An electronic apparatus includes: a communication interface; a memory storing at least one instruction; and a processor configured to execute the at least one instruction to: quantize a first neural network model for identifying an object, to acquire a second neural network model, control the communication interface to transmit information on the second neural network model to an external apparatus, receive, from the external apparatus, feature information for an image stored in the external apparatus acquired based on the second neural network model, and identification information corresponding to the feature information, and train the second neural network model based on the received feature information and the received identification information.

An electronic apparatus includes: a communication interface; a memory storing at least one instruction; and a processor configured to execute the at least one instruction to: quantize a first neural network model for identifying an object, to acquire a second neural network model, control the communication interface to transmit information on the second neural network model to an external apparatus, receive, from the external apparatus, feature information for an image stored in the external apparatus acquired based on the second neural network model, and identification information corresponding to the feature information, and train the second neural network model based on the received feature information and the received identification information.

A training data generating system includes a processor. The processor acquires a plurality of medical images. The processor associates medical images with each other which are included in the plurality of medical images based on similarities of an imaging target to generate an associated image group including medical images associated with each other. The processor outputs, to a display, an application target image to be an image as an application target of representative training information based on the associated image group. The processor accepts input of representative contour information indicative of a contour of a specific region in the application target image as the representative training information. The processor applies contour information, as training information, to each medical image included in the associated image group based on the representative training information input to the application target image.

A training data generating system includes a processor. The processor acquires a plurality of medical images. The processor associates medical images with each other which are included in the plurality of medical images based on similarities of an imaging target to generate an associated image group including medical images associated with each other. The processor outputs, to a display, an application target image to be an image as an application target of representative training information based on the associated image group. The processor accepts input of representative contour information indicative of a contour of a specific region in the application target image as the representative training information. The processor applies contour information, as training information, to each medical image included in the associated image group based on the representative training information input to the application target image.

An image recording system includes a processor. The processor acquires a time series RAW image group including a plurality of time series RAW images in a first time section. The processor extracts, from the time series RAW image group, a recording candidate RAW image group included in a second time section as a part of the first time section. The processor records at least one RAW image included in the recording candidate RAW image group as a recording target RAW image which is a RAW image to be recorded. The processor selects the recording target RAW image from the recording candidate RAW image group. The processor converts the RAW image which is not selected as the recording target RAW image from the recording candidate RAW image group or the time series RAW image group to compressed data, and records the compressed data.

An image recording system includes a processor. The processor acquires a time series RAW image group including a plurality of time series RAW images in a first time section. The processor extracts, from the time series RAW image group, a recording candidate RAW image group included in a second time section as a part of the first time section. The processor records at least one RAW image included in the recording candidate RAW image group as a recording target RAW image which is a RAW image to be recorded. The processor selects the recording target RAW image from the recording candidate RAW image group. The processor converts the RAW image which is not selected as the recording target RAW image from the recording candidate RAW image group or the time series RAW image group to compressed data, and records the compressed data.

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.

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.

A method for video recognition and related products are provided. The method includes the following. An original set of clip descriptors is obtained by providing multiple clips of a video as an input of a 3D CNN of a neural network, where the neural network includes the 3D CNN and at least one first fully connected layer, and each of the multiple clips includes at least one frame. An attention vector corresponding to the original set of clip descriptors is determined. An enhanced set of clip descriptors is obtained based on the original set of clip descriptors and the attention vector. The enhanced set of clip descriptors is input into the at least one first fully connected layer and video recognition is performed based on an output of the at least one first fully connected layer.

A method for video recognition and related products are provided. The method includes the following. An original set of clip descriptors is obtained by providing multiple clips of a video as an input of a 3D CNN of a neural network, where the neural network includes the 3D CNN and at least one first fully connected layer, and each of the multiple clips includes at least one frame. An attention vector corresponding to the original set of clip descriptors is determined. An enhanced set of clip descriptors is obtained based on the original set of clip descriptors and the attention vector. The enhanced set of clip descriptors is input into the at least one first fully connected layer and video recognition is performed based on an output of the at least one first fully connected layer.