A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects). A great variety of other features and arrangements—some involving designing classifiers so as to combat classifier copying—are also detailed.

A learning-based model is trained using a plurality of attributes of media. Depth estimation is performed using the learning-based model. The depth estimation supports performing a computer vision task on the media. Attributes used in the depth estimation include scene understanding, depth correctness, and processing of sharp edges and gaps. The media may be processed to perform media restoration or the media quality enhancement. A computer vision task may include semantic segmentation.

An annotation method and device and a storage medium are provided. The annotation method includes operations as follows. A first probability value that a first sample image is annotated with an Nth tag when the first sample image is annotated with an Mth tag is determined based on first tag information of a first image set. M and N are unequal and are positive integers. The first probability value is added to second tag information of a second sample image annotated with the Mth tag in a second image set.

A system includes a cloud computing system having a server, and a user environment computing system having an edge electronic device and a group of edge side sensors installed on at least one of inside and outside of the edge electronic device, the cloud computing system and the user environment computing system connected via a network line. The user environment computing system transfers a plurality of kinds of detection data collected by the group of edge side sensors to the cloud computing system for learning of an inference model generated by the server. When detection data is newly obtained from one sensor out of the group of edge side sensors and the detection data newly obtained is related to the inference model, the detection data newly obtained is transferred to the server for additional learning of the inference model.

A method for self-supervised learning is described. The method includes generating a plurality of augmented data from unlabeled image data. The method also includes generating a population augmentation graph for a class determined from the plurality of augmented data. The method further includes minimizing a contrastive loss based on a spectral decomposition of the population augmentation graph to learn representations of the unlabeled image data. The method also includes classifying the learned representations of the unlabeled image data to recover ground-truth labels of the unlabeled image data.

A system for dynamic conditional guidance using artificial intelligence. The system includes a computing device, designed and configured to c calculate a diagnostic output using a biological extraction related to a user, and a first machine-learning process, wherein the diagnostic output identifies a prognostic label and an ameliorative label; classify, using a physiological classifier and a first classification algorithm, the diagnostic output to a physiological state for the user; generate a vector output for the physiological state for the user, using a clustering algorithm; receive a user input generated in response to the diagnostic output; update the vector output using the user input; and identify a recommendation for the user, utilizing the updated vector output.

Methods, systems, and media for discriminating and generating translated images are provided. In some embodiments, the method comprises: identifying a set of training images, wherein each image is associated with at least one domain from a plurality of domains; training a generator network to generate: i) a first fake image that is associated with a first domain; and ii) a second fake image that is associated with a second domain; training a discriminator network, using as inputs to the discriminator network: i) an image from the set of training images; ii) the first fake image; and iii) the second fake image; and using the generator network to generate, for an image not included in the set of training images at least one of: i) a third fake image that is associated with the first domain; and ii) a fourth fake image that is associated with the second domain.

A computing device obtains information about a medical slide image, and determines a dataset type of the medical slide image and a panel of the medical slide image. The computing device assigns to an annotator account, an annotation job defined by at least the medical slide image, the determined dataset type, an annotation task, and a patch that is a partial area of the medical slide image. The annotation task includes the determined panel, and the panel is designated as one of a plurality of panels including a cell panel, a tissue panel, and a structure panel. The dataset type indicates a use of the medical slide image and is designated as one of a plurality of uses including a training use of a medical learning model and a validation use of the machine learning model.

Systems and methods applicable, for instance, to training machine learning models (MLMs). Training of a multihead classifier MLM can utilize a two term loss function. A first term of the loss function can be used to reward each of the heads for the extent to which it properly predicts labels of the labeled training data instances. A second term of the loss function can reward each of the heads for the extent to which it disagrees with each of the other heads in terms of predicting labels. As such, the MLM can both predict proper labels for the labeled training data instances and be distinct on the unlabeled instances.

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.