AI & ML
Robust Multimodal Learning via Entropy-Gated Contrastive Fusion
arXiv stat.ML0 views
arXiv:2505.15417v2 Announce Type: replace 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.
Source：https://arxiv.org/abs/2505.15417PreviousLinear Bandits on Ellipsoids: Minimax Optimal AlgorithmsNextOptimal In-context Adaptivity and Distributional Robustness of Transformers
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