Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.

An information processing apparatus includes first and second display controllers and a receiver. The first display controller performs control to display a first character recognition result. The first character recognition result is a character recognition result of a first element. The first element and a second element have a specific dependency relationship and are included in a form. The receiver receives a checking/correcting result of checking and/or correcting the first character recognition result. The second display controller performs control to display information that the checking/correcting result and a second character recognition result, which is a character recognition result of the second element, do not satisfy the dependency relationship if the checking/correcting result and the second character recognition result do not satisfy the dependency relationship.

Methods and systems are described for generating dynamic conversational responses using two-tier machine learning models. The dynamic conversational responses may be generated in real time and reflect the likely goals and/or intents of a user. The two-tier machine learning model may include a first tier that determines an intent cluster based on a feature input, and a second tier that determines a specific intent from the cluster.

An object area measurement method and an apparatus are provided, relating to the computer vision and deep learning technology. The method includes acquiring an original image with a spatial resolution, the original image including a target object; acquiring an object identification model including at least two sets of classification models; generating one or more original image blocks based on the original image; performing operations on each original image block: scaling each original image block at at least two scaling levels to obtain scaled image blocks with at least two sizes, the scaled image blocks respectively corresponding to the at least two sets of classification models, and inputting the scaled image blocks into the object identification model to obtain an identification result of the target object; and determining an area of the target object based on the respective identification results of the one or more original image blocks and the spatial resolution.

An object of the invention is to provide an image learning device, an image learning method, a neural network, and an image classification device which can support appropriate classification of an image. In the image learning device according to an aspect of the invention, the neural network performs a first task of classifying a recognition target in a medical image and outputting a classification score as an evaluation result, and a second task different from the first task. The neural network updates a weight coefficient on the basis of a comparison result between the classification score output for the medical image of a first image group and a ground truth classification label, and does not reflect the classification score output for the medical image of a second image group in an update of the weight coefficient, for the first task. The neural network updates the weight coefficient on the basis of the evaluation result output for the medical image of the first image group and the evaluation result output for the medical image of the second image group, for the second task.

Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining an analysis profile comprising an initial number of analysis cycles dedicated to each of a plurality of detectors, where each detector is independently configured to detect objects according to a unique set of analysis parameters and/or a unique detector algorithm. The method also includes: receiving digital video data that depicts at least one object; analyzing the digital video data using some or all of the detectors in accordance with the analysis profile, where the analyzing produces an analysis result for each detector used in the analysis. Further, the method includes updating the analysis profile by adjusting the number of analysis cycles dedicated to at least one of the detectors based on the analysis results.

A technique for detecting a glitch in an image is provided. The technique includes providing an image to a plurality of individual classifiers to generate a plurality of individual classifier outputs and providing the plurality of individual classifier outputs to an ensemble classifier to generate a glitch classification.

A surveillance system is coupled to a plurality of sensor data sources arranged at locations within a plurality of regions of a site under surveillance. The surveillance system accesses a threat model that identifies contextual events classified as threats. The surveillance system identifies at least one contextual event for a site in real-time by processing sensor data generated by the sensor data sources, and co-occurring contextual data for at least one of the regions. Each identified contextual event is classified as one of a threat and a non-threat by using the threat model.

A valuation platform may receive, from a user device, video data associated with a vehicle, and may receive a vehicle history report of the vehicle based on a vehicle identification number of the vehicle. The valuation platform may receive, from the user device, feature data associated with the vehicle, and may process the video data, the vehicle history report, and the feature data, with a machine learning model, to determine one or more values for the vehicle. The valuation platform may determine a valuation for the vehicle based on the determined one or more values for the vehicle. The valuation platform may create a vehicle profile for the vehicle based on the video data, the vehicle history report, the feature data, the determined one or more values for the vehicle, and the valuation for the vehicle, and may perform one or more actions based on the vehicle profile.

The present invention relates to a method for acquiring an object information, the method comprising: obtaining an input image acquired by capturing a sea; obtaining a noise level of the input image; when the noise level indicates a noise lower than a predetermined level, acquiring an object information related to an obstacle included in the input image from the input image by using a first artificial neural network, and when the noise level indicates a noise higher than the predetermined level, obtaining a noise-reduced image of which the environmental noise is reduced from the input image by using a second artificial neural network, and acquiring an object information related to an obstacle included in the sea from the noise-reduced image by using the first artificial neural network.