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Found 9 papers, 3 papers with code
Segment, Select, Correct: A Framework for Weakly-Supervised Referring Segmentation
Referring Image Segmentation (RIS) - the problem of identifying objects in images through natural language sentences - is a challenging task currently mostly solved through supervised learning.
Faithful Knowledge Distillation
To address these questions, we introduce a faithful imitation framework to discuss the relative calibration of confidences and provide empirical and certified methods to evaluate the relative calibration of a student w. r. t.
Provably Correct Physics-Informed Neural Networks
Recent work provides promising evidence that Physics-informed neural networks (PINN) can efficiently solve partial differential equations (PDE).
Certifying Ensembles: A General Certification Theory with S-Lipschitzness
Improving and guaranteeing the robustness of deep learning models has been a topic of intense research.
ANCER: Anisotropic Certification via Sample-wise Volume Maximization
Randomized smoothing has recently emerged as an effective tool that enables certification of deep neural network classifiers at scale.
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving
In this paper, we present PILOT -- a planning framework that comprises an imitation neural network followed by an efficient optimiser that actively rectifies the network's plan, guaranteeing fulfilment of safety and comfort requirements.
Integrating Planning and Interpretable Goal Recognition for Autonomous Driving
The ability to predict the intentions and driving trajectories of other vehicles is a key problem for autonomous driving.
Robotics
PaRoT: A Practical Framework for Robust Deep Neural Network Training
Deep Neural Networks (DNNs) are finding important applications in safety-critical systems such as Autonomous Vehicles (AVs), where perceiving the environment correctly and robustly is necessary for safe operation.
Analytical Modeling of Vanishing Points and Curves in Catadioptric Cameras
Vanishing points and vanishing lines are classical geometrical concepts in perspective cameras that have a lineage dating back to 3 centuries.
