Abstract: Systems and methods for performing multiple tasks with a single artificial intelligence model that can include training a supernet model for an application by splitting the application into tasks, and splitting the supernet model into subnets. The methods and systems can further assign the tasks computing budgets, and match the tasks to subnets by matching the computing budget of the tasks to the computing capacity of the subnets. Further, the methods and systems can perform the tasks with matching subnets to produce parameters that are used by the supernet to perform the application. The supernet combines all of the task to produce a model for the application and the supernet retains weights for the tasks to be used in subsequent applications.

Abstract: A method for improving face recognition from unseen domains by learning semantically meaningful representations is presented. The method includes obtaining face images with associated identities from a plurality of datasets, randomly selecting two datasets of the plurality of datasets to train a model, sampling batch face images and their corresponding labels, sampling triplet samples including one anchor face image, a sample face image from a same identity, and a sample face image from a different identity than that of the one anchor face image, performing a forward pass by using the samples of the selected two datasets, finding representations of the face images by using a backbone convolutional neural network (CNN), generating covariances from the representations of the face images and the backbone CNN, the covariances made in different spaces by using positive pairs and negative pairs, and employing the covariances to compute a cross-domain similarity loss function.

Abstract: A computer-implemented method for training a neural network to predict object categories without manual annotation is provided. The method includes feeding training datasets including at least images and data annotations to an object detection neural network, converting, by a text prompter, the data annotations into natural text inputs, converting, by a text embedder, the natural text inputs into embeddings, minimizing objective functions during training to adjust parameters of the object detection neural network, and predicting, by the object detection neural network, objects within images and videos.

Abstract: A computer-implemented method for detecting objects within an advanced driver assistance system (ADAS) is provided. The method includes obtaining road scene datasets from a plurality of cameras, including at least road scene images and road scene data annotations, to be provided to an object detection neural network communicating with an open-vocabulary detector of a vehicle, converting, by a text prompter, the road scene data annotations into natural text inputs, converting, by a text embedder, the natural text inputs into embeddings, minimizing objective functions during training to adjust parameters of the object detection neural network, and detecting, by the object detection neural network, objects within the road scene datasets to provide alerts or notifications to a driver of the vehicle pertaining to the detected objects.

Abstract: Systems and methods for generating a hypernetwork configured to be trained for a plurality of tasks; receiving a task preference vector identifying a hierarchical priority for the plurality of tasks, and a resource constraint as a tuple; finding tree sub-structures and the corresponding modulation of features for every tuple within an N-stream anchor network; optimizing a branching regularized loss function to train an edge hypernet; and training a weight hypernet, keeping the anchor net and the edge hypernet fixed.

Abstract: A computer-implemented method for model training is provided. The method includes receiving, by a hardware processor, sets of images, each set corresponding to a respective task. The method further includes training, by the hardware processor, a task-based neural network classifier having a center and a covariance matrix for each of a plurality of classes in a last layer of the task-based neural network classifier and a plurality of convolutional layers preceding the last layer, by using a similarity between an image feature of a last convolutional layer from among the plurality of convolutional layers and the center and the covariance matrix for a given one of the plurality of classes, the similarity minimizing an impact of a data model forgetting problem.

Abstract: A method for employing facial information in unsupervised person re-identification is presented. The method includes extracting, by a body feature extractor, body features from a first data stream, extracting, by a head feature extractor, head features from a second data stream, outputting a body descriptor vector from the body feature extractor, outputting a head descriptor vector from the head feature extractor, and concatenating the body descriptor vector and the head descriptor vector to enable a model to generate a descriptor vector.

Abstract: A method for improving face recognition from unseen domains by learning semantically meaningful representations is presented. The method includes obtaining face images with associated identities from a plurality of datasets, randomly selecting two datasets of the plurality of datasets to train a model, sampling batch face images and their corresponding labels, sampling triplet samples including one anchor face image, a sample face image from a same identity, and a sample face image from a different identity than that of the one anchor face image, performing a forward pass by using the samples of the selected two datasets, finding representations of the face images by using a backbone convolutional neural network (CNN), generating covariances from the representations of the face images and the backbone CNN, the covariances made in different spaces by using positive pairs and negative pairs, and employing the covariances to compute a cross-domain similarity loss function.

Abstract: Methods and systems for training a model include combining data from multiple datasets, the datasets having different respective label spaces. Relationships between labels in the different label spaces are identified. A unified neural network model is trained, using the combined data and the identified relationships to generate a unified model, with a class relational binary cross-entropy loss.

Abstract: A method for multi-task learning via gradient split for rich human analysis is presented. The method includes extracting images from training data having a plurality of datasets, each dataset associated with one task, feeding the training data into a neural network model including a feature extractor and task-specific heads, wherein the feature extractor has a feature extractor shared component and a feature extractor task-specific component, dividing filters of deeper layers of convolutional layers of the feature extractor into N groups, N being a number of tasks, assigning one task to each group of the N groups, and manipulating gradients so that each task loss updates only one subset of filters.