Abstract: A method of performing open world object detection includes receiving object data, that includes embeddings data corresponding to a plurality of embeddings for known objects in a first input image, projecting the embeddings into a hyperbolic embedding space that includes embeddings in a plurality of categories of objects each including one or more classes of objects, regularizing the projected embeddings within the hyperbolic embedding space by moving each of the projected embeddings closer to embeddings in a same category of the plurality of categories and further away from embeddings in different categories of the plurality of categories, receiving an unmatched query corresponding to an object in a second input image, and generating, based on the hyperbolic embedding space including the regularized embeddings, an output signal that indicates whether the object in the second input image corresponds to an unknown object in one of the classes of objects.

Abstract: A method of performing data pre-selection for an object detection system includes receiving a first dataset that includes unlabeled data corresponding to one or more images, providing the first dataset and a plurality of learnable prompt vectors to a pre-training model. The learnable prompt vectors include text inputs. The method further includes generating, using the pre-training model, an unsupervised learning prompt based on the first dataset and the plurality of learnable prompt vectors. The unsupervised learning prompt corresponds to a multi-modal feature of the one or more images of the first dataset. The method further includes extracting features from either of the first dataset and a second dataset based on the unsupervised learning prompt, selecting and labeling a subset of instances of the extracted features, and generating and outputting a labeled dataset based on the labeled subset of instances.

Abstract: A device repairing method includes: collecting a device-parameter set of a target device; based on the device-parameter set, determining whether a to-be-repaired component exists among a plurality of device components of the target device, to obtain a determination result; in response to the determination result indicating that the to-be-repaired component exists, repairing the to-be-repaired component by operating a preset port in the to-be-repaired component, to obtain a repairment result; and according to the repairment result, determining whether the to-be-repaired component is repaired, and in response to the to-be-repaired component being not repaired, repeatedly executing the step of repairing the to-be-repaired component by operating the preset port in the to-be-repaired component, till the repairment result indicates that the to-be-repaired component is repaired or a repairment time quantity reaches a preset time-quantity threshold.

Abstract: A computer-implemented system and method relates to language-guided self-supervised semantic segmentation. A modified image is generated by performing data augmentation on a source image. A machine learning model generates first pixel embeddings based on the modified image. First segment embeddings are generated using the first pixel embeddings. A pretrained vision-language model generates second pixel embeddings based on the source image. Second segment embeddings are generated by applying segment contour data from the first pixel embeddings to the second pixel embeddings after the data augmentation is performed on the second pixel embeddings. Embedding consistent loss data is generated by comparing the first segment embeddings in relation to the second segment embeddings. Combined loss data is generated that includes the embedding consistent loss data. Parameters of the machine learning model are updated based on the combined loss data.

Abstract: The present disclosure provides a self-learning collaborative control method for active steering and yaw moment for a motor vehicle, including a first step of constructing fundamental formulas which are stored in a vehicle ECU, and a second step of calculating an active steering angle ?C and a yaw moment Mc on line by the vehicle ECU according to following sub-steps during a driving process of the motor vehicle, and controlling a driving state of the motor vehicle according to ?C and Mc. The second step includes a first sub-step of collecting raw real-time parameter values, a second sub-step of performing calculation by the identifier and the control target reference model, a third sub-step of calculating ?C and Mc. The present disclosure can realize the self-learning collaborative control of active steering and yaw moment without requiring a system control model and correct a driver's steering operation.

Abstract: A computer-implemented system and method includes generating first pseudo segment data from a first augmented image and generating second pseudo segment data from a second augmented image. The first augmented image and the second augmented image are in a dataset along with other augmented images. A machine learning system is configured to generate pixel embeddings based on the dataset. The first pseudo segment data and the second pseudo segment data are used to identify a first set of segments that a given pixel belongs with respect to the first augmented image and the second augmented image. A second set of segments is identified across the dataset. The second set of segments do not include the given pixel. A local segmentation loss is computed for the given pixel based on the corresponding pixel embedding that involves attracting the first set of segments while repelling the second set of segments.

Abstract: An autonomous vehicle longitudinal-and-lateral control method for preventing motion sickness is disclosed. Firstly, a vehicle dynamics model is established, and the current vehicle travelling state and road surface information are determined according to the vehicle dynamics model. After the vehicle travelling state and road surface information are acquired, the desired velocity, desired acceleration, and desired wheel turning angle are output according to a driver model for reducing occurrence of motion sickness while ensuring safety and efficiency, to carry out longitudinal-and-lateral control of the vehicle.

Abstract: The present disclosure provides a self-learning collaborative control method for active steering and yaw moment for a motor vehicle, including a first step of constructing fundamental formulas which are stored in a vehicle ECU, and a second step of calculating an active steering angle ?C and a yaw moment Mc on line by the vehicle ECU according to following sub-steps during a driving process of the motor vehicle, and controlling a driving state of the motor vehicle according to ?C and Mc. The second step includes a first sub-step of collecting raw real-time parameter values, a second sub-step of performing calculation by the identifier and the control target reference model, a third sub-step of calculating ?C and Mc. The present disclosure can realize the self-learning collaborative control of active steering and yaw moment without requiring a system control model and correct a driver's steering operation.

