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Batch-Conditioned Semantic Anchors for Robust Transductive Adaptation of Vision--Language Models
MR Sherif, Author2, Author3, Author4, Author5
transductive adaptationvision-language modelssemantic referencesbatch conditioning
2611.06993
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3h ago80%
Abstract
The paper presents JPTA, a framework that enhances transductive adaptation of vision-language models by refining predictions based on batch-conditioned semantic references.
Reality Card
Core Claim
JPTA outperforms existing methods like TransCLIP and StatA in adapting vision-language models, particularly in scenarios with sparse and prior-shifted batches.
Method / Result
JPTA consistently shows improved performance across standard benchmarks, especially in low effective-class regimes.
Limitations
The paper does not specify detailed reproducibility metrics or datasets used for validation.
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