ブラックボックス視覚言語モデルの不確実性を代理ベイズ推定で定量化する視覚グラウンディング
Surrogate Uncertainty for Black-box Visual Grounding
ブラックボックスVLMによる視覚的指示対象の特定に対し、外部の代理ベイズモデルでクエリごとの不確実性を推定し、較正とタスク性能を改善する手法を提案した。
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著者: Hanbo Zhang, Tianrun Hu, Cedar Site Bai, Anxing Xiao, David Hsu
分類: cs.RO
原文アブストラクト
Visual grounding is crucial for robots to understand language instructions with visual observations. This paper addresses the problem of visual grounding uncertainties, where a robot uses a black-box vision-language model (VLM) to ground free-form natural-language instructions to target objects. Prevailing methods derive uncertainty from the VLM itself. Yet, outputs of the VLM, e.g., generated uncertainties, are from the same parameters and structures producing the predictions, and inherit the bias. We give a formal Bayesian definition of the problem that treats the VLM prediction as an observation. An external surrogate Bayesian model then quantifies per-query uncertainty over a local neighborhood of a held-out calibration set, decomposed into aleatoric and epistemic components. A hierarchical retrieval across input modalities realizes a compact joint-space neighborhood, mitigating the curse of dimensionality. The estimator further integrates with conformal prediction or model-based planning for active visual grounding, where robots are allowed to take verbal or physical actions to ground targets. Across grounding benchmarks and state-of-the-art VLMs, it consistently improves both calibration and task performance. On a real Fetch robot under open-world instructions, it enables principled trade-offs between confident action and information gathering, substantially improving robustness.
