Target-Checked Reliability Score Refinement for Video Question Answering

• URL: http://arxiv.org/abs/2609.13288v1
• Date: Wed, 09 Sep 2026 08:29:00 GMT
• Title: Target-Checked Reliability Score Refinement for Video Question Answering
• Abstract summary: We study whether answer-level reliability scores can be improved under target shift without retraining the models or changing their answers.<n>We collect option-probability lists from three fixed video-language models under four deterministic video samplings.<n>We compare the original score, defined as the probability assigned to the chosen answer, with a histogram-based gradient-boosting (HGB) score trained on the development datasets.<n>A candidate replaces the original score only when repeated video-level checks indicate a positive, stable improvement.
• Score: 3.8271803328378677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video-language models can answer multiple-choice questions with high confidence yet be wrong. We study whether answer-level reliability scores can be improved under target shift without retraining the models or changing their answers. We collect option-probability lists from three fixed video-language models under four deterministic video samplings and represent cross-view changes and cross-model agreement as a response graph. Using a labeled target pilot, we compare the original score, defined as the probability assigned to the chosen answer, with a histogram-based gradient-boosting (HGB) score trained on the development datasets and a regularized logistic-regression score trained on the target pilot. A candidate replaces the original score only when repeated video-level checks indicate a positive, stable improvement. We develop this rule on public VideoQA benchmarks and Video Hallucination Diagnosis (VHD), a controlled diagnostic dataset for shared high-confidence errors. Ranking quality is measured by the area under the risk-coverage curve (AURC), where lower is better. On a held-out 963-question HERBench split, the method reduces mean AURC across the three models by 16.64% (95% confidence interval (CI), 12.12 to 22.61%); the smallest model-level gain is 11.39%. On a separate held-out 911-question Perception Test split, the mean reduction is 18.87% (95% CI, 15.43 to 22.14%). For InternVL3.5, the target check retains the original scores. Using the same outputs, the method outperforms seven training-free baselines in mean AURC on both datasets. It also improves AUROC, reduces calibration error, and lowers the error rate at 50% coverage by 6.50 and 6.58 percentage points.

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