An artificial intelligence findings system includes a findings engine that receives medical image data and generates findings based on the medical image data and image interpretation algorithms. An adjustment engine allows the user to adjust the findings to produce a report. A tracking module tracks findings and adjustments made to the findings by the user when producing the report. The tracking module produces tracking information. A machine learning engine receives the tracking information.

A deep learning based compression (DLBC) system generates a progressive representation of the encoded input image such that a client device that requires the encoded input image at a particular target bitrate can readily be transmitted the appropriately encoded data. More specifically, the DLBC system computes a representation that includes channels and bitplanes that are ordered based on importance. For a given target rate, the DLBC system truncates the representation according to a trained zero mask to generate the progressive representation. Transmitting a first portion of the progressive representation enables a client device with the lowest target bitrate to appropriately playback the content. Each subsequent portion of the progressive representation allows the client device to playback the content with improved quality.

An information processing apparatus includes a processor configured to acquire a first recognition result and a first recognition probability on target data from a first recognizer, acquire a second recognition result and a second recognition probability on the target data from a second recognizer, execute checking of the first recognition result and the second recognition result, and execute first control in a case where the first recognition result and the second recognition result match each other as a result of the checking. The first control is control for executing either of first processing or second processing on the matched recognition result and outputting a processing result based on at least one of the first recognition probability or the second recognition probability. A human workload for the first processing is smaller than a human workload for the second processing.

A document processing framework (DPF) for robotic process automation (RPA) is provided. The DPF may allow plug-and-play use of different vendor products on same platform, where users can setup a basic schema for document processing and document understanding workflow. The DPF may allow users to define a taxonomy, digitize a file, classify the file into one or more document types, validate the classification, extract data, validate the extracted data, train classifiers, and/or train extractors. A public package may be provided that can be used by software developers to manage the DPF and build their own classifier and extractor components.

A method and device for object detection, and a non-transitory computer readable storage medium are provided. The method includes the following. Object detection is performed on images of at least one second domain with a neural network to obtain detection results, where the neural network is trained with a first image sample set for a first domain. For at least one image among the images of the at least one second domain of which the detection result has a confidence level that is lower than a first threshold, the at least one image is assigned as an image sample in at least one second image sample set. At least one image sample is selected from the first image sample set and at least one image sample is selected from each of the at least one second image sample set.

A computer vision (CV) training system, includes: a supervised learning system to estimate a supervision output from one or more input images according to a target CV application, and to determine a supervised loss according to the supervision output and a ground-truth of the supervision output; an unsupervised learning system to determine an unsupervised loss according to the supervision output and the one or more input images; a weakly supervised learning system to determine a weakly supervised loss according to the supervision output and a weak label corresponding to the one or more input images; and a joint optimizer to concurrently optimize the supervised loss, the unsupervised loss, and the weakly supervised loss.

The present disclosure relates to systems and methods for automatically identifying and verifying vehicle license plate data. Specifically, the inventive system utilizes multiple automated data points to correlate a more accurate read of a license plate taken on roadways than systems that rely solely on Optical Character Recognition. The inventive system utilizes machine learning to automatically determine the make and model of the vehicle, which is then matched against motor vehicle records to provide for automation in the 80-90% range. The inventive system also utilizes analytics to determine if there are issues with the cameras and provides for near real time alerts to maintenance personnel.

The present disclosure relates to systems and methods for automatically identifying and verifying vehicle license plate data. Specifically, the inventive system utilizes multiple automated data points to correlate a more accurate read of a license plate taken on roadways than systems that rely solely on Optical Character Recognition. The inventive system utilizes machine learning to automatically determine the make and model of the vehicle, which is then matched against motor vehicle records to provide for automation in the 80-90% range. The inventive system also utilizes analytics to determine if there are issues with the cameras and provides for near real time alerts to maintenance personnel.

An auto-learning semantic method and system interprets content by applying neural networks and then generates and/or updates generalized semantic chains based upon the interpretations. The generalized semantics chains have associated weightings or probabilities and the semantic chains can comprise generalizations related to categorization and causation. Automatic learning occurs as the system assess the probabilities associated with the semantic chains and focuses its attention accordingly with the intent of increasing its confidence of its generalizations. The auto-learned generalizations are applied in generating communications that may be directed internally to the system as well as externally.

An information processing apparatus comprising: at least one processor programmed to cause the apparatus to: hold label information regarding presence of a target object, the label information being set for the target object in an image; obtain a reliability of the label information; cause a display apparatus to display the label information and an image corresponding to the label information in the image, based on the reliability; accept an operation made by a user; and modify the label information based on the operation.