Shifts Challenge: Robustness and Uncertainty under Real-World Distributional Shift is a competition held by NeurIPS 2021. The objective of this competition is to search for methods to solve the motion prediction problem in cross-domain. In the real world dataset, It exists variance between input data distribution and ground-true data distribution, which is called the domain shift problem. In this report, we propose a new architecture inspired by state of the art papers. The main contribution is the backbone architecture with self-attention mechanism and predominant loss function. Subsequently, we won 3rd place as shown on the leaderboard.
翻译:挑战:在Real-World分布式转变下,强势和不确定性是NeurIPS 2021公司进行的一项竞争。这次竞争的目的是寻找解决跨领域运动预测问题的方法。在真实的世界数据集中,输入数据分布和地真数据分布之间存在差异,这被称为领域转移问题。在本报告中,我们提出了一个由最新文件启发的新架构。主要贡献是具有自我注意机制和主要损失功能的骨干结构。随后,我们赢得了领导板上显示的第三位。