A device may receive vehicle operation data associated with operation of a plurality of vehicles, and may process the vehicle operation data to generate processed vehicle operation data. The device may extract multiple features from the processed vehicle operation data, and may train machine learning models, with the multiple features, to generate trained machine learning models that provide model outputs. The device may process the multiple features, with a feature selection model and based on the model outputs, to select sets of features from the plurality of features, and may process the sets of features, with the trained machine learning models, to generate indications of driving behavior and reliabilities of the indications. The device may select a set of features, from the sets of features, based on the indications and the reliabilities, where the set of features may be calculated by a device associated with a particular vehicle.

A device may receive vehicle operation data associated with operation of a plurality of vehicles, and may process the vehicle operation data to generate processed vehicle operation data. The device may extract multiple features from the processed vehicle operation data, and may train machine learning models, with the multiple features, to generate trained machine learning models that provide model outputs. The device may process the multiple features, with a feature selection model and based on the model outputs, to select sets of features from the plurality of features, and may process the sets of features, with the trained machine learning models, to generate indications of driving behavior and reliabilities of the indications. The device may select a set of features, from the sets of features, based on the indications and the reliabilities, where the set of features may be calculated by a device associated with a particular vehicle.

In some implementations, a training platform may receive data for generating synthetic models of a body part, such as a hand. The data may include information relating to a plurality of potential poses of the hand. The training platform may generate a set of synthetic models of the hand based on the information, where each synthetic model, in the set of synthetic models, representing a respective pose of the plurality of potential poses. The training platform may derive an additional set of synthetic models based on the set of synthetic models by performing one or more processing operations with respect to at least one synthetic model in the set of synthetic models, and causing the set of synthetic models and the additional set of synthetic models to be provided to a deep learning network to train the deep learning network to perform image segmentation, object recognition, or motion recognition.

An object identification and collection method is disclosed. The method includes receiving a pick-up path that identifies a route in which to guide an object-collection system over a target geographical area to pick up objects, determining a current location of the object-collection system relative to the pick-up path, and guiding the object-collection system along the pick-up path over the target geographical area based on the current location. The method further includes capturing images in a direction of movement of the object-collection system along the pick-up path, identifying a target object in the images; tracking movement of the target object through the images, determining that the target object is within range of an object picker assembly on the object-collection system based on the tracked movement of the target object, and instructing the object picker assembly to pick up the target object.

A mechanism is described for facilitating misuse index for explainable artificial intelligence in computing environments, according to one embodiment. A method of embodiments, as described herein, includes mapping training data with inference uses in a machine learning environment, where the training data is used for training a machine learning model. The method may further include detecting, based on one or more policy/parameter thresholds, one or more discrepancies between the training data and the inference uses, classifying the one or more discrepancies as one or more misuses, and creating a misuse index listing the one or more misuses.

An automatic generation system of a training image and a method thereof are provided. The disclosure generates a training image and records the target category and the target position. The disclosure adds the target image to the container image as a candidate image, calculates a reliability of the candidate image, and repeatedly executes the process until the reliability of the candidate image meets a threshold condition for generating the training image. The disclosure is able to generate the training images automatically, and the recognition difficulty of the training image is adjustable by the user, so as to be suitable for customized recognition training.

An urban remote sensing image scene classification method in consideration of spatial relationships is provided and includes following steps of: cutting a remote sensing image into sub-images in an even and non-overlapping manner; performing a visual information coding on each of the sub-images to obtain a feature image Fv; inputting the feature image Fv into a crossing transfer unit to obtain hierarchical spatial characteristics; performing convolution of dimensionality reduction on the hierarchical spatial characteristics to obtain dimensionality-reduced hierarchical spatial characteristics; and performing a softmax model based classification on the dimensionality-reduced hierarchical spatial characteristics to obtain a classification result. The method comprehensively considers the role of two kinds of spatial relationships being regional spatial relationship and long-range spatial relationship in classification, and designs three paths in a crossing transfer unit for relationships fusion, thereby obtaining a better urban remote sensing image scene classification result.

A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.

Systems, methods, and other embodiments described herein relate to improving the classification of objects depicted in a scene. In one embodiment, a method includes generating, using an ontological detector, a type classification of a detected object according to a detector ontology of known classes. The detected object is represented as segmented data from sensor data about a surrounding environment. The method includes, in response to determining that the type classification specifies an unknown class that is not defined in the detector ontology, annotating the segmented data as unknown. The method includes providing the segmented data to specify that the type classification for the detected object is unknown.