The large-scale deployment of autonomous vehicles is yet to come, and one of the major remaining challenges lies in urban dense traffic scenarios.
Inspired by this, we propose ASAP-RL, an efficient reinforcement learning algorithm for autonomous driving that simultaneously leverages motion skills and expert priors.
Large-scale deployment of autonomous vehicles has been continually delayed due to safety concerns.
Ranked #1 on Autonomous Driving on CARLA Leaderboard
This paper pays close attention to the cross-modality visible-infrared person re-identification (VI Re-ID) task, which aims to match pedestrian samples between visible and infrared modes.
Inspired by spatial-based contrastive SSL, we show that significant improvement can be achieved by a proposed temporal-based contrastive learning approach, which includes three novel and efficient modules: temporal augmentations, temporal memory bank and SSTL loss.
Specifically, in order to generate high-quality proposals, we consider several factors including the video feature encoder, the proposal generator, the proposal-proposal relations, the scale imbalance, and ensemble strategy.
This technical report introduces our winning solution to the spatio-temporal action localization track, AVA-Kinetics Crossover, in ActivityNet Challenge 2020.
In this technical report, we briefly introduce the solutions of our team 'Efficient' for the Multi-Moments in Time challenge in ICCV 2019.
no code implementations • 9 Mar 2020 • Xianpei Han, Zhichun Wang, Jiangtao Zhang, Qinghua Wen, Wenqi Li, Buzhou Tang, Qi. Wang, Zhifan Feng, Yang Zhang, Yajuan Lu, Haitao Wang, Wenliang Chen, Hao Shao, Yubo Chen, Kang Liu, Jun Zhao, Taifeng Wang, Kezun Zhang, Meng Wang, Yinlin Jiang, Guilin Qi, Lei Zou, Sen Hu, Minhao Zhang, Yinnian Lin
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks.
In this paper, we present the task definition, the description of data and the evaluation methodology used during this shared task.