Abstract: A model learning device determines a first machine learning model so as to further increase a combined loss function obtained by combining: a first loss function indicating the level of change in the reliability of a second image feature in a feature region of a reconstructed image, from the reliability of a first image feature in a feature region of the original image; and a second loss function indicating the level of recognition error. In addition, the model learning device: determines, so as to further reduce the combined loss function, respective parameter sets for a second machine learning model used in the generation of compressed data, and a third machine learning model used in the generation of the reconstructed image from the compressed data; and determines a parameter set for the fourth machine learning model in common with that for the first machine learning model.

Abstract: To provide a technique which makes it possible to suitably estimate an important region and a non-important region in an image, an information processing apparatus (1) includes: an obtaining means (10) for obtaining input data which includes at least one of image data and point cloud data; an estimating means (11) for estimating levels of importance with respect to a respective plurality of regions which are included in a frame indicated by the input data; a replacing means (12) for generating replaced data by replacing at least one of the plurality of regions, which are included in the input data, with alternative data in accordance with the levels of importance; an evaluating means (13) for deriving an evaluation value by referring to the replaced data; and a training means (14) for training the estimating means with reference to the evaluation value.

Abstract: To provide a technique which makes it possible to suitably estimate an important region and a non-important region in an image, an information processing apparatus includes: an obtaining means for obtaining input data which includes at least one of image data and point cloud data; an estimating means for estimating levels of importance with respect to a respective plurality of regions which are included in a frame indicated by the input data; a replacing means for generating replaced data by replacing at least one of the plurality of regions, which are included in the input data, with alternative data in accordance with the levels of importance; an evaluating means for deriving an evaluation value by referring to the replaced data; and a training means for training the estimating means with reference to the evaluation value.

Abstract: Provided is an image processing apparatus which makes it possible to, during remote control, carry out suitable monitoring while suitably suppressing a transmission load. An image processing apparatus includes: an obtaining means for obtaining an image which has been captured from a movable body; an importance level estimating means for estimating levels of importance with respect to a respective plurality of regions included in the image; and a quality parameter determining means for determining quality parameters with respect to the respective plurality of regions with reference to the levels of importance.

Abstract: A first distribution estimating device determines a first probability distribution of quantized values in a predetermined value range corresponding to an input value, by using a first machine learning model. A first sampling device samples the quantized values and determines a first sample value, using the first probability distribution. A second distribution estimating device determines a second probability distribution corresponding to the first sample value, by using a second machine learning model. A second sampling device samples the quantized values in the value range and determines a second sample value, using the second probability distribution. It can be implemented in the form of any of an information processing system, an encoding device, a decoding device, a model learning device, an information processing method, an encoding method, a decoding method, a model learning method, and a program storage medium.

Abstract: An information processing device receives communication data based on feature data that indicates a feature of presented content of target data; restores the feature data on the basis of the received communication data; restores the target data on the basis of the restored feature data; performs a recognition process on the presented content of the target data on the basis of the restored feature data; and outputs information that indicates the presented content of the restored target data and a recognition result of the recognition process.

Abstract: Estimating determines, on the basis of processed quantized values, a quantization interval for quantizing input element values, which are elements of input values serving as a processing object and determines, on the basis of the processed quantized values, a probability distribution of quantization value candidates, which are candidates of quantized values of the input element values; quantizing uses the quantization interval to quantize the input element values and determine a quantized value; and entropy encoding uses the probability distribution to perform entropy encoding of a quantization time sequence which includes quantized value codes indicating the quantized values of the input element values.

Abstract: A monitoring person can rapidly take a countermeasure against a situation occurring in a moving object. Image reception means (31) receives image data obtained by imaging the outside of the moving object. Candidate acquisition means (32) acquires control content candidates of autonomous driving of the moving object. Monitoring screen display means (33) causes a display apparatus to display a monitoring screen. The monitoring screen includes a region where the image data is displayed and a region where the control content candidates are displayed. When one control content is selected from the control content candidates, control content transmission means (34) transmits a control signal representing the selected control content to the moving object.

Abstract: An information processing device includes: an image acquisition unit configured to acquire an image captured by an imaging unit mounted on a vehicle; a target detection unit configured to detect a target region including a target in the acquired image; a classification identification unit configured to identify classification including a type of the detected target and a size of the detected target region; and a region determination unit configured to determine a region obtained by adding a margin corresponding to the identified classification to the target region as an image processing region.

Abstract: The present disclosure provides an information processing apparatus and the like capable of specifying an object in an image that may affect traveling of a vehicle. An information processing apparatus includes: an acquisition unit that acquires an image captured by an image capturing unit mounted on a vehicle; an object detection unit that detects one or more objects in the acquired image; a traveling region specifying unit that specifies a traveling region, in which the vehicle is traveling, from regions in the acquired image; and a determination unit that determines, based on the traveling region, an image processing region, which is subjected to image processing, among regions of the one or more objects.

Abstract: A region prediction unit predicts the target region in the second image frame based on the target region detected in a first image frame and a time interval between the first and second image frames. The region prediction unit predicts the target region in the third image frame based on the target region detected in the second image frame and a time interval between the second and third image frames. A region determination unit determines, as an image-processing region, a region obtained by adding a margin to the target prediction region in the third image frame in accordance with an overlapping portion between the detected target region in the second image frame and the prediction region in the second image frame.

Abstract: A system includes a non-transitory computer readable medium configured to store instructions thereon; and a processor connected to the non-transitory computer readable medium. The processor is configured to execute the instructions for generating a mask based on received data from a sensor, wherein the mask includes a plurality of importance values, and each region of the received data is designated a corresponding importance value of the plurality of importance values. The processor is configured to execute the instructions for encoding the received data based on the mask; and transmitting the encoded data to a decoder for defining reconstructed data. The processor is configured to execute the instructions for computing a loss based on the reconstructed data, the received data and the mask. The processor is configured to execute the instructions for providing training to an encoder for encoding the received data based on the computed loss.

Abstract: A system includes a non-transitory computer readable medium configured to store instructions thereon; and a processor connected to the non-transitory computer readable medium. The processor is configured to execute the instructions for generating a mask based on received data from a sensor, wherein the mask includes a plurality of importance values, and each region of the received data is designated a corresponding importance value of the plurality of importance values. The processor is configured to execute the instructions for encoding the received data based on the mask; and transmitting the encoded data to a decoder for defining reconstructed data. The processor is configured to execute the instructions for computing a loss based on the reconstructed data, the received data and the mask. The processor is configured to execute the instructions for providing training to an encoder for encoding the received data based on the computed loss.