VIOLA: Towards Video In-Context Learning with Minimal Annotations

• URL: http://arxiv.org/abs/2601.15549v1
• Date: Thu, 22 Jan 2026 00:35:30 GMT
• Title: VIOLA: Towards Video In-Context Learning with Minimal Annotations
• Authors: Ryo Fujii, Hideo Saito, Ryo Hachiuma,
• Abstract summary: We introduce VIOLA, a framework that synergizes minimal expert supervision with abundant unlabeled data.<n>Our framework significantly outperforms various baselines in low-resource settings, achieving robust adaptation with minimal annotation costs.
• Score: 20.810620293371027
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Generalizing Multimodal Large Language Models (MLLMs) to novel video domains is essential for real-world deployment but remains challenging due to the scarcity of labeled data. While In-Context Learning (ICL) offers a training-free adaptation path, standard methods rely on large annotated pools, which are often impractical in specialized environments like industrial or surgical settings since they require the experts' annotations. To bridge this gap, we introduce VIOLA (Video In-cOntext Learning with minimal Annotation), a label-efficient framework that synergizes minimal expert supervision with abundant unlabeled data. First, to maximize the efficiency of a strict annotation budget, we propose density-uncertainty-weighted sampling. Unlike standard diversity or uncertainty strategies that risk selecting visual outliers, our method leverages density estimation to identify samples that are simultaneously diverse, representative, and informative. Second, to utilize the remaining unlabeled data without noise propagation, we construct a hybrid pool and introduce confidence-aware retrieval and confidence-aware prompting. These mechanisms explicitly model label reliability, retrieving demonstrations based on a composite score of similarity and confidence while enabling the MLLM to adaptively distinguish between verified ground truths and noisy pseudo-labels. Extensive experiments across nine diverse benchmarks using four MLLMs demonstrate that our framework significantly outperforms various baselines in low-resource settings, achieving robust adaptation with minimal annotation costs.

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