Abstract: Highlights•Innovatively introducing mixup strategy to multimodal representation learning.•Conducting multimodal mixup through adapting and exploring steps.•Mixing negative samples in multimodal contrastive learning.•Improving the robustness against missing modalities and the performance of multimodal representations.•Transferable to various multimodal learning scenarios.
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