论文标题
近红外估计的对比度加权学习
Contrastive Weighted Learning for Near-Infrared Gaze Estimation
论文作者
论文摘要
基于外观的凝视估计非常成功。以下许多作品改善了域的概述，以进行凝视估计。但是，尽管凝视估计的领域概括取得了很大进展，但最近的大多数工作都集中在跨数据库的性能上 - 考虑了照明，姿势和照明方面的不同分布。尽管RGB图像不同分布的不同分布的凝视估计很重要，但基于近红外图像的凝视估计对于黑暗环境中的凝视估计也至关重要。另外，仅依靠监督学习来进行回归任务的固有局限性。本文有助于解决这些问题，并提出了GazeCWL，这是使用对比度学习的近红外图像进行凝视估算的新型框架。这利用对抗性攻击技术来扩大数据，并专门针对回归任务的新颖对比度损失功能，这些损失功能有效地将不同样品在潜在空间中的特征簇起来。我们的模型在基于红外图像的凝视估计中优于先前的域概括模型，并以45.6 \％的速度优于基线，同时将最新的ART提高8.6 \％，我们证明了我们方法的功效。
Appearance-based gaze estimation has been very successful with the use of deep learning. Many following works improved domain generalization for gaze estimation. However, even though there has been much progress in domain generalization for gaze estimation, most of the recent work have been focused on cross-dataset performance -- accounting for different distributions in illuminations, head pose, and lighting. Although improving gaze estimation in different distributions of RGB images is important, near-infrared image based gaze estimation is also critical for gaze estimation in dark settings. Also there are inherent limitations relying solely on supervised learning for regression tasks. This paper contributes to solving these problems and proposes GazeCWL, a novel framework for gaze estimation with near-infrared images using contrastive learning. This leverages adversarial attack techniques for data augmentation and a novel contrastive loss function specifically for regression tasks that effectively clusters the features of different samples in the latent space. Our model outperforms previous domain generalization models in infrared image based gaze estimation and outperforms the baseline by 45.6\% while improving the state-of-the-art by 8.6\%, we demonstrate the efficacy of our method.
