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The Blending Ratio Is Not Where the Performance Is: Diagnosing Prototype Blending for Few-Shot Adaptation of Vision-Language Models
Liangzhi Li, Author 2, Author 3, Author 4, Author 5
few-shot learningvision-language modelsprototype blendingvalidation-free methods
2608.23634
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3h ago80%
Abstract
This paper investigates the effectiveness of blending ratios in few-shot adaptation of vision-language models, revealing that optimal ratios do not correlate with performance improvements.
Reality Card
Core Claim
The study demonstrates that the theoretically optimal blending ratio does not lead to the best performance, and validation-free methods can achieve better results.
Method / Result
Validation-free linear probes outperform the oracle-tuned blend by +1.9 points on average.
Limitations
The reliance on specific datasets and models may limit the generalizability of the findings.
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