Conformal Coverage Guarantees for Any Video Temporal Grounder

TL;DR - COVER is a post-hoc, model-agnostic conformal prediction wrapper that converts any video temporal grounder into one that outputs a temporal region guaranteed to contain the true moment with probability ≥ 1−α. It matters because event boundaries are inherently ambiguous — annotators often overlap by less than half — so single-interval predictions hide reliability that calibrated regions expose.

• Calibrates a quantile of a temporal nonconformity score on held-out labels and widens the base prediction accordingly; the guarantee is finite-sample and distribution-free under exchangeability, with no retraining or white-box access required.
• Two score families are provided: a two-sided boundary-widening score for interval-emitting grounders, and a super-level-set score for grounders emitting a relevance signal.
• Grounding-specific theory bounds certified region size, characterizes when coverage survives conditioning on event length, and analyzes degradation when multiple moments from one video break exchangeability.
• Evaluated across three benchmarks and five grounders (trained localizers and black-box video–language models), realized coverage tracks the target, and calibration reveals failure modes that point metrics obscure.