提出自适应状态空间模型，提升事件流眼特征估计稳定性。
Enhancing Eye Feature Estimation from Event Data Streams through Adaptive Inference State Space Modeling
- 动态调整当前与近期信息权重，应对眼动变化带来的事件密度波动。
- 在真实数据集上实现比现有方法更高的眼特征估计精度。
- 适合需要低功耗实时眼动追踪的嵌入式系统应用。
基于事件的数据流可高效、低功耗地进行眼特征提取，对实际眼动追踪系统具有重要意义。然而，现有方法难以应对眼动行为动力学变化引起的事件密度突变，导致预测性能下降。本文提出自适应推理状态空间模型（AISSM），通过互补的动态置信网络估计信噪比和事件密度，动态调整当前与近期信息的相对权重。此外，设计了一种新型训练技术，提升训练效率。实验表明，AISSM 在事件流眼特征提取任务中优于当前最优模型。
原文摘要 · Abstract (English)
Eye feature extraction from event-based data streams can be performed efficiently and with low energy consumption, offering great utility to real-world eye tracking pipelines. However, few eye feature extractors are designed to handle sudden changes in event density caused by the changes between gaze behaviors that vary in their kinematics, leading to degraded prediction performance. In this work, we address this problem by introducing the adaptive inference state space model (AISSM), a novel architecture for feature extraction that is capable of dynamically adjusting the relative weight placed on current versus recent information. This relative weighting is determined via estimates of the signal-to-noise ratio and event density produced by a complementary dynamic confidence network. Lastly, we craft and evaluate a novel learning technique that improves training efficiency. Experimental results demonstrate that the AISSM system outperforms state-of-the-art models for event-based eye feature extraction.
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