includes selecting a current verification mode, from among plural verification modes, to be implemented for the verifying of the face, determining one or more recognizers, from among plural recognizers, based on the selected current verification mode, extracting feature information from information of the face using at least one of the determined one or more recognizers, and indicating whether a verification is successful based on the extracted feature information.

Various embodiments of the disclosure relate to an electronic device for controlling a predefined function based on a response time of an external electronic device on a user input, and a method thereof. The electronic device includes: a memory configured to store one or more applications; a communication module comprising communication circuitry configured to communicate with an external electronic device; and a processor, wherein the processor is configured to control the electronic device to: receive an input; generate first control data for controlling at least one application among the one or more applications using a first recognition method based at least on the input; transmit at least part of the input to the external electronic device through the communication module, wherein the external electronic device is configured to generate second control data for controlling the at least one application using a second recognition method based at least on the input; identify a time that passes until the second control data is received after the at least part of the input is transmitted to the external electronic device; control the at least one application using the first control data based on the passing time satisfying a first predefined condition; and control the at least one application using the second control data based on the passing time satisfying a second predefined condition.

There is disclosed in the present disclosure a seismic full horizon tracking method, a computer device and a computer-readable storage medium. The method includes: acquiring three-dimensional seismic data; extracting horizon extreme points from the three-dimensional seismic data to construct a sample space; equally dividing the sample space into a plurality of sub-spaces with overlapping portions, and performing a clustering process on the horizon extreme points in each sub-space to obtain horizon fragments corresponding to each horizon of the three-dimensional seismic data; establishing a topological consistency between the horizon fragments; and fusing the horizon fragments corresponding to each horizon of the three-dimensional seismic data based on the topological consistency, to obtain a full horizon tracking result of the three-dimensional seismic data. In the disclosure, a layer crossing phenomenon occurring in seismic full horizon tracking can be avoided, and a better full horizon tracking effect can be achieved.

Sensor fusion is performed for efficient deep learning processing. A camera image is received from an image sensor and supplemental sensor data is received from one or more supplemental sensors, the camera image and the supplemental sensor data including imaging of a cabin of a vehicle. Regions of interest in the camera image are determined based on one or more of the camera image or the supplemental sensor data, the regions of interest including areas of the camera image flagged for further image analysis. A machine-learning model is utilized to perform object detection on the regions of interest of the camera image to identify one or more objects in the camera image. The objects are placed into seating zones of the vehicle.

A mechanism is described for facilitating embedding of human labeler influences in machine learning interfaces in computing environments, according to one embodiment. A method of embodiments, as described herein, includes detecting sensor data via one or more sensors of a computing device, and accessing human labeler data at one or more databases coupled to the computing device. The method may further include evaluating relevance between the sensor data and the human labeler data, where the relevance identifies meaning of the sensor data based on human behavior corresponding to the human labeler data, and associating, based on the relevance, human labeler data with the sensor data to classify the sensor data as labeled data. The method may further include training, based on the labeled data, a machine learning model to extract human influences from the labeled data, and embed one or more of the human influences in one or more environments representing one or more physical scenarios involving one or more humans.