no code implementations • 16 May 2024 • Zenglin Shi, Pei Liu, Tong Su, Yunpeng Wu, Kuien Liu, Yu Song, Meng Wang
It partitions the output logits of the model into dense groups, each corresponding to a task in the task pool.
no code implementations • 5 Apr 2024 • Tong Su, Xin Peng, Sarubi Thillainathan, David Guzmán, Surangika Ranathunga, En-Shiun Annie Lee
Parameter-efficient fine-tuning (PEFT) methods are increasingly vital in adapting large-scale pre-trained language models for diverse tasks, offering a balance between adaptability and computational efficiency.
no code implementations • CVPR 2023 • Xishun Wang, Tong Su, Fang Da, Xiaodong Yang
To cope with these difficulties, this paper proposes a novel agent-centric model with anchor-informed proposals for efficient multimodal motion prediction.
no code implementations • 13 Jul 2022 • Tong Su, Xishun Wang, Xiaodong Yang
To safely navigate in various complex traffic scenarios, autonomous driving systems are generally equipped with a motion forecasting module to provide vital information for the downstream planning module.
no code implementations • 13 Dec 2021 • Tong Su, Yu Meng, Yan Xu
As a core technology of the autonomous driving system, pedestrian trajectory prediction can significantly enhance the function of active vehicle safety and reduce road traffic injuries.