arXiv Computer Vision
Rethinking Gradient Modulation in Multimodal Regression
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MultiFair: Multimodal Balanced Fairness-Aware Medical Classification with Dual-Level Gradient Modulation
arXiv:2510. 07328v2 Announce Type: replace-cross Abstract: Medical decision systems increasingly rely on data from multiple sources to ensure reliable and unbiased diagnosis.
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Beyond Modality Fusion: Deep Ensembles for Multimodal Classification
arXiv:2607. 05019v1 Announce Type: new Abstract: In multimodal classification, late-fusion approaches classify concatenated modality-specific features extracted by unimodal neural networks.
arXiv Machine Learning
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MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning
arXiv:2607. 16789v1 Announce Type: new Abstract: Real-world perception and decision making are inherently multimodal, integrating complementary signals across modalities.
arXiv Machine Learning
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Mitigating Strong-Modality Collapse in Multimodal Learning via Inverted Asymmetric Fusion
The paper introduces Inverted Asymmetric Fusion (IAF) to address strong-modality collapse in multimodal learning, where dominant modalities are degraded during fusion. IAF preserves the dominant modality by passing it unchanged and letting weaker modalities attend to it, while also strengthening weaker modalities via Modality-Aware Knowledge Distillation. Experiments on MultiHuSE, UR-FUNNY, and MUStARD show that IAF maintains unimodal performance and improves over the best unimodal baseline by up to 8.25%.
arXiv AI
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MultiMem: Measuring and Mitigating Memorization in Multi-Modal Contrastive Learning
arXiv:2606. 22220v2 Announce Type: replace-cross Abstract: Memorization in machine learning models enables high performance on rare in-distribution samples by capturing their atypical patterns.
arXiv Machine Learning
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Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning
arXiv:2505. 19614v2 Announce Type: replace Abstract: Multimodal learning has seen remarkable progress, particularly with large-scale pre-training across various modalities.