Abstract: Method and apparatus for image processing. A plurality of positive text exemplars is processed to generate a set of normal features using a trained model. A plurality of negative text exemplars is processed to generate a set of anomaly features using the trained model. A query image depicting an object is received. A query image feature for the query image is generated using the trained model. An anomaly score for the query image is generated based at least in part on determining one or more distances between the query image feature and one or more normal features of the set of normal features, and determining one or more distances between the query image feature and one or more anomaly features of the set of anomaly features.

Abstract: Detecting data anomalies by receiving a query image, determining a query image viewpoint according to a trained neural radiance field model, generating a 2D reference image according to the neural radiance field model, determining a difference between the query image and the reference image, and highlighting the difference in a presentation of the query image.

Abstract: A computing system, computer program product, and computer-implemented method for incremental learning without forgetting for a classification/detection model are provided. The method includes receiving, at a computing system, a classification/detection model including a base embedding space and corresponding base embedding vectors that are based on a base training dataset including base classes. The method also includes expanding the classification/detection model to account for a new training dataset including new classes by lifting the base embedding space to add an orthogonal subspace for the new classes, producing an expanded embedding space and corresponding expanded embedding vectors that are of a higher dimension than the base embedding vectors.

Abstract: An example system includes a processor that can randomly mask tokens using different masks to generate different subsets of masked tokens. The processor can process the different sets of masked tokens via a pretrained masked auto-encoder (MAE) encoder to output intermediate representations. The processor can process the intermediate representations via a pretrained MAE decoder to output reconstructed images. The processor can further compare input image with the output reconstructed images to generate an anomaly score.

Abstract: An example system includes a processor to receive detected chart regions in a page of a document. The processor is to produce, via a graphical elements detector, predicted heatmaps and bounding boxes for graphical objects in the detected chart regions. The processor is also to apply chart type specific analysis algorithm to the predicted heatmaps and bounding boxes, to extract tabular chart data. The processor can then generate an output data file and a visualization based on the predicted heatmap and the extracted tabular chart data.

Abstract: Method and apparatus for image processing. A plurality of positive text exemplars is processed to generate a set of normal features using a trained model. A plurality of negative text exemplars is processed to generate a set of anomaly features using the trained model. A query image depicting an object is received. A query image feature for the query image is generated using the trained model. An anomaly score for the query image is generated based at least in part on determining one or more distances between the query image feature and one or more normal features of the set of normal features, and determining one or more distances between the query image feature and one or more anomaly features of the set of anomaly features.

Abstract: Detecting data anomalies by receiving a query image, determining a query image viewpoint according to a trained neural radiance field model, generating a 2D reference image according to the neural radiance field model, determining a difference between the query image and the reference image, and highlighting the difference in a presentation of the query image.

Abstract: An example system includes a processor that can randomly mask tokens using different masks to generate different subsets of masked tokens. The processor can process the different sets of masked tokens via a pretrained masked auto-encoder (MAE) encoder to output intermediate representations. The processor can process the intermediate representations via a pretrained MAE decoder to output reconstructed images. The processor can further compare input image with the output reconstructed images to generate an anomaly score.

Abstract: An example a system includes a processor to receive a model that is a neural network and a number of training images. The processor can train the model using a bridge transform that converts the training images into a set of transformed images within a bridge domain. The model is trained using a contrastive loss to generate representations based on the transformed images.

Abstract: An example system includes a processor to receive detected chart regions in a page of a document. The processor is to produce, via a graphical elements detector, predicted heatmaps and bounding boxes for graphical objects in the detected chart regions. The processor is also to apply chart type specific analysis algorithm to the predicted heatmaps and bounding boxes, to extract tabular chart data. The processor can then generate an output data file and a visualization based on the predicted heatmap and the extracted tabular chart data.

Abstract: A computing system, computer program product, and computer-implemented method for incremental learning without forgetting for a classification/detection model are provided. The method includes receiving, at a computing system, a classification/detection model including a base embedding space and corresponding base embedding vectors that are based on a base training dataset including base classes. The method also includes expanding the classification/detection model to account for a new training dataset including new classes by lifting the base embedding space to add an orthogonal subspace for the new classes, producing an expanded embedding space and corresponding expanded embedding vectors that are of a higher dimension than the base embedding vectors.

Abstract: A method can include learning a common embedding space and a set of parameters for each one of a plurality of sets of mixture models, wherein one mixture model is associated with one class of objects within a set of object categories. The method can also include adding new mixture models to the set of mixture models to support novel categories based on a set of example embedding vectors computed for each one of the novel categories. Additionally, the method includes detecting in images a plurality of boxes with associated labels and corresponding confidence scores, wherein the boxes correspond to image regions comprising objects of both known categories and the novel categories. Furthermore, the method includes, given a query image, executing an instruction based on the common embedding space and the set of mixture models, the instruction comprising identifying objects from both categories in the query image.

