One aspect provides a machine-learned video prediction model configured to receive and process one or more previous video frames to generate one or more predicted subsequent video frames, wherein the machine-learned video prediction model comprises a convolutional variational auto encoder, and wherein the convolutional variational auto encoder comprises an encoder portion comprising one or more encoding cells and a decoder portion comprising one or more decoding cells.

One aspect provides a machine-learned video prediction model configured to receive and process one or more previous video frames to generate one or more predicted subsequent video frames, wherein the machine-learned video prediction model comprises a convolutional variational auto encoder, and wherein the convolutional variational auto encoder comprises an encoder portion comprising one or more encoding cells and a decoder portion comprising one or more decoding cells.

Methods and systems are described herein for generating composite data streams. A data stream processing system may receive multiple data streams from, for example, multiple unmanned vehicles and determine, based on the type of data within each data stream, a machine learning model for each data stream for processing the type of data. Each machine learning model may receive the frames of a corresponding data stream and output indications and locations of objects within those data streams. The data stream processing system may then generate a composite data stream with indications of the detected objects.

Methods and systems are described herein for generating composite data streams. A data stream processing system may receive multiple data streams from, for example, multiple unmanned vehicles and determine, based on the type of data within each data stream, a machine learning model for each data stream for processing the type of data. Each machine learning model may receive the frames of a corresponding data stream and output indications and locations of objects within those data streams. The data stream processing system may then generate a composite data stream with indications of the detected objects.

According to one embodiment, a computer-implemented method for detecting and classifying columns of tables and/or tabular data arrangements within image data includes: detecting one or more tables and/or one or more tabular data arrangements within the image data; extracting the one or more tables and/or the one or more tabular data arrangements from the processed image data; and classifying either: a plurality of columns of the one or more extracted tables; a plurality of columns of the one or more extracted tabular data arrangements; or both the columns of the one or more extracted tables and the columns of the one or more extracted tabular data arrangements. Corresponding systems and computer program products are also disclosed.

According to one embodiment, a computer-implemented method for detecting and classifying columns of tables and/or tabular data arrangements within image data includes: detecting one or more tables and/or one or more tabular data arrangements within the image data; extracting the one or more tables and/or the one or more tabular data arrangements from the processed image data; and classifying either: a plurality of columns of the one or more extracted tables; a plurality of columns of the one or more extracted tabular data arrangements; or both the columns of the one or more extracted tables and the columns of the one or more extracted tabular data arrangements. Corresponding systems and computer program products are also disclosed.

An object of the present disclosure is to provide an information processing apparatus, an information processing system, an information processing method, and an information processing program capable of achieving efficient use of training data. An information processing apparatus according to the present disclosure includes: a recognition unit (101) that performs object recognition processing using sensor information acquired by a sensor, the object recognition processing being performed by a first recognizer that has been pretrained; and a training data application determination unit (22d) that determines whether the sensor information is applicable as training data to a second recognizer different from the first recognizer.

Embodiments are disclosed for creating and managing semantic layers in a graphic design system. A method of creating and managing semantic layers includes receiving a selection of a content type to be generated, receiving a selection of a location in a digital canvas to place content of the content type, generating, using one or more machine learning models, content of the selected content type at the location in the digital canvas, and automatically adding the content to a layer associated with the digital canvas based on a semantic label associated with the content.

Embodiments are disclosed for creating and managing semantic layers in a graphic design system. A method of creating and managing semantic layers includes receiving a selection of a content type to be generated, receiving a selection of a location in a digital canvas to place content of the content type, generating, using one or more machine learning models, content of the selected content type at the location in the digital canvas, and automatically adding the content to a layer associated with the digital canvas based on a semantic label associated with the content.

A processing system includes a processor with hardware. The processor is configured to perform processing of acquiring a detection target image captured by an endoscope apparatus, controlling the endoscope apparatus based on control information, detecting a region of interest included in the detection target image based on the detection target image for calculating estimated probability information representing a probability of the detected region of interest, identifying the control information for improving the estimated probability information related to the region of interest within the detection target image based on the detection target image, and controlling the endoscope apparatus based on the identified control information.