In one example, an apparatus comprises: a first sensor layer, including an array of pixel cells configured to generate pixel data; and one or more semiconductor layers located beneath the first sensor layer with the one or more semiconductor layers being electrically connected to the first sensor layer via interconnects. The one or more semiconductor layers comprises on-chip compute circuits configured to receive the pixel data via the interconnects and process the pixel data, the on-chip compute circuits comprising: a machine learning (ML) model accelerator configured to implement a convolutional neural network (CNN) model to process the pixel data; a first memory to store coefficients of the CNN model and instruction codes; a second memory to store the pixel data of a frame; and a controller configured to execute the codes to control operations of the ML model accelerator, the first memory, and the second memory.

Disclosed is a content-based image retrieval (CBIR) system and related methods that serve as a diagnostic aid for diagnosing whether a dermoscopic image correlates to a skin cancer type. Systems and methods according to aspects of the invention use as a reference a set of images of pathologically confirmed benign or malignant past cases from a collection of different classes that are of high similarity to the unknown new case in question, along with their diagnostic profiles. Systems and methods according to aspects of the invention predict what class of skin cancer is associated with a particular patient skin lesion, and may be employed as a diagnostic aid for general practitioners and dermatologists.

Embodiments of the invention are directed to a system, method, or computer program product structured for generating resource identification strings to avoid electronic misreads. In some embodiments, the system is structured for generating a new technology resource string of characters, comparing the new string to existing technology resource strings, and determining whether the new string is the same as an existing string. The system is also structured for, in response to determining it is not, for each existing string, pairing characters of the strings and determining whether the strings have at least a threshold number of matching character pairs; if there are, for at least one of the existing strings, determining whether characters of the non-matching pairs are commonly misread characters and determining whether there are a threshold combination of matching/commonly misread pairs; and if there are, discard the new string and generate a second new technology resource string.

A method is provided for Optical Character Recognition (OCR). A plurality of OCR decoding results each having a plurality of positions is obtained from capturing and decoding a plurality of images of the same one or more OCR characters. A recognized character in each OCR decoding result is compared with the recognized character that occupies an identical position in each of the other OCR decoding results. A number of occurrences that each particular recognized character occupies the identical position in the plurality of OCR decoding results is calculated. An individual confidence score is assigned to each particular recognized character based on the number of occurrences, with a highest individual confidence score assigned to a particular recognized character having the greatest number of occurrences. Determining which particular recognized character has been assigned the highest individual confidence score determines which particular recognized character comprises a presumptively valid character for the identical position.

System receives historical text block, creates historical features for historical text block's historical text lines. System trains machine-learning model to cluster historical features into historical features clusters based on their similarities. System identifies historical features cluster as historical human text cluster. System classifies each historical text line for historical human text cluster as human text, and each historical text line for other historical features clusters as machine text. System receives text block, creates features for text block's text lines. System applies trained machine-learning model to cluster features into features clusters based on their similarities. System identifies features cluster as human text cluster. System classifies each text line for human text cluster as human text, and each text line for other features clusters as machine text. System applies human text analysis to each text line classified as human text and machine text analysis to each text line classified as machine text.

Embodiments of the invention are directed to a system, method, or computer program product structured for generating resource identification strings to avoid electronic misreads. In some embodiments, the system is structured for generating a new technology resource string of characters, comparing the new string to existing technology resource strings, and determining whether the new string is the same as an existing string. The system is also structured for, in response to determining it is not, for each existing string, pairing characters of the strings and determining whether the strings have at least a threshold number of matching character pairs; if there are, for at least one of the existing strings, determining whether characters of the non-matching pairs are commonly misread characters and determining whether there are a threshold combination of matching/commonly misread pairs; and if there are, discard the new string and generate a second new technology resource string.

An information processing apparatus includes a processor to input each sample image into feature extracting components to obtain at least two features of the sample image, and to cause a classifying component to calculate a classification loss of the sample image based on the at least two features; extract, from each pair of features, a plurality of sample pairs for calculating mutual information between each pair of features; input the plurality of sample pairs into a machine learning architecture corresponding to each pair of features, to calculate an information loss between each pair of features. The processor is to adjust parameters of the feature extracting components, the classifying component and the machine learning architecture by minimizing a sum of classification losses and information losses of sample images in the training set based upon the obtained at least two features of the sample image to calculate the classification losses and the information losses, to obtain the at least two feature extracting components and the classifying component having been trained.

Provided is an item circulation tracking processing method, in this method, an item is taken as a tracking object, and the whole process from purchase to use of an item on any household shopping receipt may be tracked, so as to achieve a full-flow automatic tracking processing of the item after entering the household, serve as a good helper for household item management, and provide a reliable reference for future consumption planning. Further provided are an item circulation tracking processing apparatus, device and a readable storage medium, which have the above beneficial effects, and are not described herein again.

Provided is an item circulation tracking processing method, in this method, an item is taken as a tracking object, and the whole process from purchase to use of an item on any household shopping receipt may be tracked, so as to achieve a full-flow automatic tracking processing of the item after entering the household, serve as a good helper for household item management, and provide a reliable reference for future consumption planning. Further provided are an item circulation tracking processing apparatus, device and a readable storage medium, which have the above beneficial effects, and are not described herein again.

A machine may be configured to perform image evaluation of images depicting items for sale and to provide recommendations for improving the images depicting the items to increase the sales of the items depicted in the images. For example, the machine accesses a result of a user behavior analysis. The machine receives an image of an item from a user device. The machine performs an image evaluation of the received image based on an analysis of the received image and the result of the user behavior analysis. The performing of the image evaluation may include determining a likelihood of a user engaging in a desired user behavior in relation to the received image. Then, the machine generates, based on the evaluation of the received image, an output that references the received image and indicates the likelihood of a user engaging in the desired behavior.