논문해외
SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time
HF Daily Papers
We introduce SIMIT, a test-time self-improvement framework in which a vision-language model creates its own query-specific training data before answering an unlabeled test query.
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