论文标题
DEESCO：深度异构合奏，具有随机组合损失，以进行凝视
DeeSCo: Deep heterogeneous ensemble with Stochastic Combinatory loss for gaze estimation
论文作者
论文摘要
从医学研究到游戏应用程序，凝视估计已成为有价值的工具。尽管存在许多基于硬件的解决方案，但最近的基于深度学习的方法，再加上大规模数据库的可用性，允许仅使用消费者传感器提供精确的凝视估算。但是，关于问题的提出，建筑选择和学习范式设计凝视估计系统的范式仍然存在许多问题，以弥合仅使用消费者传感器的特定硬件和方法之间的基于几何的系统之间的差距。在本文中，我们引入了基于热图的弱预测变量的深度，端到端可训练的集合，以进行2D/3D注视估计。我们表明，通过这些弱预测变量的异质体系结构设计，我们可以改善后一个预测指标之间的非相关性，以设计更强大的深层集成模型。此外，我们提出了一种随机组合损失，该损失包括在火车时对弱预测变量进行随机采样组合。这允许训练更好的单个弱预测变量，而它们之间的相关性较低。反过来，这可以显着提高深层合奏的性能。我们表明，与随机组合损失（DEESCO）在多个数据集上的2D/3D凝视估计的最先进的方法相比，我们的深层异质集合优于最先进的方法。
From medical research to gaming applications, gaze estimation is becoming a valuable tool. While there exists a number of hardware-based solutions, recent deep learning-based approaches, coupled with the availability of large-scale databases, have allowed to provide a precise gaze estimate using only consumer sensors. However, there remains a number of questions, regarding the problem formulation, architectural choices and learning paradigms for designing gaze estimation systems in order to bridge the gap between geometry-based systems involving specific hardware and approaches using consumer sensors only. In this paper, we introduce a deep, end-to-end trainable ensemble of heatmap-based weak predictors for 2D/3D gaze estimation. We show that, through heterogeneous architectural design of these weak predictors, we can improve the decorrelation between the latter predictors to design more robust deep ensemble models. Furthermore, we propose a stochastic combinatory loss that consists in randomly sampling combinations of weak predictors at train time. This allows to train better individual weak predictors, with lower correlation between them. This, in turns, allows to significantly enhance the performance of the deep ensemble. We show that our Deep heterogeneous ensemble with Stochastic Combinatory loss (DeeSCo) outperforms state-of-the-art approaches for 2D/3D gaze estimation on multiple datasets.
