A method for human-object interaction detection includes receiving an image. A set of features are extracted from multiple positions of the image. One or more human-object pairs may be predicted based on the extracted set of features. A human-object interaction may be determined based on a set of candidate interactions and the predicted human-object pairs.

A computer assesses language attributes of web application display text elements. The computer receives access to a selected web application. The computer parses hypertext markup language content of the web application and generating a parse tree representing the content. The computer identifies, using the parse tree, display text elements within the content and determining associated element selector queries that identify respective display text elements within the parse tree. The computer processes a set of display text elements, using a plurality of Natural Language Processing classifier models, each of the classifier models generates a relevant language prediction for the processed display text element. The computer collects, for each text element, groups of classifiers associated with substantially-similar predictions and indexed by relevant text element selector. The computer determines a target language match condition for each group. The computer initiates a corresponding at least one corrective action associated with the match condition.

Systems and methods for classifying at least a portion of an image as being textured or textureless are presented. The system receives an image generated by an image capture device, wherein the image represents one or more objects in a field of view of the image capture device. The system generates one or more bitmaps based on at least one image portion of the image. The one or more bitmaps describe whether one or more features for feature detection are present in the at least one image portion, or describe whether one or more visual features for feature detection are present in the at least one image portion, or describe whether there is variation in intensity across the at least one image portion. The system determines whether to classify the at least one image portion as textured or textureless based on the one or more bitmaps.

An apparatus, method, and vehicle for providing a braking level of a forward vehicle may quantify a degree of braking of the forward vehicle into a braking level and provide the braking level of the forward vehicle to a driver. The apparatus includes a brake lamp position recognizing device configured to recognize positions of brake lamps of the forward vehicle based on an image and a relative acceleration of the forward vehicle, a braking determining device configured to determine whether or not braking of the forward vehicle is performed based on a brake lamp image extracted from the image of the forward vehicle, a braking level determining device configured to determine a braking level of the forward vehicle based on the relative acceleration of the forward vehicle, and a braking level image providing device configured to provide the determined braking level of the forward vehicle through an image.

The embodiments of the present disclosure provide a method and an apparatus for determining object classification. The method may include: performing, by a target detection network, an object detection on a first image, to obtain a first classification confidence of a target object involved in the first image; obtaining an object image comprising a re-detection object from the first image, and performing, by a filter, the object detection on the object image, to determine a second classification confidence of the re-detection object; wherein the re-detection object is the target object whose first classification confidence is within a preset threshold range; correcting the first classification confidence of the re-detection object based on the second classification confidence to obtain an updated confidence; determining a classification detection result of the re-detection object based on the updated confidence.

Techniques are disclosed for using a trained machine learning (ML) pipeline to identify categories associated with target data items even though the identified categories may not already be present in the hierarchy. The ML pipeline may include trained cluster-based and classification-based machine learning models, among others. If the results of the cluster-based and classification-based machine learning models are the same, then the target data items is assigned to a hierarchical classification consistent with the identical results of the machine learning model. An assigned hierarchical classification may be validated by the operation of subsequent trained ML models that determine whether parent and child categories in the identified classification are properly associated with one another.

A method and system is disclosed for creating a machine learning model that is reconfigurable. A fixed parameter model is created to include fixed feature values obtained during a training process for the machine learning model. The fixed parameter model may include a fixed base classifier used by the machine learning model to classify objects detected by an ultra-sonic system within a vicinity of a vehicle. A configurable parameter model may be created to include feature values that are different from the fixed feature values, the configurable parameter model including a modified base classifier. A vehicle controller may receive and update the fixed parameter model with the configurable parameter model. The machine learning model may be updated to use the configurable parameter model to classify the objects detected by the ultra-sonic system.

A method and system is disclosed for tuning a machine learning classifier. An object class requirement may be provided and include rank thresholds. The object class requirements may also include a range goal that defines a minimum distance from the object the machine learning algorithm should not provide false positive results. A base classifier may be trained using a weighted loss function that includes one or more weight values that are computed using the one or more object class requirements. An output of the weighted loss function may be evaluated using an objective function which may be established using the one or more object class requirements. The one or more weights may also be re-tuned using the weighted loss function if the output of the weighted loss function does not converge within a predetermined loss threshold.

Disclosed are embodiments for preventing training data vulnerabilities in training data. In one embodiment, a method comprises receiving a first and second set of importance features for a first and second label output by a machine learning (ML) model; generating a first feature dictionary based on the first set of importance features and a second feature dictionary based on the second set of importance features; identifying a subset of labeled examples in a training dataset used to train the ML model based on the first feature dictionary and second feature dictionary; modifying the subset of labeled examples based on the first feature dictionary and second feature dictionary, the modifying generating a modified training data set; and retraining the ML model using the modified training data set.

One embodiment provides a system which facilitates construction of an ensemble of machine learning models. During operation, the system determines a training set of data objects, wherein each data object is associated with one of a plurality of classes. The system divides the training set of data objects into a number of partitions. The system generates a respective machine learning model for each respective partition using a universal kernel function, which processes the data objects divided into a respective partition to obtain the ensemble of machine learning models. The system trains the machine learning models based on the data objects of the training set. The system predicts an outcome for a testing data object based on the ensemble of machine learning models and an ensemble decision rule.