Dynamic multimodal learning improves cross-modal data understanding
Dynamic Alignment and Calibration for Multimodal Learning
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Multimodal learning involves combining information from different types of data, like images and text, to make smarter decisions. The authors found that existing methods often treat all data samples the same way, which can cause mistakes when the data from different sources doesn't match up well. They created a new system that adjusts how it compares and combines data based on how confident it is in each part, making the overall learning process more flexible and accurate. This approach helps computers better understand and use information from multiple data sources.
What this means in practice
- •For multimodal ai developers: Improve robustness of AI systems that combine images, text, or audio by dynamically adjusting how different data types are aligned and weighted during learning.
- •For software engineers in computer vision: Enhance multimodal fusion methods in vision applications when input data has varying confidence and scale, reducing errors from uniform fusion approaches.
Authors
Jinghao Xu, Zhenhua Guo, Xiaofeng Zhu, Xiaoshuang Shi
Abstract
Dynamic multimodal learning aims to learn robust representations by adaptively modeling information discrepancies across modalities. 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. For modality pairs with significant feature magnitude differences or small confidence gaps, it might be unreliable to strictly align fusion weights according to confidence. To address these issues, we propose an Alignment- and Calibration-driven Multimodal Learning framework (ACML). 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. 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. Extensive experiments on multiple multimodal benchmark datasets demonstrate that ACML consistently achieves superior performance and robustness over recent state-of-the-art methods.