Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Widely observed neural scaling laws, in which error falls off as a power of the training set
size, model size, or both, have driven substantial performance improvements in deep …

How should we integrate representations from complementary sensors for autonomous
driving? Geometry-based fusion has shown promise for perception (eg, object detection …

Over recent years, an increasing amount of compute and data has been poured into training
large language models (LLMs), usually by doing one-pass learning on as many tokens as …

Active learning aims to reduce labeling costs by selecting only the most informative samples
on a dataset. Few existing works have addressed active learning for object detection. Most …

Visual recognition is currently one of the most important and active research areas in
computer vision, pattern recognition, and even the general field of artificial intelligence. It …

Deep Neural Networks trained in a fully supervised fashion are the dominant technology in
perception-based autonomous driving systems. While collecting large amounts of unlabeled …

Although deep learning has made great progress in recent years, the exploding economic
and environmental costs of training neural networks are becoming unsustainable. To …

Abstract Self-Supervised Learning (SSL) has emerged as the solution of choice to learn
transferable representations from unlabeled data. However, SSL requires to build samples …

Real-time safety assessment of dynamic systems has recently received increasing attention.
However, the performance of existing advanced approaches is often negatively affected by …