A feature extractor and novel training objective are provided for content-based image retrieval. For example, a computer-implemented method includes applying a query image and a search image to a neural network of a feature extraction network of a computing device, the query image indicating an object to be searched for in the search image. The feature extraction network includes the neural network, a spatial feature neural network receiving a first output of the neural network pertaining to the search image, and an embedding network receiving a second output of the neural network pertaining to the query image. The method includes generating spatial search features from the spatial feature neural network, generating a query feature from the embedding network, applying the query feature to an artificial neural network (ANN) index, and determining an optimal matching result of an object in the search image based on an operation using the ANN index.

Systems and methods for construction zone segmentation are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes construction zones scenes having various objects. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.

A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

A system and method for training a text report identification machine learning model and an image identification machine learning model, including: initially training a text report machine learning model, using a labeled set of text reports including text pre-processing the text report and extracting features from the pre-processed text report, wherein the extracted features are input into the text report machine learning model; initially training an image machine learning model, using a labeled set of images; applying the initially trained text report machine learning model to a first set of unlabeled text reports with associated images to label the associated images; selecting a first portion of labeled associated images; re-training the image machine learning model using the selected first portion of labeled associated images; applying the initially trained image machine learning model to a first set of unlabeled images with associated text reports to label the associated text reports; selecting a first portion of labeled associated text reports; and re-training the text report machine learning model using the selected first portion of labeled associated text reports.

An electronic device and a controlling method thereof are provided. A controlling method of an electronic device according to the disclosure includes: performing first learning for a neural network model for acquiring a video sequence including a talking head of a random user based on a plurality of learning video sequences including talking heads of a plurality of users, performing second learning for fine-tuning the neural network model based on at least one image including a talking head of a first user different from the plurality of users and first landmark information included in the at least one image, and acquiring a first video sequence including the talking head of the first user based on the at least one image and pre-stored second landmark information using the neural network model for which the first learning and the second learning were performed.

A method for improving performance of a local device based on guide data from a remote device, according to one embodiment of the present disclosure, includes transmitting, to the remote device, first image data generated by the local device at a first time point, receiving guide data related to the first image data from the remote device, and registering, by a processor, the guide data to second image data generated by the local device at a second time point, based on first spatial information on the first image data, wherein the second time point is a time point that is after the first time point. A trained model for object recognition according to the present disclosure may include a deep neural network generated through machine learning, and the transmitting of the guide data may be performed in an Internet of Things (IoT) environment using a 5G network.

An infrared image sequence-based sleep quality evaluation system and method. The method comprises: obtaining a plurality of respiratory infrared image sequences to be evaluated, one respiratory infrared image sequence comprising a plurality of respiratory infrared image frames to be evaluated; performing sleep quality evaluation on each respiratory infrared image sequence in the plurality of respiratory infrared image sequences by means of a classifier to obtain a sleep quality evaluation result corresponding to each respiratory infrared image sequence; and counting the number of different sleep quality evaluation results according to the sleep quality evaluation results respectively corresponding to the plurality of respiratory infrared image sequences, and determining the sleep quality evaluation result with the largest number as a sleep quality evaluation result of a user. Contactless sleep monitoring can be carried out on a user, monitoring costs are reduced at the same time, and evaluation accuracy of sleep quality is improved.

A method includes obtaining an image of a scene and identifying one or more labels for one or more objects captured in the image. The method also includes generating one or more domain-specific augmented images by modifying the image, where the one or more domain-specific augmented images are associated with the one or more labels. In addition, the method includes training or retraining a machine learning model using the one or more domain-specific augmented images and the one or more labels. Generating the one or more domain-specific augmented images may include at least one of modifying the image to include a different amount of motion blur, modifying the image to include a different lighting condition, and modifying the image to include a different weather condition.

Systems, apparatuses, and methods include technology that generates final frame predictions for a first plurality of frames of a video, where the first plurality of frames is associated with unlabeled data. The technology predicts an ordered list of actions for the first plurality of frames based on the final frame predictions, and temporally aligning the ordered list of actions to the final frame predictions to generate labels.

A computer vision system configured for detection and recognition of objects in video and still imagery in a live or historical setting uses a teacher-student object detector training approach to yield a merged student model capable of detecting all of the classes of objects any of the teacher models is trained to detect. Further, training is simplified by providing an iterative training process wherein a relatively small number of images is labeled manually as initial training data, after which an iterated model cooperates with a machine-assisted labeling process and an active learning process where detector model accuracy improves with each iteration, yielding improved computational efficiency. Further, synthetic data is generated by which an object of interest can be placed in a variety of setting sufficient to permit training of models. A user interface guides the operator in the construction of a custom model capable of detecting a new object.