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Conformal Coverage Guarantees for Any Video Temporal Grounder

Research Multimodal & Generative

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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.

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Conformal Coverage Guarantees for Any Video Temporal Grounder

arXiv cs.CV Aseel Mohamed, Rasul Khanbayov, Erchin Serpedin, Hasan Kurban 2026-08-07 arXiv:2608.07434
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-31 14:24:32.286128 UTC

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.
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