The authors analyzed the confidence gap between modalities in multimodal classification.
Asymmetric certainty gains from optimization hinder multimodal classification
The authors show that optimization produces overconfidence in strong modalities and underconfidence in weak ones, and propose MaxCR to calibrate confidence.
Chinese Tech
Longfei Huang · Xiangyu Wu · Yang Yang
Nanjing University of Science and Technology · Alibaba Group
Research Digest··2 min read
The paper identifies a flaw in multimodal learning: optimization yields asymmetric gains in predictive certainty between strong and weak modalities, driving imbalanced contributions.
Why this paper
From Alibaba Group and Nanjing University of Science and Technology
In one line
Optimization yields asymmetric certainty gains in multimodal learning, with the strong modality more confident than the weak one, harming performance.
What we could check
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