Insurance Services Office, Inc.

Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Siemens Corporation

Siemens Healthcare Gmbh

University of Colorado

Stevens Institute of Technology

Siemens Ag Osterreich

Siemesn Corporate Research, Inc.

Siemens Healthcare Diagnostics Gmbh

Maneesh Kumar Singh

Venkata Subbarao Veeravarasapu

Aurobrata Ghosh

Dorin Comaniciu

Visvanathan Ramesh

Kazunori Okada

Vinay Damodar Shet

Dongmian Zou

Zheng Zhong

Arun Krishnan

Terrance Edward Boult

Claus Bahlmann

Julian Yarkony

Khoi Nguyen

Aditya Singh

Anurag Ghosh

Ali Kamen

Andreas Hutter

Stefan Kluckner

Radu Victor Balan

Jan Neumann

Jan Ernst

Thorsten Köhler

Benedicte Bascle

Stephen Price Masticola

Gregory Baratoff

Bernhard Agthe

Lance Anthony Ladic

Xianjun Sam Zheng

Sushil Mittal

Vishnu Sai Rao Suresh Lokhande

Xiang Gao

Deepak Mittal

Shikha Bordia

Umut Akdemir

Venkata Subbarao Veeravasarapu

Abhishek Goel

Hani Altwaijry

Cristina Comaniciu

Mayank Rana

Steve Cruz

Mohit Bansal

Konrad Kording

Shaofei Wang

Sina Ditzel

Toufiq Parag

Yichen Jiang

Charles Dognin

Michael A Gall

Roberto Antonio Suarez

Sebastian Poelsterl

Machine learning (ML) systems and methods for fact extraction and claim verification are provided. The system receives a claim and retrieves a document from a dataset. The document has a first relatedness score higher than a first threshold, which indicates that ML models of the system determine that the document is most likely to be relevant to the claim. The dataset includes supporting documents and claims including a first group of claims supported by facts from more than two supporting documents and a second group of claims not supported by the supporting documents. The system selects a set of sentences from the document. The set of sentences have second relatedness scores higher than a second threshold, which indicate that the ML models determine that the set of sentences are most likely to be relevant to the claim. The system determines whether the claim includes facts from the set of sentences.

Machine learning systems and methods for many-hop fact extraction and claim verification

Systems and methods for detection and localization of image and document forgery. The method can include the step of receiving a dataset having a plurality of authentic images and a plurality of manipulated images. The method can also include the step of benchmarking a plurality of image forgery algorithms using the dataset. The method can further include the step of generating a plurality of receiver operating characteristic (ROC) curves for each of the plurality of image forgery algorithms. The method also includes the step of calculating a plurality of area under curve metrics for each of the plurality of ROC curves. The method further includes the step of training a neural network for image forgery based on the plurality of area under curve metrics.

Systems and methods for detection and localization of image and document forgery

Systems and methods for generating simulated scenes from open map data for machine learning are presented. The system includes an automatic scene generative pipeline that uses freely-available map information and random texture maps to create large-scale 3D urban scene layouts for supervised learning methods. The system generates synthetic datasets that have improved generalization capabilities with respect to a given target domain of interest using data from open maps and texture map from the same geographic locations. Data from the generation pipeline of the system improves a model's generalization to real image sets beyond arbitrarily-simulated sets or labeled real data from other geographical regions.

System and method for generating simulated scenes from open map data for machine learning

Computer vision systems and methods for automatic parcel alignment are provided. The system receives a geotagged aerial image, parcel information, and semantic information where each of the parcel and semantic information are overlaid on the geotagged aerial image. The system cleans the parcel information and the semantic information. The system optimizes the parcel information by grouping geo-registered parcels present in the geotagged aerial image into a plurality of islands and generating a plurality of parcel alignment solutions for each island of the plurality of islands. The system refines the plurality of parcel alignment solutions for each island and regularizes each island. The system generates a composite parcel alignment solution based on the refined plurality of parcel alignment solutions for each regularized island to align the geo-registered parcels of each regularized island with the geotagged aerial image.

