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cs.LG2025
An Analysis of Concept Bottleneck Models: Measuring, Understanding, and Mitigating the Impact of Noisy Annotations
Seonghwan Park, Jueun Mun, Donghyun Oh +1
Concept bottleneck models (CBMs) ensure interpretability by decomposing predictions into human interpretable concepts. Yet the annotations used for training CBMs that enable this t…
cs.CV2025
ZIP: An Efficient Zeroth-order Prompt Tuning for Black-box Vision-Language Models
Seonghwan Park, Jaehyeon Jeong, Yongjun Kim +2
Recent studies have introduced various approaches for prompt-tuning black-box vision-language models, referred to as black-box prompt-tuning (BBPT). While BBPT has demonstrated con…