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Found 9 papers, 2 papers with code
Promptable Closed-loop Traffic Simulation
Simulation stands as a cornerstone for safe and efficient autonomous driving development.
Wolf: Captioning Everything with a World Summarization Framework
Finally, we establish a benchmark for video captioning and introduce a leaderboard, aiming to accelerate advancements in video understanding, captioning, and data alignment.
Language Conditioned Traffic Generation
In this work, we turn to language as a source of supervision for dynamic traffic scene generation.
SceneGen: Learning to Generate Realistic Traffic Scenes
Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synthesized traffic scenes versus real ones.
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
We first utilize ray casting over the 3D scene and then use a deep neural network to produce deviations from the physics-based simulation, producing realistic LiDAR point clouds.
Class-imbalanced Domain Adaptation: An Empirical Odyssey
Unsupervised domain adaptation is a promising way to generalize deep models to novel domains.
Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment
In this paper, we explore the task of Generalized Domain Adaptation (GDA): How to transfer knowledge across different domains in the presence of both covariate and label shift?
Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration
Thus, it is meaningful to let partially labeled domains learn from each other to classify all the unlabeled samples in each domain under an open-set setting.
