12 citations · 22 across the 11 of their papers we have counts for
4 papers · 1 filter
Rethinking the Test-Time Prompt Tuning Objective from the Perspective of Calibration
Jungwon Choi, Hyeonseo Jang, Kibok Lee +1
Test-time prompt tuning (TPT) has emerged as a powerful paradigm, refining prompts for each test sample via entropy minimization (EM) over multiple augmented views. However, we ide…
Are Compact Rationales Free? Measuring Tile Selection Headroom in Frozen WSI-MIL
Hyun Do Jung, Jungwon Choi, Soojung Choi +2
Whole-slide image (WSI) multiple instance learning (MIL) classifiers can achieve strong slide-level AUC while leaving the full-bag prediction opaque. Attention scores are widely re…
Bridging the Missing-Modality Gap: Improving Text-Only Calibration of Vision Language Models
Mingyeong Kim, Jungwon Choi, Chaeyun Jang +1
Vision-language models (VLMs) are often deployed on text-only inputs, although they are trained with images. We find that removing the vision modality causes large drops in accurac…
ReaMIL: Reasoning- and Evidence-Aware Multiple Instance Learning for Whole-Slide Histopathology
Hyun Do Jung, Jungwon Choi, Hwiyoung Kim
We introduce ReaMIL (Reasoning- and Evidence-Aware MIL), a multiple instance learning approach for whole-slide histopathology that adds a light selection head to a strong MIL backb…