Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data

📰 ArXiv cs.AI

Learn how to efficiently acquire cross-modal alignments for multimodal active learning with limited paired data, reducing annotation costs in deep learning

advanced Published 25 Apr 2026

Action Steps

Implement a multimodal active learning framework to acquire cross-modal alignments
Use unaligned data to reduce annotation burden
Apply active learning strategies to select the most informative samples for annotation
Configure the framework to handle different modalities and data distributions
Test the framework on a multimodal dataset to evaluate its efficiency and effectiveness

Who Needs to Know This

Machine learning engineers and researchers working on multimodal learning tasks can benefit from this framework to reduce annotation costs and improve model performance

Key Insight

💡 Multimodal active learning can efficiently acquire cross-modal alignments with limited paired data, reducing annotation costs in deep learning

Key Takeaways

Learn how to efficiently acquire cross-modal alignments for multimodal active learning with limited paired data, reducing annotation costs in deep learning

Full Article

Title: Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data

Abstract:
arXiv:2510.03247v2 Announce Type: replace-cross Abstract: Active learning (AL) is a principled strategy to reduce annotation cost in data-hungry deep learning. However, existing AL algorithms focus almost exclusively on unimodal data, overlooking the substantial annotation burden in multimodal learning. We introduce the first framework for multimodal active learning with unaligned data, where the learner must actively acquire cross-modal alignments rather than labels on pre-aligned pairs. This s

Read full paper → ← Back to Reads