Computer vision systems and methods for automatic alignment of parcels with geotagged aerial imagery

Computer vision systems and methods for end-to end training of neural networks are provided. The system generates a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem and trains the convolutional neural network and a conditional random field with the fixed point algorithm and a plurality of images of a dataset to learn to perform semantic image segmentation. The system can segment an attribute of an image of the dataset by the trained neural network and the conditional random field.

Computer vision systems and methods for end-to-end training of convolutional neural networks using differentiable dual-decomposition techniques

Computer vision systems and methods for object detection with reinforcement learning are provided. The system includes a reinforcement learning agent configured to detect an object pertaining to a target object class and a plurality of objects pertaining to different target object classes, such that the reinforcement learning agent determines a bounding box for each of the detected of objects. The system first sets parameters of the reinforcement learning agent. The system then detects an object and/or objects in an image based on the set parameters. Finally, the system determines a bounding box and/or bounding boxes for each of the detected objects.

Computer vision systems and methods for object detection with reinforcement learning

Computer vision systems and methods for text classification are provided. The system detects a plurality of text regions in an image and generates a bounding box for each detected text region. The system utilizes a neural network to recognize text present within each bounding box and classifies the recognized text, based on at least one extracted feature of each bounding box and the recognized text present within each bounding box, according to a plurality of predefined tags. The system can associate a key with a value and return a key-value pair for each predefined tag.

Computer vision systems and methods for information extraction from text images using evidence grounding techniques

A system for improved localization of image forgery. The system generates a variational information bottleneck objective function and works with input image patches to implement an encoder-decoder architecture. The encoder-decoder architecture controls an information flow between the input image patches and a representation layer. The system utilizes information bottleneck to learn useful residual noise patterns and ignore semantic content present in each input image patch. The system trains a neural network to learn a representation indicative of a statistical fingerprint of a source camera model from each input image patch while excluding semantic content thereof. The system can determine a splicing manipulation localization by the trained neural network.

Machine learning systems and methods for improved localization of image forgery

Computer vision systems and methods for optimizing correlation clustering for image segmentation are provided. The system receives input data and generates a correlation clustering formulation for Benders Decomposition for optimized correlation clustering of the input data. The system optimizes the Benders Decomposition for the generated correlation clustering formulation and performs image segmentation using the optimized Benders Decomposition.

Computer vision systems and methods for optimizing correlation clustering for image segmentation using Benders decomposition

Computer vision systems and methods for noisy contour alignment are provided. The system generates a loss function and trains a convolutional neural network with the loss function and a plurality of images of a dataset to learn to align contours with progressively increasing complex forward and backward transforms over increasing scales. The system can align an attribute of an image of the dataset by the trained neural network.

Computer vision systems and methods for unsupervised learning for progressively aligning noisy contours

A method of identifying an optimum treatment for a patient suffering from coronary artery disease, comprising: (i) providing patient information selected from: (a) status in the patient of one or more coronary disease associated biomarkers; (b) one or more items of medical history information selected from prior condition history, intervention history and medication history; (c) one or more items of diagnostic history, if the patient has a diagnostic history; and (d) one or more items of demographic data; (ii) aggregating the patient information in: (a) a Bayesian network; (b) a machine learning and neural network; (c) a rule-based system; and (d) a regression-based system; (iii) deriving a predicted probabilistic adverse event outcome for each intervention comprising percutaneous coronary intervention by placement of a bare metal stent, or a drug-coated stent; or by coronary artery bypass grafting; and (iv) determining the intervention having the lowest predicted probabilistic adverse outcome.

Method to identify optimum coronary artery disease treatment

Computer vision systems and methods for localizing image forgery are provided. The system generates a constrained convolution via a plurality of learned rich filters. The system trains a convolutional neural network with the constrained convolution and a plurality of images of a dataset to learn a low level representation of each image among the plurality of images. The low level representation is indicative of a statistical signature of at least one source camera model of each image. The system can determine a splicing manipulation localization by the trained convolutional neural network.

Computer vision systems and methods for blind localization of image forgery

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Lawrenceville, NJ, United States of America

Maneesh Kumar Singh