MissFormer：用于轨迹过滤和预测的缺失观测的基于（内）注意力的处理
MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction
摘要：
在对象跟踪等应用中，时间序列数据不可避免地会丢失观测值。随着基于深度学习的模型在各种序列学习任务中的成功，这些模型越来越多地取代对象跟踪应用中用于推断对象运动状态的经典方法。虽然传统的跟踪方法可以处理丢失的观测结果，但默认情况下，它们的大多数深度对应方法都不适合这种情况。为此，本文介绍了一种基于变换的方法来处理在可变输入长度的轨迹数据的缺失观测。该模型是通过不断增加所需推理任务的复杂性间接形成的。从再现无噪声轨迹开始，模型然后学习从噪声输入中推断轨迹。通过提供缺失的令牌，二进制编码的缺失事件，模型学习关注缺失的数据，并推断出以剩余输入为条件的完整轨迹。在一系列连续缺失事件的情况下，模型则充当纯预测模型。该方法的能力表现在合成数据和真实世界的数据反映原型对象跟踪的情况。
英文摘要：
In applications such as object tracking, time-series data inevitably carry missing observations. Following the success of deep learning-based models for various sequence learning tasks, these models increasingly replace classic approaches in object tracking applications for inferring the objects' motion states. While traditional tracking approaches can deal with missing observations, most of their deep counterparts are, by default, not suited for this. Towards this end, this paper introduces a transformer-based approach for handling missing observations in variable input length trajectory data. The model is formed indirectly by successively increasing the complexity of the demanded inference tasks. Starting from reproducing noise-free trajectories, the model then learns to infer trajectories from noisy inputs. By providing missing tokens, binary-encoded missing events, the model learns to in-attend to missing data and infers a complete trajectory conditioned on the remaining inputs. In the case of a sequence of successive missing events, the model then acts as a pure prediction model. The abilities of the approach are demonstrated on synthetic data and real-world data reflecting prototypical object tracking scenarios.
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。
