7 accepted papers
In this paper we describe a novel framework for diffusion-based generative modeling on constrained spaces. In particular, we introduce manual bridges, a framework that expands the kinds of constraints that can be practically used to form so-called diffusion bridges. We develop a mechanism for combin…
Lane detection plays a crucial role in autonomous driving systems, enabling vehicles to navigate safely and efficiently in complex environment. Despite significant advancements in recent years, accurate lane detection remains a challenging task, particularly in scenarios with occlusions, ambiguous l…
Despite the remarkable accomplishments of graph neural networks (GNNs), they typically rely on task-specific labels, posing potential challenges in terms of their acquisition. Existing work have been made to address this issue through the lens of unsupervised domain adaptation, wherein labeled sourc…
Score-based diffusion models are a powerful class of generative models, widely utilized across diverse domains. Despite significant advancements in large-scale tasks such as text-to-image generation, their application to constrained domains has received considerably less attention. This work address…
Score function estimation is the cornerstone of both training and sampling from diffusion generative models. Despite this fact, the most commonly used estimators are either biased neural network approximations or high variance Monte Carlo estimators based on the conditional score. We introduce a nov…
In recent advancements within the domain of three-dimensional semantic mapping, a novel framework termed SemanticMapper has emerged, heralding a paradigm shift in the arena of mesh localization through textual inputs. The core innovation of SemanticMapper lies in its adeptness at navigating and accu…
Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of prerecorded real-world traffic scenarios in simulation ensures realism but the rarity of safety critical events makes large scale collection of driving scenarios…