CoCoA-Mix combines cross-entropy with a confidence penalty and a scalar-weighted mixture of specialized and generalized prompts, beating prior prompt-tuning baselines on base-to-new, cross-dataset, and few-shot incremental benchmarks.
In theFGVC Aircraftdataset (Base), CoA-loss focuses more precisely on fine-grained details such as text on airplane wings, outperforming zero-shot CLIP in specialization
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CoCoA-Mix: Confusion-and-Confidence-Aware Mixture Model for Context Optimization
CoCoA-Mix combines cross-entropy with a confidence penalty and a scalar-weighted mixture of specialized and generalized prompts, beating prior prompt-tuning baselines on base-to-new, cross-dataset, and few-shot incremental benchmarks.