Processes of explainable active learning, for an object detector, by using a Bayesian dual encoder is provided. The processes include: (a) inputting test images into the object detector to generate cropped images, resizing the test images and the cropped images, and inputting the resized images into a data encoder to output data codes; (b) (b1) one of (i) inputting the test images into the object detector, applying Bayesian output embedding and resizing the activation entropy maps and the cropped activation entropy maps, and (ii) inputting resized object images and applying the Bayesian output embedding and (b2) inputting the resized activation entropy maps into a model encoder to output model codes; and (c) (i) confirming reference data codes, selecting specific test images as rare samples, and updating the data codebook, and (ii) confirming reference model codes and selecting specific test images as hard samples.

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 method for biometrics spoofing detection according to an embodiment of the present disclosure includes receiving a biometric authentication request from an application, acquiring biometrics at a sensor, and applying a machine learning-based anti-spoofing scheme to the biometrics based on an authentication purpose of the biometrics. The anti-spoofing scheme for biometrics of the present disclosure may include a deep neural network generated by machine learning, and may be used in an Internet of Things environment using a 5G network.

An example system includes a processor to receive raw and unlabeled videos. The processor is to extract speech from the raw and unlabeled videos. The processor is to extract positive frames and negative frames from the raw and unlabeled videos based on the extracted speech for each object to be detected. The processor is to extract region proposals from the positive frames and negative frames. The processor is to extract features based on the extracted region proposals. The processor is to cluster the region proposals and assign a potential score to each cluster. The processor is to train a binary object detector to detect objects based on positive samples randomly selected based on the potential score.

A system and method for vehicle occlusion detection is disclosed. A particular embodiment includes: receiving training image data from a training image data collection system; obtaining ground truth data corresponding to the training image data; performing a training phase to train a plurality of classifiers, a first classifier being trained for processing static images of the training image data, a second classifier being trained for processing image sequences of the training image data; receiving image data from an image data collection system associated with an autonomous vehicle; and performing an operational phase including performing feature extraction on the image data, determining a presence of an extracted feature instance in multiple image frames of the image data by tracing the extracted feature instance back to a previous plurality of N frames relative to a current frame, applying the first trained classifier to the extracted feature instance if the extracted feature instance cannot be determined to be present in multiple image frames of the image data, and applying the second trained classifier to the extracted feature instance if the extracted feature instance can be determined to be present in multiple image frames of the image data.

A method includes obtaining a first training data set including unannotated multi-dimensional medical images and executing a self-supervised masked image modeling (MIM) training process to pre-train an image encoder on the first training data set. The method also includes obtaining a second training data set that includes annotated multi-dimensional medical images. Here, each annotated multi-dimensional medical image includes a plurality of image voxels each paired with a corresponding ground-truth label indicating a class the corresponding image voxel belongs to. The method also includes executing a supervised training process to train an image analysis model on the second training data set to teach the image analysis model to learn how to predict the corresponding ground-truth labels for the plurality of image voxels of each annotated multi-dimensional medical image. The image analysis model incorporates the pre-trained image encoder.

A computer-implemented method of signal processing comprises providing images. The method comprises for each respective one of at least a subset of the images: applying a weakly-supervised learnt function, the weakly-supervised learnt function outputting respective couples each including a respective localization and one or more respective confidence scores, each confidence score representing a probability of instantiation of a respective object category at the respective localization. The method further comprises determining, based on the output of the weakly-supervised learnt function, one or more respective annotations, each annotation including a respective localization and a respective label representing instantiation a respective object category at the respective localization. The method further comprises forming a dataset including pieces of data, each piece of data including a respective image of the subset and at least a part of the one or more annotations determined for the respective image. This improves the field of object detection.

The disclosure relates to a method for operating a driver assistance system of a motor vehicle. The method includes detecting a first data set of sensor data measured by a sensor device of the driver assistance program. The first data set of sensor data includes missing class allocation information, wherein the class allocation information relates to the objects represented by the sensor data. The method also includes pre-training a classification algorithm of the driver assistance system while taking into consideration the first data set in order to improve the object differentiation of the classification algorithm. The method further includes generating a second data set of simulated sensor data which includes at least one respective piece of class allocation information according to a specific specification. The method also includes training the classification algorithm of the driver assistance system while taking into consideration the second data set in order to improve an allocation assignment of the classification algorithm for objects differentiated by the classification algorithm. The method further includes improving the detection of objects, which are represented by additional measured sensor data, by the driver assistance system.

This application relates to a tissue nodule detection and tissue nodule detection model training method, apparatus, device, storage medium and system. The method for training a tissue nodule detection model includes: obtaining source domain data and target domain data, the source domain data comprising a source domain image and an image annotation, the target domain data comprising a target image, and the image annotation being used for indicating location information of a tissue nodule in the source domain image; performing feature extraction on the source domain image using a neural network model to obtain a source domain sampling feature, performing feature extraction on the target image using the neural network model to obtain a target sampling feature, and determining a model result according to the source domain sampling feature using the neural network model; determining a distance parameter between the source domain data and the target domain data according to the source domain sampling feature and the target sampling feature, the distance parameter being a parameter describing a magnitude of a data difference between the source domain data and the target domain data; determining, according to the model result and the image annotation, a loss function value corresponding to the source domain image; and training the neural network model to obtain a tissue nodule detection model by iteratively reducing a combination of the loss function value and the distance parameter. In this way, the detection accuracy can be improved.

A computer-implemented method of signal processing comprises providing images. The method comprises for each respective one of at least a subset of the images: applying a weakly-supervised learnt function, the weakly-supervised learnt function outputting respective couples each including a respective localization and one or more respective confidence scores, each confidence score representing a probability of instantiation of a respective object category at the respective localization. The method further comprises determining, based on the output of the weakly-supervised learnt function, one or more respective annotations, each annotation including a respective localization and a respective label representing instantiation a respective object category at the respective localization. The method further comprises forming a dataset including pieces of data, each piece of data including a respective image of the subset and at least a part of the one or more annotations determined for the respective image. This improves the field of object detection.