Systems and methods are provided for relocating an overlay overlapping information in content. The systems and methods may comprise receiving a content item, the content item comprising a video image, and determining a first screen position of an information box (e.g., a score box) in the video image. Determining may be performed with image analysis and/or a machine learning model. The system receives an overlay image (e.g., a channel logo) with a second screen position and determines if the second screen position (e.g., for the logo) overlaps the first screen position (e.g., for the score). In response to determining the second screen position (e.g., of the logo) overlaps the first screen position (e.g., the score), the system modifies the second screen position (e.g., for the logo). Then the system generates for display the overlay image on the video in the modified screen position. The system may not relocate the overlay if the overlay is a high priority.

Systems and methods for classifying images (e.g., ads) are described. An image is accessed. Optical character recognition is performed on at least a first portion of the image. Image recognition is performed via a convolutional neural network on at least a second portion of the image. At least one class for the image is automatically identified, via a fully connected neural network, based on one or more predictions, each of the one or more predictions being based on both the optical character recognition and the image recognition. Finally, the at least one class identified for the image is output.

A computer-implemented method can include receiving, by a computing system including one or more computing devices, data describing a media content item that includes a plurality of image frames for sequential display. The method can include inputting, by the computing system, the data describing the media content item into a machine-learned temporal analysis model that is configured to receive the data describing the media content item, and in response to receiving the data describing the media content item, output temporal analysis data that describes temporal information associated with sequentially viewing the plurality of image frames of the media content item. The method can include receiving, by the computing system and as an output of the machine-learned temporal analysis model, the temporal analysis data.

A manufacturing data analyzing method and a manufacturing data analyzing device are provided. The manufacturing data analyzing method includes the following steps. Each of at least one numerical data, at least one image data and at least one text data is transformed into a vector. The vectors are gathered to obtain a combined vector. The combined vector is inputted into an inference model to obtain a defect cause and a modify suggestion.

A method for identifying a logo within at least one image includes identifying an area containing the logo within the at least one image, extracting logo features from the area by analyzing image gradient vectors associated with the at least one image, and using a machine learning model to identify the logo from the extracted logo features, wherein the machine learning model is trained to identify at least one target logo based on a received image data containing the logo features.

One or more highlights of a video stream may be identified. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. According to one method, at least a portion of the video stream may be stored. The portion of the video stream may be compared with templates of a template database to identify the one or more highlights. Each highlight may be a subset of the video stream that is deemed likely to match the one or more templates. The highlights, an identifier that identifies each of the highlights within the video stream, and/or metadata pertaining particularly to the one or more highlights may be stored to facilitate playback of the highlights for the users.

A term weight determination method includes: obtaining a video and video-associated text, the video-associated text including at least one term; generating a halfway vector of the term by performing multimodal feature fusion on the features of the video, the video-associated text and the at least one term; and generating the weight of the at least one term based on the halfway vector of the at least one term.

In various embodiments, methods and systems for implementing a media management system, for video data processing and adaptation data generation, are provided. At a high level, a video data processing engine relies on different types of video data properties and additional auxiliary data resources to perform video optical character recognition operations for recognizing characters in video data. In operation, video data is accessed to identify recognized characters. A video OCR operation to perform on the video data for character recognition is determined from video character processing and video auxiliary data processing. Video auxiliary data processing includes processing an auxiliary reference object; the auxiliary reference object is an indirect reference object that is a derived input element used as a factor in determining the recognized characters. The video data is processed based on the video OCR operation and based on processing the video data, at least one recognized character is communicated.

A method for exchanging data between an external data source for annotations and an integrated medical data display system, comprises: determining information displayed on a screen of the integrated medical data display system by capturing the screen; and performing at least one of (i) selecting data from the external data source assigned to the determined information and displaying the selected data complementary, or (ii) extracting annotations from the integrated medical data display system based on the determined information and appending the extracted annotations to the external data source for annotations.

This disclosure relates to a method for recognizing multimedia content. The method includes: obtaining target text information and content information in a video; performing text recognition processing on the content information to obtain associated text information; when the original text information or the associated text information meets a first malicious promotion condition, obtaining a target text classification result by a text classification model; and determining a video recognition result corresponding to the video according to the target text classification result.