According to one embodiment, a state determination apparatus includes a processor. The processor acquires a targeted image. The processor acquires a question concerning the targeted image and an expected answer to the question. The processor generates an estimated answer estimated with respect to the question concerning the targeted image using a trained model trained to estimate an answer based on a question concerning an image. The processor determines a state of a target for determination in accordance with a similarity between the expected answer and the estimated answer.

A beauty prediction method and device based on multitasking and weak supervision, and a storage medium are disclosed. The method includes the steps of pre-processing inputted facial images; allocating the pre-processed images to multiple tasks; extracting shared image features; and obtaining a plurality of classification results via a plurality of classification networks each including a residual network, a standard neural network and a classifier.

Disclosed are methods and devices related to autonomous driving. In one aspect, a method is disclosed. The method includes determining three-dimensional bounding indicators for one or more first objects in road target information captured by a light detection and ranging (LIDAR) sensor; determining camera bounding indicators for one or more second objects in road image information captured by a camera sensor; processing the road image information to generate a camera matrix; determining projected bounding indicators from the camera matrix and the three-dimensional bounding indicators; determining, from the projected bounding indicators and the camera bounding indicators, associations between the one or more first objects and the one or more second objects to generate combined target information; and applying, by the autonomous driving system, the combined target information to produce a vehicle control signal.

Systems and methods for object detection. Object detection may be used to control autonomous vehicle(s). For example, the methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of the autonomous vehicle; and using, by the computing device, the LiDAR dataset and image(s) to detect an object that is in proximity to the autonomous vehicle. The object being is detected by: computing a distribution of object detections that each point of the LiDAR dataset is likely to be in; creating a plurality of segments of LiDAR data points using the distribution of object detections; merging the plurality of segments of LiDAR data points to generate merged segments; and detecting the object in a point cloud defined by the LiDAR dataset based on the merged segments. The object detection may be used by the computing device to facilitate at least one autonomous driving operation.

Methods, devices and storage media for data processing are provided. One of the methods include: obtaining a target image, wherein the target image comprises at least one tubular image; determining a spatial distribution feature and an image feature of each of the at least one tubular image; obtaining, based on a tubular structure recognition model, at least one fusion feature respectively corresponding to the at least one tubular image by fusing the spatial distribution feature and the image feature of each of the at least one tubular image; and recognizing, based on the tubular structure recognition model and the at least one fusion feature respectively corresponding to the at least one tubular image, at least one tubular structure respectively corresponding to the at least one tubular image.

A computer system obtains, in electronic format, a training dataset. The training dataset comprises a plurality of training images from a plurality of agricultural plots. Each training image is from a respective agricultural plot in the plurality of agricultural plots and comprises at least one identified fruit. The computer system determines, for each respective fruit in each respective training image in the plurality of training images, a corresponding contour. The computer system trains an untrained or partially trained computational model using at least the corresponding contour for each respective fruit in each respective training image in the plurality of training images, thereby obtaining a first trained computational model that is configured to identify fruit in agricultural plot images.

A method for processing a medical image performed by a computer device. The method includes: calling a first coding network in an image processing model to code a first sample image of a first mode of a target medical object, to obtain a first feature map of the first sample image; calling a decoding network to obtain, based on the first feature map, a predictive segmentation image used for indicating at least one predicted specified type region within the first sample image; calling a generative network to generate a predictive generation image of a second mode based on the first feature map; and training the image processing model based on a difference between the predictive segmentation image and a tag image of the target medical object and a difference between the predictive generation image and a second sample image of a second mode of the target medical object.

An information processing device for a vehicle for sensor data fusion for object detection, including circuitry configured to: obtain, based on obtained first sensor data from a first sensor of the vehicle and a first predetermined object pose probability model, first object pose probability data, wherein the first predetermined object pose probability model is specific for the first sensor; obtain, based on obtained second sensor data from a second sensor of the vehicle and a second predetermined object pose probability model, second object pose probability data, wherein the second predetermined object pose probability model is specific for the second sensor; and fuse the first and the second object pose probability data to obtain fused object pose probability data for object detection.

A vehicle includes one or more cameras that capture a plurality of two-dimensional images of a three-dimensional object. A light detector and/or a semantic classifier search within those images for lights of the three-dimensional object. A vehicle signal detection module fuses information from the light detector and/or the semantic classifier to produce a semantic meaning for the lights. The vehicle can be controlled based on the semantic meaning. Further, the vehicle can include a depth sensor and an object projector. The object projector can determine regions of interest within the two-dimensional images, based on the depth sensor. The light detector and/or the semantic classifier can use these regions of interest to efficiently perform the search for the lights.

Provided are an information processing device, a program, a trained model, a diagnostic support device, a learning device, and a prediction model generation method that can perform prediction with high accuracy using images. An information processing device includes: an information acquisition unit that receives an input of image data and non-image data related to a target matter; and a prediction unit that predicts an aspect related to the matter at a time different from a time when the image data is captured on the basis of the image data and the non-image data input through the information acquisition unit. The prediction unit performs weighting calculation by a calculation method, which outputs a combination of products of elements of a first feature amount calculated from the image data and a second feature amount calculated from the non-image data, to calculate a third feature amount in which the first feature amount and the second feature amount are fused and performs the prediction on the basis of the third feature amount.