论文标题
Freegaze：通过频域对比学习的资源有效凝视估算
FreeGaze: Resource-efficient Gaze Estimation via Frequency Domain Contrastive Learning
论文作者
论文摘要
凝视估计对于许多科学领域和日常应用至关重要，范围从认知心理学的基本研究到注意感知移动系统。尽管深度学习的最新进展在建立高度准确的凝视估计系统方面取得了巨大的成功，但相关的高计算成本以及对大规模标记的凝视数据的依赖，以实现对现有解决方案实际使用的监督学习地点挑战。为了超越这些局限性，我们提出了FreeGaze，这是一个无监督的注视代表学习的资源效率框架。 FreeGaze在其设计中结合了频域目光的估计和对比凝视的表示。前者大大减轻了系统校准和凝视估计中的计算负担，并大大减少了系统延迟。尽管后者克服了现有基于学习的同行的数据标记障碍，并确保在没有凝视标签的情况下确保有效的凝视表示学习。我们对两个凝视估算数据集的评估表明，通过现有基于监督的学习方法，FreeGaze可以实现可比较的凝视估计精度，同时可以分别实现6.81和1.67倍的系统校准和注视估计的速度。
Gaze estimation is of great importance to many scientific fields and daily applications, ranging from fundamental research in cognitive psychology to attention-aware mobile systems. While recent advancements in deep learning have yielded remarkable successes in building highly accurate gaze estimation systems, the associated high computational cost and the reliance on large-scale labeled gaze data for supervised learning place challenges on the practical use of existing solutions. To move beyond these limitations, we present FreeGaze, a resource-efficient framework for unsupervised gaze representation learning. FreeGaze incorporates the frequency domain gaze estimation and the contrastive gaze representation learning in its design. The former significantly alleviates the computational burden in both system calibration and gaze estimation, and dramatically reduces the system latency; while the latter overcomes the data labeling hurdle of existing supervised learning-based counterparts, and ensures efficient gaze representation learning in the absence of gaze label. Our evaluation on two gaze estimation datasets shows that FreeGaze can achieve comparable gaze estimation accuracy with existing supervised learning-based approach, while enabling up to 6.81 and 1.67 times speedup in system calibration and gaze estimation, respectively.
