A dynamic multimodal network whose per-sample depth and parameters are set by prototype-based quality estimates, reporting large gains over prior trustworthy classifiers.
An empirical study of training end-to-end vision-and-language transformers,
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Multi-QuAD: Multi-Level Quality-Adaptive Dynamic Network for Reliable Multimodal Classification
A dynamic multimodal network whose per-sample depth and parameters are set by prototype-based quality estimates, reporting large gains over prior trustworthy classifiers.