RAIN：用于运动预测的增强型混合注意力推理网络
RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting
摘要：
运动预测在各种领域（例如，自动驾驶、人机交互），其目的是在给定一组历史观察的情况下预测未来的运动序列。然而，观察到的要素可能具有不同的重要性水平。在某些情况下，一些信息可能与预测无关，甚至会分散预测的注意力。为了解决这个问题，我们提出了一个通用的运动预测框架（命名为RAIN）与动态的关键信息选择和排名的基础上的混合注意力机制。一般框架实例化处理多智能体轨迹预测和人体运动预测任务，分别。在前一个任务中，模型学习用图形表示来识别代理之间的关系，并确定它们的相对重要性。在后一项任务中，模型学习捕获长期人类运动中的时间接近性和依赖性。我们还提出了一种有效的双阶段训练管道，并采用交替训练策略来优化框架不同模块中的参数。我们在不同领域的合成模拟和运动预测基准上验证了该框架，证明了我们的方法不仅实现了最先进的预测性能，而且提供了可解释的和合理的混合注意力权重。
英文摘要：
Motion forecasting plays a significant role in various domains (e.g., autonomous driving, human-robot interaction), which aims to predict future motion sequences given a set of historical observations. However, the observed elements may be of different levels of importance. Some information may be irrelevant or even distracting to the forecasting in certain situations. To address this issue, we propose a generic motion forecasting framework (named RAIN) with dynamic key information selection and ranking based on a hybrid attention mechanism. The general framework is instantiated to handle multi-agent trajectory prediction and human motion forecasting tasks, respectively. In the former task, the model learns to recognize the relations between agents with a graph representation and to determine their relative significance. In the latter task, the model learns to capture the temporal proximity and dependency in long-term human motions. We also propose an effective double-stage training pipeline with an alternating training strategy to optimize the parameters in different modules of the framework. We validate the framework on both synthetic simulations and motion forecasting benchmarks in different domains, demonstrating that our method not only achieves state-of-the-art forecasting performance, but also provides interpretable and reasonable hybrid attention weights.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
