  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.
