Abstract: A computer-implemented method for semantic segmentation includes constructing a co-occurrence table that includes co-occurrences of predictions of a pre-trained model for base classes and labels for novel classes from the pre-trained model for base classes and from training data with novel classes. Classifiers are trained that associated with a base class and that classify an input into the base class and one of the novel classes that have co-occurrences with the base class according to the co-occurrence. A prediction is fused from the pre-trained model and the trained classifiers to obtain a final prediction result as a fully labeled image.

Abstract: Embodiments of the invention are directed to a computer system including a memory communicatively coupled to a processor system, where the processor system is operable to perform processor system operations to predict an anomaly in a target domain (TD) dataset. The processor system operations include training a model to perform an anomaly prediction task on a TD. The training includes applying a transfer learning operation that includes learning to predict the anomaly based at least in part on a first source domain (SD) precision matrix computed from a first SD.

Abstract: A computer-implemented method for domain adaptation of an object detection model includes obtaining a domain vector for a domain from one or more images in the domain, the domain vector representing the property of the domain. The domain vector is input into a fully connected layers in the object detection model. A domain-specific result of the object detection model is provided as output. The method can further include computing a domain tensor and inputting the domain tensor into convolutional layers in the object detection model.

Abstract: One or more systems, devices, computer program products, and/or computer-implemented methods provided herein relate to accurate anomaly detection in images using patched features. According to an embodiment, an extraction component can extract multiple layers of features from one or more patches of an image using a pretrained convolutional neural network (CNN). A feature mapping component can concatenate the features from the multiple layers to generate a tensor feature map comprising a one-dimensional feature vector for respective patches. A cropping component can perform center cropping on the tensor feature map. A calculation component can calculate a distance to a feature distribution mean for respective patches.

Abstract: A computerized machine learning anomaly detection model trained on a plurality of samples of one or more source domains (optionally, one or more source domains and the target domain) is accessed. Using online deep sets, one or more domain vectors are computed for each target domain sample at an observation point, each target domain sample corresponding to a given target domain, where the one or more domain vectors represent a similarity and difference among the source and target domains. The target domain sample is processed using the anomaly detection model trained on the plurality of samples of the source to generate an anomaly score, the processing being based on the computed one or more domain vectors.

Abstract: A method includes receiving, by a computing device, acceleration information from an acceleration sensor attached to a product; receiving, by the computing device, from an image capture device an image of a prospective purchaser of the product; determining, by the computing device, a movement of the prospective purchaser based on the acceleration information and the image, the movement being in relation to a movement of the product; and sending, by the computing device, display information to a digital display, the display information including information related to the product. The movement of the product is based on the acceleration information.

Abstract: A method includes receiving, by a computing device, acceleration information from an acceleration sensor attached to a product; receiving, by the computing device, from an image capture device an image of a prospective purchaser of the product; determining, by the computing device, a movement of the prospective purchaser based on the acceleration information and the image, the movement being in relation to a movement of the product; and sending, by the computing device, display information to a digital display, the display information including information related to the product. The movement of the product is based on the acceleration information.