Abstract: A method for training a machine learning model. The method comprises receiving a training dataset that includes a plurality of images. The method also includes identifying, by a machine learning model, at least one portion of at least one image of the plurality of images in the training dataset associated with a first object type. The method further includes identifying other images having at least one portion that includes the first object type. The method also includes grouping the identified other images into a first image group. The method also includes generating for display a first user interface, that at least includes a rank matrix, wherein a first row of the rank matrix represents the images of the first image object. The user may provide feedback for the visualization using the first interface. The method may also include training the machine learning model based on the user feedback.

Abstract: Methods and systems for performing function testing for moveable objects. One system includes an electronic processor configured to access a driving scene including a moveable object. The electronic processor is also configured to perform spatial representation learning on the driving scene. The electronic processor is also configured to generate an adversarial example based on the learned spatial representation. The electronic processor is also configured to retrain the deep learning model using the adversarial example and the driving scene.

Abstract: Methods and systems for performing concept-based adversarial generation with steerable and diverse semantics. One system includes an electronic processor configured to access an input image. The electronic processor is also configured to perform concept-based semantic image generation based on the input image. The electronic processor is also configured to perform concept-based semantic adversarial learning using a set of semantic latent spaces generated as part of performing the concept-based semantic image generation. The electronic processor is also configured to generate an adversarial image based on the concept-based semantic adversarial learning. The electronic processor is also configured to test a target model using the adversarial image.

Abstract: A computer-implemented system and method includes generating first pseudo segment data from a first augmented image and generating second pseudo segment data from a second augmented image. The first augmented image and the second augmented image are in a dataset along with other augmented images. A machine learning system is configured to generate pixel embeddings based on the dataset. The first pseudo segment data and the second pseudo segment data are used to identify a first set of segments that a given pixel belongs with respect to the first augmented image and the second augmented image. A second set of segments is identified across the dataset. The second set of segments do not include the given pixel. A local segmentation loss is computed for the given pixel based on the corresponding pixel embedding that involves attracting the first set of segments while repelling the second set of segments.

Abstract: Embodiments of systems and methods for diagnosing an object-detecting machine learning model for autonomous driving are disclosed herein. An input image is received from a camera mounted in or on a vehicle that shows a scene. A spatial distribution of movable objects within the scene is derived using a context-aware spatial representation machine learning model. An unseen object is generated in the scene that is not originally in the input image utilizing a spatial adversarial machine learning model. Via the spatial adversarial machine learning model, the unseen object is moved to different locations to fail the object-detecting machine learning model. An interactive user interface enables a user to analyze performance of the object-detecting machine learning model with respect to the scene without the unseen object and the scene with the unseen object.

Abstract: Methods and systems for performing concept-based adversarial generation with steerable and diverse semantics. One system includes an electronic processor configured to access an input image. The electronic processor is also configured to perform concept-based semantic image generation based on the input image. The electronic processor is also configured to perform concept-based semantic adversarial learning using a set of semantic latent spaces generated as part of performing the concept-based semantic image generation. The electronic processor is also configured to generate an adversarial image based on the concept-based semantic adversarial learning. The electronic processor is also configured to test a target model using the adversarial image.

Abstract: Methods and systems for performing function testing for moveable objects. One system includes an electronic processor configured to access a driving scene including a moveable object. The electronic processor is also configured to perform spatial representation learning on the driving scene. The electronic processor is also configured to generate an adversarial example based on the learned spatial representation. The electronic processor is also configured to retrain the deep learning model using the adversarial example and the driving scene.

Abstract: A visual analytics workflow and system are disclosed for assessing, understanding, and improving deep neural networks. The visual analytics workflow advantageously enables interpretation and improvement of the performance of a neural network model, for example an image-based objection detection and classification model, with minimal human-in-the-loop interaction. A data representation component extracts semantic features of input image data, such as colors, brightness, background, rotation, etc. of the images or objects in the images. The input image data are passed through the neural network to obtain prediction results, such as object detection and classification results. An interactive visualization component transforms the prediction results and semantic features into interactive and human-friendly visualizations, in which graphical elements encoding the prediction results are visually arranged depending on the extracted semantic features of input image data.

Abstract: The disclosed embodiments enable a report to be generated using a template. The template may include one or more properties for which for which corresponding values are to be inherited by each copy of the template. A value of a property that is inherited may be overridden by modifying the inherited value. A copy of a template may be incorporated into a report design. Conversely, at least a portion of a report design may be saved as a template.

Abstract: The disclosed embodiments enable a report to be generated using a template. The template may include one or more properties for which for which corresponding values are to be inherited by each copy of the template. A value of a property that is inherited may be overridden by modifying the inherited value. A copy of a template may be incorporated into a report design. Conversely, at least a portion of a report design may be saved as a template.

Abstract: According to an example, association information of a target account is obtained. Recommendation information is generated for the target account according to the association information and is transmitted to the target account. After feedback information transmitted by the target account in response to the recommendation information is received, processing is performed according to the feedback information transmitted by the target account.