4 papers
MM-Future: Multi-Mode Joint World-Action Modeling for Autonomous Driving
Shuai Liu, Hechangle Gong, Hao Jiang +5
Autonomous driving involves coupled decision-making and scene evolution under multi-mode uncertainty. To capture this coupling and uncertainty, we introduce MM-Future, a world-acti…
UniDWM: Towards a Unified Driving World Model via Multifaceted Representation Learning
Shuai Liu, Siheng Ren, Xiaoyao Zhu +5
Achieving reliable and efficient planning in complex driving environments requires a model that can reason over the scene's geometry, appearance, and dynamics. We present UniDWM, a…
Efficient Event Stream Super-Resolution with Recursive Multi-Branch Fusion
Quanmin Liang, Zhilin Huang, Xiawu Zheng +4
Current Event Stream Super-Resolution (ESR) methods overlook the redundant and complementary information present in positive and negative events within the event stream, employing…
Bilateral Event Mining and Complementary for Event Stream Super-Resolution
Zhilin Huang, Quanmin Liang, Yijie Yu +5
Event Stream Super-Resolution (ESR) aims to address the challenge of insufficient spatial resolution in event streams, which holds great significance for the application of event c…