2019/05/06 · A novel framework for Few-shot Adaptive GaZE Estimation (FAZE) for learning person-specific gaze networks with very few (less than or equal to 9) calibration ...

FAZE learns a rotation- aware latent representation of gaze via a disentangling encoder-decoder architecture along with a highly adaptable gaze estimator ...

FAZE is a framework for few-shot adaptation of gaze estimation networks, consisting of equivariance learning (via the DT-ED or Disentangling Transforming ...

個人間解剖学的差異は,個人に依存しない注視推定ネットワークの精度を制限する。しかし,より高い品質を必要とする応用を可能にするためには,より低い注視誤差の必要性 ...

FAZE learns a rotation- aware latent representation of gaze via a disentangling encoder-decoder architecture along with a highly adaptable gaze estimator ...

Faze learns a rotation-aware latent representation of gaze via a disentangling encoder-decoder architecture along with a highly adaptable gaze estimator.

A novel and effective algorithm for training gaze networks with very few (less than 10) training examples per subject to create highly accurate, personalized ...

Few-Shot Adaptive Gaze Estimation (Supplementary Material). Seonwook Park12*, Shalini De Mello1*, Pavlo Molchanov1, Umar Iqbal1, Otmar Hilliges2, Jan Kautz1.

The proposed method achieves higher accuracy than conventional methods in a very different domain problem setting, where RGB images are the source domain, and ...

To solve this, we present a novel and effective algorithm for training gaze networks with very few (less than 10) training examples per subject to create highly ...