Problem
Research questions and friction points this paper is trying to address.
Uncertainty estimation
Vision-Language-Navigation (VLN)
conformal prediction (CP)
ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation
🤖 AI Summary
针对视觉-语言导航中不确定性估计的问题，提出了一种基于序列归一化的保形预测方法（ENCP），通过调整非一致性分数来提高预测准确性。
📝 Abstract
Uncertainty estimation for Vision-Language-Navigation (VLN) models is a critical task since it can help identify ambiguous and unreliable predictions, enabling agents to make safer navigation decisions. As one of the most advanced uncertainty estimation frameworks, conformal prediction (CP) offers a promising approach for uncertainty estimation in VLN. However, given that VLN agent requires a sequence of steps, standard calibration in conformal prediction fails to provide coverage guarantee it promises over a dependent, variable-length VLN episode. To this end, we propose Episode-Normalized Conformal Prediction (ENCP), which rescales a nonconformity score by the policy's residual confidence and calibrates one maximum score per episode. Under exchangeable calibration and test episodes, this construction covers the ground truth at every step with probability at least $1 - α$, while allowing dependence among steps within an episode. Across four VLN policies and three nonconformity scores on R2R and REVERIE dataset, ENCP meets all reported empirical step-coverage targets on the seen-to-unseen evaluation. These results demonstrate that ENCP can provide model-agnostic uncertainty estimates, which might be useful for determining when a VLN agent should defer to a more capable predictor, including human assistance.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Episode-Normalized Conformal Prediction
uncertainty estimation
Vision-Language-Navigation
residual confidence
coverage guarantee
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