Media processing systems and techniques are described. A media processing system receives image data that represents an environment captured by an image sensor. The media processing system receives an indication of an object in the environment that is represented in the image data. The media processing system divides the image data into regions, including a first region and a second region. The object is represented in one of the plurality of regions. The media processing system modifies the image data to obscure the first region without obscuring the second region based on the object being represented in the one of the plurality of regions. The media processing system outputs the image data after modifying the image data. In some examples, the object is depicted in the first region and not the second region. In some examples, the object is depicted in the second region and not the first region.

Automatic license plate recognition occurs when a light sensor that continually captures video detects motion as a vehicle is driven through a gate. The light sensor detects the vehicle and license plate in the video stream captured by the light sensor. An algorithm associated with the video stream of the light sensor is trained to detect license plates. The light sensor starts executing the recognition algorithm when it detects motion. Recognition of characters in the license plate is based upon an aggregation of several captured video frames in which a license plate is detected.

Systems and techniques for increasing the efficiency of a process of providing a resource by a resource provider are disclosed. In one example, a method detects a presence of a vehicle at a fuel dispenser, transmits an authorization request message automatically in response to detecting the presence of the vehicle, and automatically allows the fuel dispenser to dispense fuel to the vehicle.

Systems and techniques for increasing the efficiency of a process of providing a resource by a resource provider are disclosed. In one example, a method detects a presence of a vehicle at a fuel dispenser, transmits an authorization request message automatically in response to detecting the presence of the vehicle, and automatically allows the fuel dispenser to dispense fuel to the vehicle.

The system for recognizing one or more objects of a design creative within an environment, analyzing the one or more objects using a deep neural networking model and generating a modified design creative by (i) determining a location of a design creative within the media content, (iii) determining an object from the design creative, (iv) determining an attribute of the object, (v) implementing a compliance rule to the attribute of the object to determine a distinctness and an effectiveness of a brand product, (vi) generating an attention sequence and heatmap for the media content, (vii) automatically generating a first recommendation based on the compliance rule, the attention heatmap, and the attention sequence, and (viii) automatically generating a modified design creative for the environment based on the attention heatmap, the attention sequence and the generated first recommendation using the deep neural networking model.

An image content determination device includes at least one processor, in which the processor is configured to execute first recognition processing of recognizing a character and a face of a first person from a first image including the character and the face of the first person, execute first acquisition processing of acquiring first person-related information related to the first person included in the first image based on the recognized character and face of the first person, execute second recognition processing of recognizing a face of a second person from a second image including the face of the second person, and execute second acquisition processing of acquiring second person-related information related to the second person included in the second image, in which the second person-related information is acquired using the first person-related information corresponding to the first image including the face of the first person similar to the face of the second person.

A method for recognizing a text, an electronic device and a storage medium. An implementation of the method comprises: obtaining a multi-dimensional first feature map of a to-be-recognized image; performing, based on feature values in the first feature map, feature enhancement processing on each feature value in the first feature map; and performing a text recognition on the to-be-recognized image based on the first feature map after the enhancement processing.

A method for recognizing a text, an electronic device and a storage medium. An implementation of the method comprises: obtaining a multi-dimensional first feature map of a to-be-recognized image; performing, based on feature values in the first feature map, feature enhancement processing on each feature value in the first feature map; and performing a text recognition on the to-be-recognized image based on the first feature map after the enhancement processing.

This disclosure relates generally to visio-linguistic understanding. Conventional methods use contextual visio-linguistic reasoner for visio-linguistic understanding which requires more compute power and large amount of pre-training data. Embodiments of the present disclosure provide a method for visio-linguistic understanding using contextual language model reasoner. The method converts the visual information of an input image into a format that the contextual language model reasoner understands and accepts for a downstream task. The method utilizes the image captions and confidence score associated with the image captions along with a knowledge graph to obtain a combined input in a format compatible with the contextual language model reasoner. Contextual embeddings corresponding to the downstream task is obtained using the combined input. The disclosed method is used to solve several downstream tasks such as scene understanding, visual question answering, visual common-sense reasoning and so on.

This disclosure relates generally to visio-linguistic understanding. Conventional methods use contextual visio-linguistic reasoner for visio-linguistic understanding which requires more compute power and large amount of pre-training data. Embodiments of the present disclosure provide a method for visio-linguistic understanding using contextual language model reasoner. The method converts the visual information of an input image into a format that the contextual language model reasoner understands and accepts for a downstream task. The method utilizes the image captions and confidence score associated with the image captions along with a knowledge graph to obtain a combined input in a format compatible with the contextual language model reasoner. Contextual embeddings corresponding to the downstream task is obtained using the combined input. The disclosed method is used to solve several downstream tasks such as scene understanding, visual question answering, visual common-sense reasoning and so on.