Methods, apparatus, systems, and storage medium for a face liveness detection are provided. The method includes obtaining an image comprising a face of an object; extracting an image feature of the image through an encryption network in a joint model for encryption and detection; performing image reconstruction based on the image feature to obtain an encrypted image corresponding to the image, the encrypted image being different in image content from the image; transmitting the encrypted image to a liveness detection server, wherein the liveness detection server is configured to perform liveness detection on the encrypted image through a detection network in the joint model for encryption and detection to obtain a liveness detection result of the object in the image; and receiving the liveness detection result of the object in the image from the liveness detection server.

An object detection model training apparatus, method and non-transitory computer readable storage medium thereof are provided. The apparatus performs a first object detection on a plurality of training images to generate a piece of first label information corresponding to each of the training images by a first teacher model. The apparatus trains a student model based on the training images and the first label information. The apparatus performs a second object detection on the training images to generate a piece of second label information corresponding to each of the training images by a second teacher model. The apparatus trains the student model based on the training images and the second label information.

A system for correlating image data includes a memory configured to store a sequence of images of a sample. The system also includes a processor operatively coupled to the memory and configured to crop a first pair of images to specify a region of interest in the first pair of images, where at least one image in the pair of images is from the sequence of images. The processor is also configured to calculate, using a first convolutional neural network, a displacement field for the first pair of images. The processor is also configured to calculate, using a second convolutional neural network, a strain field for the first pair of images. The processor is further configured to determine an amount of displacement or deformation of the sample based at least in part on the displacement field and the strain field.

A learning device performs learning a facial recognition model so as to further reduce a triplet loss that uses a first facial image, a second facial image that is a candidate for an adversarial example of a same person as the first facial image, and a third facial image that is a candidate for an adversarial example of a different person than the first facial image.

A generative adversarial network. The generative adversarial network includes: a generator configured for generating an image and a corresponding label map; a discriminator configured for determining a classification of a provided image and a provided label map, wherein the classification characterizes whether the provided image and the provided label map have been generated by the generator or not and determining the classification comprises the steps of: determining a first feature map of the provided image; masking the first feature map according to the provided label map thereby determining a masked feature map; globally pooling the masked feature map thereby determining a feature representation of the provided image masked by the provided label map; determining a classification of the image based on the feature representation.

An image classification method comprises: extracting a logic program from a CNN, trained to classify features in images, which is a symbolic approximation of outputs of kernels at an extraction layer of the CNN; deriving kernel-based classification rules; forward-propagating pairs of feature-labeled images through the logic program to obtain kernel activations at the extraction layer for features in the images, where the scene in one of the pair contains a particular feature and the other is of the same scene without the feature; and calculating the correlation between each kernel in the logic program and each feature in the feature-labeled images using the kernel activations and the features associated with the feature-labeled images, assigning to each kernel in the logic program the label of the feature with which the kernel has the highest correlation, and applying the assigned kernel labels to the kernels in the rules to obtain kernel-labeled rules.

The disclosure relates to a processing method for generating learning data, which may include: specifying requirement information for generating learning data, based on request information for making a request for learning; and transmitting the requirement information to a device that generates the learning data. The disclosure also relates to a system and a program that realize the method, and a storage medium that stores the program.

Systems and methods for acquiring and inspecting lens images of ophthalmic lenses using one or more cameras to acquire the images of the lenses in a dry state or a wet state. The images are preprocessed and then inputted into an artificial intelligence network, such as a convolutional neural network (CNN), to analyze and characterize for type of lens defects. The artificial intelligence network identifies defect regions on the images and output defect categories or classifications for each of the images based in part on the defect regions.

An image processing method according to the invention includes obtaining a ground truth image teaching a cell region occupied by a cell in an original image for each of a plurality of the original images obtained by bright-field imaging of the cell, generating a reverse image by reversing luminance of the original image at least for the cell region based on each original image, and constructing a classification model by performing machine learning using a set of the original image and the ground truth image corresponding to the original image and a set of the reverse image and the ground truth image corresponding to the original image as a basis of the reverse image respectively as training data.

A semi-supervised learning method for object detection in an autonomous vehicle and a device for performing semi-supervised learning for object detection in an autonomous vehicle can include receiving, by a server, no-label voxel data from a vehicle, performing, by the server, a data-based update on a server object detection model on the basis of label voxel data and the no-label voxel data, determining, by the server, a loss value on the basis of the label voxel data and the no-label voxel data, and performing, by the server, a loss-based update on the server object detection model using the loss value.