Abstract: Embodiments of the present disclosure include training a model using a plurality of pairs of feature vectors related to a first class. Embodiments include providing a sample feature vector related to a second class as an input to the model. Embodiments include receiving at least one synthesized feature vector as an output from the model. Embodiments include training a classifier to recognize the second class using a training data set comprising the sample feature vector related to the second class and the at least one synthesized feature vector. Embodiments include providing a query feature vector as an input to the classifier. Embodiments include receiving output from the classifier that identifies the query feature vector as being related to the second class, wherein the output is used to perform an action.

Abstract: A method can include learning a common embedding space and a set of parameters for each one of a plurality of sets of mixture models, wherein one mixture model is associated with one class of objects within a set of object categories. The method can also include adding new mixture models to the set of mixture models to support novel categories based on a set of example embedding vectors computed for each one of the novel categories. Additionally, the method includes detecting in images a plurality of boxes with associated labels and corresponding confidence scores, wherein the boxes correspond to image regions comprising objects of both known categories and the novel categories. Furthermore, the method includes, given a query image, executing an instruction based on the common embedding space and the set of mixture models, the instruction comprising identifying objects from both categories in the query image.

Abstract: Embodiments of the present disclosure include training a model using a plurality of pairs of feature vectors related to a first class. Embodiments include providing a sample feature vector related to a second class as an input to the model. Embodiments include receiving at least one synthesized feature vector as an output from the model. Embodiments include training a classifier to recognize the second class using a training data set comprising the sample feature vector related to the second class and the at least one synthesized feature vector. Embodiments include providing a query feature vector as an input to the classifier. Embodiments include receiving output from the classifier that identifies the query feature vector as being related to the second class, wherein the output is used to perform an action.

Abstract: There is provided a method of identifying objects in an image, comprising: extracting query descriptors from the image, comparing each query descriptor with training descriptors for identifying matching training descriptors, each training descriptor is associated with a reference object identifier and with relative location data (distance and direction from a center point of a reference object indicated by the reference object identifier), computing object-regions of the digital image by clustering the query descriptors having common center points defined by the matching training descriptors, each object-region approximately bounding one target object and associated with a center point and a scale relative to a reference object size, wherein the object-regions are computed independently of the identifier of the reference object associated with the object-regions, wherein members of each cluster point toward a common center point, and classifying the target object of each object-region according to the referen

Abstract: There is provided a method of identifying objects in an image, comprising: extracting query descriptors from the image, comparing each query descriptor with training descriptors for identifying matching training descriptors, each training descriptor is associated with a reference object identifier and with relative location data (distance and direction from a center point of a reference object indicated by the reference object identifier), computing object-regions of the digital image by clustering the query descriptors having common center points defined by the matching training descriptors, each object-region approximately bounding one target object and associated with a center point and a scale relative to a reference object size, wherein the object-regions are computed independently of the identifier of the reference object associated with the object-regions, wherein members of each cluster point toward a common center point, and classifying the target object of each object-region according to the referen

Abstract: There is provided a method of identifying objects in an image, comprising: extracting query descriptors from the image, comparing each query descriptor with training descriptors for identifying matching training descriptors, each training descriptor is associated with a reference object identifier and with relative location data (distance and direction from a center point of a reference object indicated by the reference object identifier), computing object-regions of the digital image by clustering the query descriptors having common center points defined by the matching training descriptors, each object-region approximately bounding one target object and associated with a center point and a scale relative to a reference object size, wherein the object-regions are computed independently of the identifier of the reference object associated with the object-regions, wherein members of each cluster point toward a common center point, and classifying the target object of each object-region according to the referen

Abstract: There is provided a method of identifying objects in an image, comprising: extracting query descriptors from the image, comparing each query descriptor with training descriptors for identifying matching training descriptors, each training descriptor is associated with a reference object identifier and with relative location data (distance and direction from a center point of a reference object indicated by the reference object identifier), computing object-regions of the digital image by clustering the query descriptors having common center points defined by the matching training descriptors, each object-region approximately bounding one target object and associated with a center point and a scale relative to a reference object size, wherein the object-regions are computed independently of the identifier of the reference object associated with the object-regions, wherein members of each cluster point toward a common center point, and classifying the target object of each object-region according to the referen

Abstract: A method comprising: training a price tag detector, comprising a gross feature detector and a classifier, to automatically detect a price tag in an image, by: a) training the gross feature detector using supervised learning with labeled images, and b) training the classifier using a two-phase hybrid learning process comprising: c) applying an initial supervised learning using the labeled images, yielding a semi-trained version of the classifier, and d) applying a subsequent unsupervised learning using unlabeled images, yielding a fully trained version of the classifier, wherein applying the unsupervised learning comprises: for each unlabeled image: i) detecting multiple price tag hypotheses using the gross feature detector, ii) classifying each price tag hypothesis using the semi-trained classifier, ii) rating each classification based contextual data extracted from the unlabeled image, iv) retraining the semi-trained classifier with the rated classifications, and repeating steps ii) through iv) until the rec