论文标题
一种用于估计人群运动预测中场景概括的信息理论方法
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction
论文作者
论文摘要
基于学习的建模人群运动的方法已变得越来越成功，但需要在大型数据集上进行培训和评估，再加上复杂的模型选择和参数调整。为了避免这一耗时的耗时过程，我们提出了一种新的评分方法，该方法是对在源人群场景中训练的模型的概括，并使用无训练的，模型 - 不合Snostic的互动 +多样性量化得分应用于目标人群场景，ISDQ。交互部分旨在表征场景域的难度，而情景域的多样性则以多样性得分捕获。可以以计算方式计算两个分数。我们的实验结果验证了所提出的方法对几个模拟和现实世界（源，目标）概括任务的功效，这证明了其在训练和测试模型之前选择最佳域对的潜力。
Learning-based approaches to modeling crowd motion have become increasingly successful but require training and evaluation on large datasets, coupled with complex model selection and parameter tuning. To circumvent this tremendously time-consuming process, we propose a novel scoring method, which characterizes generalization of models trained on source crowd scenarios and applied to target crowd scenarios using a training-free, model-agnostic Interaction + Diversity Quantification score, ISDQ. The Interaction component aims to characterize the difficulty of scenario domains, while the diversity of a scenario domain is captured in the Diversity score. Both scores can be computed in a computation tractable manner. Our experimental results validate the efficacy of the proposed method on several simulated and real-world (source,target) generalization tasks, demonstrating its potential to select optimal domain pairs before training and testing a model.
