Robust Multimodal Learning via Entropy-Gated Contrastive Fusion

Leon Chlon; Maggie Chlon; MarcAntonio M. Awada

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arXiv · 2505.15417

Robust Multimodal Learning via Entropy-Gated Contrastive Fusion

Leon Chlon·Maggie Chlon·MarcAntonio M. Awada

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Abstract

Real-world multimodal systems routinely face missing-input scenarios, and in reality, robots lose audio in a factory or a clinical record omits lab tests at inference time. Standard fusion layers either preserve robustness or calibration but never both. We introduce Adaptive Entropy-Gated Contrastive Fusion (AECF), a single light-weight layer that (i) adapts its entropy coefficient per instance, (ii) enforces monotone calibration across all modality subsets, and (iii) drives a curriculum mask directly from training-time entropy. On AV-MNIST and MS-COCO, AECF improves masked-input mAP by +18 pp at a 50% drop rate while reducing ECE by up to 200%, yet adds 1% run-time. All back-bones remain frozen, making AECF an easy drop-in layer for robust, calibrated multimodal inference.

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BibTeX RIS

Leon Chlon, Maggie Chlon, MarcAntonio M. Awada. 2025-05-21. Robust Multimodal Learning via Entropy-Gated Contrastive Fusion. https://arxiv.org/abs/2505.15417

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