An action recognition method and apparatus related to artificial intelligence and include extracting a spatial feature of a to-be-processed picture, determining a virtual optical flow feature of the to-be-processed picture based on the spatial feature and X spatial features and X optical flow features in a preset feature library, where the X spatial features and the X optical flow features include a one-to-one correspondence, determining a first type of confidence of the to-be-processed picture in different action categories based on similarities between the virtual optical flow feature and Y optical flow features, where each of the Y optical flow features in the preset feature library corresponds to one action category, X and Y are both integers greater than 1, and determining an action category of the to-be-processed picture based on the first type of confidence.

An image processing apparatus includes a controller. The controller calculates a fundamental frequency component included in sound data and a harmonic component corresponding to the fundamental frequency component, converts the fundamental frequency component and the harmonic component into image data, and generates a sound image where the fundamental frequency component and the harmonic component converted into the image data are arranged adjacent each other.

An image processing apparatus includes a controller. The controller calculates a fundamental frequency component included in sound data and a harmonic component corresponding to the fundamental frequency component, converts the fundamental frequency component and the harmonic component into image data, and generates a sound image where the fundamental frequency component and the harmonic component converted into the image data are arranged adjacent each other.

A method including rendering graphics for an application using graphics processing units (GPUs). Responsibility for rendering of geometry is divided between GPUs based on screen regions, each GPU having a corresponding division of the responsibility which is known. First pieces of geometry are rendered at the GPUs during a rendering phase of a previous image frame. Statistics are generated for the rendering of the previous image frame. Second pieces of geometry of a current image frame are assigned based on the statistics to the GPUs for geometry testing. Geometry testing at a current image frame on the second pieces of geometry is performed to generate information regarding each piece of geometry and its relation to each screen region, the geometry testing performed at each of the GPUs based on the assigning. The information generated for the second pieces of geometry is used when rendering the geometry at the GPUs.

A method including rendering graphics for an application using graphics processing units (GPUs). Responsibility for rendering of geometry is divided between GPUs based on screen regions, each GPU having a corresponding division of the responsibility which is known. First pieces of geometry are rendered at the GPUs during a rendering phase of a previous image frame. Statistics are generated for the rendering of the previous image frame. Second pieces of geometry of a current image frame are assigned based on the statistics to the GPUs for geometry testing. Geometry testing at a current image frame on the second pieces of geometry is performed to generate information regarding each piece of geometry and its relation to each screen region, the geometry testing performed at each of the GPUs based on the assigning. The information generated for the second pieces of geometry is used when rendering the geometry at the GPUs.

An inspection apparatus according to one or more embodiments extracts an attention area from a target image using a first estimation model, performs a computational process with a second estimation model using the extracted attention area, and determines whether a target product has a defect based on a computational result from the second estimation model. The first estimation model is generated based on multiple first training images of defect-free products in a target environment. The second estimation model is generated based on multiple second training images of defects. The computational process with the second estimation model includes generating multiple feature maps with different dimensions by projecting the target image into different spaces with lower dimensions. The extracted attention area is integrated into at least one of the multiple feature maps in the computational process with the second estimation model.

An inspection apparatus according to one or more embodiments extracts an attention area from a target image using a first estimation model, performs a computational process with a second estimation model using the extracted attention area, and determines whether a target product has a defect based on a computational result from the second estimation model. The first estimation model is generated based on multiple first training images of defect-free products in a target environment. The second estimation model is generated based on multiple second training images of defects. The computational process with the second estimation model includes generating multiple feature maps with different dimensions by projecting the target image into different spaces with lower dimensions. The extracted attention area is integrated into at least one of the multiple feature maps in the computational process with the second estimation model.

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a deep visual fingerprinting model with parameters learned from robust contrastive learning to identify matching digital images and image provenance information. For example, the disclosed systems utilize an efficient learning procedure that leverages training on bounded adversarial examples to more accurately identify digital images (including adversarial images) with a small computational overhead. To illustrate, the disclosed systems utilize a first objective function that iteratively identifies augmentations to increase contrastive loss. Moreover, the disclosed systems utilize a second objective function that iteratively learns parameters of a deep visual fingerprinting model to reduce the contrastive loss. With these learned parameters, the disclosed systems utilize the deep visual fingerprinting model to generate visual fingerprints for digital images, retrieve and match digital images, and provide digital image provenance information.

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a deep visual fingerprinting model with parameters learned from robust contrastive learning to identify matching digital images and image provenance information. For example, the disclosed systems utilize an efficient learning procedure that leverages training on bounded adversarial examples to more accurately identify digital images (including adversarial images) with a small computational overhead. To illustrate, the disclosed systems utilize a first objective function that iteratively identifies augmentations to increase contrastive loss. Moreover, the disclosed systems utilize a second objective function that iteratively learns parameters of a deep visual fingerprinting model to reduce the contrastive loss. With these learned parameters, the disclosed systems utilize the deep visual fingerprinting model to generate visual fingerprints for digital images, retrieve and match digital images, and provide digital image provenance information.

The present disclosure relates to an information processing apparatus, an information processing method, an information processing system, and a program capable of appropriately evaluating an object recognition filter by simpler processing. A generation unit that generates teacher data of a preprocessing filter provided in a preceding stage of the object recognition filter is generated by a cyclic generative adversarial network (Cyclic GAN) that is unsupervised learning. The teacher data generated by the generated generation unit is applied to the object recognition filter, an evaluation image is generated from a difference between object recognition result images, and an evaluation filter that generates an evaluation image from the evaluation image and the teacher data is generated. The evaluation filter is applied to an input image to generate an evaluation image, and the object recognition filter is evaluated by the generated evaluation image. The present disclosure can be applied to an object recognition device.