论文标题
基于外观的凝视估计的粗线自适应网络
A Coarse-to-Fine Adaptive Network for Appearance-Based Gaze Estimation
论文作者
论文摘要
人类目光对于各种吸引人的应用至关重要。旨在以更准确的凝视估计，一系列最近的作品提出了同时利用面部和眼睛图像。然而，面部和眼睛图像仅是这些作品中的独立或平行特征来源，其特征之间的内在相关性被忽略了。在本文中，我们做出以下贡献：1）我们提出了一种粗到十的策略，该策略估算了面部图像的基本凝视方向，并通过眼镜图像预测的相应残留物进行了完善。 2）在拟议的策略的指导下，我们设计了一个框架，该框架引入了Bi-gram模型，以桥接凝视的残留和基本凝视方向，以及一个适应性地获得合适的细颗粒功能的注意力组成部分。 3）整合上述创新，我们构建了一个名为CA-NET的粗到精细的自适应网络，并在Mpiigaze和Eyediap上实现最先进的表演。
Human gaze is essential for various appealing applications. Aiming at more accurate gaze estimation, a series of recent works propose to utilize face and eye images simultaneously. Nevertheless, face and eye images only serve as independent or parallel feature sources in those works, the intrinsic correlation between their features is overlooked. In this paper we make the following contributions: 1) We propose a coarse-to-fine strategy which estimates a basic gaze direction from face image and refines it with corresponding residual predicted from eye images. 2) Guided by the proposed strategy, we design a framework which introduces a bi-gram model to bridge gaze residual and basic gaze direction, and an attention component to adaptively acquire suitable fine-grained feature. 3) Integrating the above innovations, we construct a coarse-to-fine adaptive network named CA-Net and achieve state-of-the-art performances on MPIIGaze and EyeDiap.
