The present disclosure relates to methods for estimating a cause of a defect in a semiconductor wafer. An example method includes acquiring contact model images including information of contact surfaces between manufacturing equipment and a wafer, receiving a defect image including defect information of a target wafer, generating, based on the contact model images and the defect image, partial representations of the contact model images that represent parts associated with the defect information, and determining, from the manufacturing equipment, suspicious equipment estimated to have caused the defect in the target wafer based on the defect image and the partial representations of the contact model images.

A method and system for validating promotional emails and product availability from E-commerce websites is disclosed. In one embodiment, the method includes retrieving a first set of images corresponding to an image strip and a second set of images corresponding to a promotional email from a database. The first set of images and the second set of images may be associated with one or more products. The method further includes calculating a similarity score between each of the first set of images and each of the second set of images using a first Computer Vision (CV) technique. The method further includes selecting one or more valid images from the second set of images based on the similarity score. The method further includes determining a stock availability status of at least one product presented in the one or more valid images from at least one website using a deep learning algorithm.

The information processing apparatus according to the present embodiment includes: a learning unit configured to train a model; a captured image acquisition unit configured to acquire a captured image; a reference image generation unit configured to generate a reference image; and an evaluation unit configured to evaluate the object based on a comparison between the reference image and the captured image, the learning unit includes a residual calculation unit configured to calculate a residual by comparing a generated image output from the model in a training course with a training image including the captured image, and a determination unit configured to determine that training of the model is completed when the residual satisfies a predetermined condition, and the residual calculation unit calculates a residual based on the differential image.

The method includes: receiving an image to be detected, wherein the image to be detected includes an object to be detected; detecting the image to be detected based on a trained multi-task network model to obtain a region where the object wears a mask; comparing an area of the region where the object wears the mask to a predetermined threshold to determine a first detection result; in response to the object being determined as wearing the mask, determining comparison information of a mask area to be compared, and determining a target region in the image to be detected corresponding to the comparison information based on a face feature point template; and obtaining a second detection result based on an overlapping degree between the target region and the region where the object wears the mask.

A device may receive cargo data associated with cargo, and may segment one or more objects identified in the cargo data to generate image segments. The device may process the image segments, with a first model, to determine a first stability of the one or more objects, and may process the image segments, with a second model, to determine a second stability of the one or more objects. The device may combine the first stability and the second stability to generate a third stability, and may utilize a large language model with the image segments and one of the first stability, the second stability, or the third stability to generate a description of the one or more objects. The device may perform one or more actions based on one or more of the description, the first stability, the second stability, or the third stability.

A computing device to authenticate works of art comprises a processor programmed to receive test image data corresponding to an image of a test painting to be authenticated; receive a plurality of first artist image data files; receive a plurality of multiple artist image data files; generate a plurality of test painting tiles from the test image data file; generate a plurality of groups of first artist painting tiles; generate a plurality of groups of multiple artist painting tiles; train a classifier to determine one of a plurality of classes for each first artist painting tile and each multiple artist painting tile; use the trained classifier to determine the class for each test painting tile; and determine whether the test painting was likely painted by the first artist according to a percentage of the test painting tiles determined to be the class corresponding to the first artist.

A neural network, trained for the task of deriving the attribution of image regions that significantly influence classification in a tool for pathology classification, comprising (i) a contracting branch, (ii) an attenuation module, (iii) an interconnected upsampling branch, and (iv) a final mapping module.

Example implementations include a method, apparatus and computer-readable medium for computer vision detection of a fall event, comprising detecting a person in a first image captured at a first time. The implementations further include identifying a plurality of keypoints on the person in an image, wherein the plurality of keypoints, when connected, indicate a pose of the person. Additionally, the implementations further include detecting, using the plurality of keypoints, that the person has fallen in response to determining that, subsequent to the pose being the standing pose in a previous image, the keypoints of the plurality of keypoints associated with the shoulders of the person are higher than the keypoints of the second plurality of keypoints associated with the eyes and the ears of the person in the second image. Additionally, the implementations further include generating an alert indicating that the person has fallen.