3 papers
cs.AI2026
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…
cs.CV2026
Improving Calibration in Test-Time Prompt Tuning for Vision-Language Models via Data-Free Flatness-Aware Prompt Pretraining
Hyeonseo Jang, Jaebyeong Jeon, Joong-Won Hwang +1
Test-time prompt tuning (TPT) has emerged as a promising technique for enhancing the adaptability of vision-language models by optimizing textual prompts using unlabeled test data.…
cs.LG2025
Soft Task-Aware Routing of Experts for Equivariant Representation Learning
Jaebyeong Jeon, Hyeonseo Jang, Jy-yong Sohn +1
Equivariant representation learning aims to capture variations induced by input transformations in the representation space, whereas invariant representation learning encodes seman…