Dynamic Alignment and Calibration for Multimodal Learning

Key points

• Dynamic multimodal learning aims to learn robust representations by adaptively modeling information discrepancies across modalities.1
• However, existing methods still suffer from two limitations: (i) static cross-modal alignment strategies usually impose uniform constraints on all samples while overlooking sample-wise variations, potentially leading to unreasonable over-alignment; and (ii) confidence- or uncertainty-aware fusion methods often fail to adequately account for feature magnitude and confidence differences across modalities.1
• Specifically, ACML incorporates a dynamic cross-modal triplet alignment module, which enforces strong semantic consistency for high-confidence positive pairs while encouraging diverse representation learning between high- and low-confidence positive pairs according to their confidence gaps.1
• Additionally, ACML introduces a difference-aware attention calibration strategy that adaptively adjusts attention regularization based on feature magnitude and confidence differences across modalities, thereby mitigating biases caused by unreasonable fusion constraints.1

Sources (1)

• [1]Dynamic Alignment and Calibration for Multimodal Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 08:05 AM

  To address these issues, we propose an Alignment- and Calibration-driven Multimodal Learning framework (ACML).

  Dynamic multimodal learning aims to learn robust representations by adaptively modeling information discrepancies across modalities.

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