Learning What to Trust in Multimodal Learning under Noisy Supervision

arXiv:2610.11057 · cs.CV, cs.LG · Submitted 2026-10-08 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Alignment — 41× in this paper · explained in 1 episode
• Accuracy — 29× in this paper · explained in 1 episode
• Multimodal Learning — 18× in this paper · explained in 2 episodes
• Regularization — 11× in this paper · explained in 3 episodes
• Alignment Scores — 10× in this paper · explained in 1 episode
• Consistency — 10× in this paper · explained in 3 episodes
• Semi-supervised Learning — 9× in this paper · explained in 4 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Representation Learning — 8× in this paper · explained in 2 episodes
• Multimodal Dataset — 6× in this paper · explained in 2 episodes
• Pseudo-labels — 5× in this paper · explained in 2 episodes
• Loss Function — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• F1 Score — 3× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Asymmetric Mean Square Error — 1× in this paper
• Calibration — 1× in this paper · explained in 7 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• DIST with Confidence Tracking — 1× in this paper
• Data Pruning — 1× in this paper · explained in 2 episodes
• Federated Learning — 1× in this paper · explained in 15 episodes
• Gaussian Mixture Model (GMM) — 1× in this paper · explained in 2 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
• A Survey of Label-noise Representation Learning: Past, Present and Future
• MOLAR: Learning Multimodal Molecular Representations from Noisy Labels
• DINOv2: Learning Robust Visual Features without Supervision
• Decoupled Weight Decay Regularization
• FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

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