DeCoDe decomposes few-shot classification into binary pairwise image comparisons whose affirmative logits serve as similarity scores, enabling strong performance from unmodified MLLMs on twelve datasets.
In: ICCV (2019)
2 Pith papers cite this work. Polarity classification is still indexing.
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Discarding visual guidance from vision-language models and using language embeddings as the primary source of domain invariance via an information bottleneck yields state-of-the-art domain generalization performance.
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Decompose, Compare, and Decide: Multimodal LLMs are Implicit Few-Shot Learners
DeCoDe decomposes few-shot classification into binary pairwise image comparisons whose affirmative logits serve as similarity scores, enabling strong performance from unmodified MLLMs on twelve datasets.
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Domain Generalization via Text-Anchored Information Bottleneck
Discarding visual guidance from vision-language models and using language embeddings as the primary source of domain invariance via an information bottleneck yields state-of-the-art domain generalization performance.