How Much Future Helps? A Controlled Study of Future-Privileged Supervision for Causal Egocentric Gaze Estimation

2026-07-01

AI Summary

This work addresses the performance limitations of causal (online) gaze estimation methods, which cannot leverage future frames during inference. To mitigate this constraint, the authors propose a controlled training framework that employs an auxiliary branch with adjustable future context to supervise a causal backbone model. During inference, only the strictly causal architecture is retained, enabling isolation and quantification of the impact of future contextual information. The study systematically reveals, for the first time, a non-monotonic relationship between future context and performance gains in causal gaze estimation, with peak improvements observed within specific temporal windows (1.7–3.3 seconds on EGTEA Gaze+ and 2.7 seconds on Ego4D). Experimental results demonstrate that this future-privileged supervision mechanism substantially enhances the prediction accuracy of lightweight causal models.

Abstract

Egocentric gaze estimation is commonly studied using models that process the full video with access to future frames, while real-world applications require strictly causal, online prediction. This discrepancy raises key questions: Does future context inherently provide valuable signals for gaze estimation? If so, how much future look-ahead optimally supervises a causal model during training? To investigate, we propose a controlled framework featuring a future-aware branch that accesses a tunable look-ahead horizon during training but is discarded at inference. This design isolates the impact of future context while keeping the inference architecture fixed and strictly causal. Across EGTEA Gaze+ and Ego4D, we find that future-privileged supervision consistently improves causal gaze prediction, confirming its utility. However, performance gains do not increase monotonically with longer look-ahead, but rather peak within a bounded temporal regime. Specifically, optimal performance corresponds to roughly 1.7--3.3 seconds of future context ($H{\in}[5, 10]$) on EGTEA Gaze+ and 2.7 seconds ($H{=}10$) on Ego4D. Our results demonstrate that lightweight causal models can effectively absorb future-aware signals, providing practical guidance for real-time egocentric gaze modeling.

Problem

• egocentric gaze estimation
• causal prediction
• future-privileged supervision
• temporal context
• online inference

Innovation

• future-privileged supervision
• causal gaze estimation
• egocentric vision
• temporal look-ahead
• controlled training framework