Empirical Bayes conformal prediction converts score variability into r-value nonconformity scores that preserve target coverage while reducing inclusion of high-variance false candidates in image classification, CLIP VLMs, and LLMs.
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CoViP is a unified framework for contextualized visual personalization in VLMs that treats personalized image captioning as the core task, applies RL-based post-training and caption-augmented generation, and shows gains on diagnostic evaluations that rule out textual shortcuts plus downstream tasks.
citing papers explorer
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Empirical Bayes Conformal Prediction for Vision and Language Models
Empirical Bayes conformal prediction converts score variability into r-value nonconformity scores that preserve target coverage while reducing inclusion of high-variance false candidates in image classification, CLIP VLMs, and LLMs.
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Contextualized Visual Personalization in Vision-Language Models
CoViP is a unified framework for contextualized visual personalization in VLMs that treats personalized image captioning as the core task, applies RL-based post-training and caption-augmented generation, and shows gains on diagnostic evaluations that rule out textual shortcuts plus downstream tasks.