Von Mises-Fisher Mixture Model with Dynamic Shrinkage for Realistic Test-Time Transduction
A range of methods aim to enhance the performance of vision-language models (VLMs) at test time. Among them, transduction has emerged as a promising paradigm due to its strong compatibility and efficiency. However, realistic evaluations often involve highly imbalanced class distributions, which cause performance degradation or even collapse. In this work, we systematically revisit transduction from the perspective of penalized likelihood estimation (PLE), showing that PLE with a KL-divergence anchor term naturally yields an adaptive shrinkage behavior between prior anchors and empirical estimates. From this viewpoint, the brittleness of transductive methods can be attributed to the absenc...
| Authors | Jiazhen Huang, Zhiming Liu, Changhu Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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