A method for optimizing an image classification model can include determining a first image classification model based on initial training data; in response to model optimization, determining a second image classification model based on the first image classification model and a noise data set; and obtaining a third image classification model by optimizing the second image classification model based on the initial training data, the third image classification model being configured to update the noise data set based on noise data generated within a predetermined time period and the noise data set.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using an image processing neural network system. One of the system includes a shared encoder neural network implemented by one or more computers, wherein the shared encoder neural network is configured to: receive an input image from a target domain; and process the input image to generate a shared feature representation of features of the input image that are shared between images from the target domain and images from a source domain different from the target domain; and a classifier neural network implemented by the one or more computers, wherein the classifier neural network is configured to: receive the shared feature representation; and process the shared feature representation to generate a network output for the input image that characterizes the input image.

An image object recognition method, apparatus, and computer device are provided. The image object recognition method includes: performing feature extraction in the direction of a horizontal angle of view and in the direction of a vertical angle of view of an image respectively, to extract a lateral feature sequence and a longitudinal feature sequence of the image; fusing the lateral feature sequence and the longitudinal feature sequence to obtain a fused feature; activating the fused feature by using a preset activation function to obtain an image feature; and recognizing an object in the image by decoding the image feature. This solution can improve the efficiency of the object recognition.

An image object recognition method, apparatus, and computer device are provided. The image object recognition method includes: performing feature extraction in the direction of a horizontal angle of view and in the direction of a vertical angle of view of an image respectively, to extract a lateral feature sequence and a longitudinal feature sequence of the image; fusing the lateral feature sequence and the longitudinal feature sequence to obtain a fused feature; activating the fused feature by using a preset activation function to obtain an image feature; and recognizing an object in the image by decoding the image feature. This solution can improve the efficiency of the object recognition.

A missing data compensation method, missing data compensation system and non-transitory computer-readable medium are provided in this disclosure. The method includes the following operations: inputting a sensing signal by a sensor; searching for a historical data sections similar to a first data section from the plurality of historical data sections to generate a plurality of candidate data sections; calculating a plurality of data relation diagrams according to the first data section and the candidate data sections, respectively; utilizing a feature recognition model to calculate a plurality of similarity values according to the data relation diagrams; selecting a candidate data section corresponding to the maximum similarity value as a sample data section; and utilizing the data in the sample data section to compensate the data in the first data section to generate compensated data section.

Some examples herein describe time-series recognition and analysis techniques with computer vision. In one example, a system can access an image depicting data lines representing time series datasets. The system can execute a clustering process to assign pixels in the image to pixel clusters. The system can generate image masks based on attributes of the pixel clusters, and identify a respective set of line segments defining the respective data line associated with each image mask. The system can determine pixel sets associated with the time series datasets based on the respective set of line segments associated with each image mask, and provide one or more pixel sets as input for a computing operation that processes the pixel sets and returns a processing result. The system may then display the processing result on a display device or perform another task based on the processing result.

Some examples herein describe time-series recognition and analysis techniques with computer vision. In one example, a system can access an image depicting data lines representing time series datasets. The system can execute a clustering process to assign pixels in the image to pixel clusters. The system can generate image masks based on attributes of the pixel clusters, and identify a respective set of line segments defining the respective data line associated with each image mask. The system can determine pixel sets associated with the time series datasets based on the respective set of line segments associated with each image mask, and provide one or more pixel sets as input for a computing operation that processes the pixel sets and returns a processing result. The system may then display the processing result on a display device or perform another task based on the processing result.

The embodiments of the application disclose a method for detecting a game prop in a game region, a device, and a storage medium. The method includes that: an image frame sequence collected from a game region at a game prop operating stage is acquired, the image frame sequence including a first preset frame number of game images and the first preset frame number being more than or equal to 2; target detection is performed on each frame of game image in the image frame sequence to obtain a first set of recognition results belonging to the same game prop, each recognition result at least including a confidence of the game prop; and reliability of the first set of recognition results of the game prop is determined based on all confidences in the first set of recognition results of the game prop and a confidence threshold.

The embodiments of the application disclose a method for detecting a game prop in a game region, a device, and a storage medium. The method includes that: an image frame sequence collected from a game region at a game prop operating stage is acquired, the image frame sequence including a first preset frame number of game images and the first preset frame number being more than or equal to 2; target detection is performed on each frame of game image in the image frame sequence to obtain a first set of recognition results belonging to the same game prop, each recognition result at least including a confidence of the game prop; and reliability of the first set of recognition results of the game prop is determined based on all confidences in the first set of recognition results of the game prop and a confidence threshold.

Provided are an imaging device, an imaging system, an imaging method, and an imaging program capable of preventing a reliability degree from decreasing in accuracy even in a case where recognition processing is performed using a partial region of image data.

An information processing device includes a reading unit configured to set, as a read unit, a part of a pixel region in which a plurality of pixels is arranged in a two-dimensional array, and control reading of a pixel signal from a pixel included in the pixel region, and a reliability degree calculation unit configured to calculate a reliability degree of a predetermined region in the pixel region on the basis of at least one of an area, a read count, a dynamic range, or exposure information of a region of a captured image, the region being set and read as the read unit.