4 papers
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…
Dual-Modality Anchor-Guided Filtering for Test-time Prompt Tuning
Jungwon Choi, Eunwoo Kim
Test-Time Prompt Tuning (TPT) adapts vision-language models using augmented views, but its effectiveness is hindered by the challenge of determining which views are beneficial. Sta…
Which Concepts to Forget and How to Refuse? Decomposing Concepts for Continual Unlearning in Large Vision-Language Models
Hyundong Jin, Dongyoon Han, Eunwoo Kim
Continual unlearning poses the challenge of enabling large vision-language models to selectively refuse specific image-instruction pairs in response to sequential deletion requests…
Self-Corrective Task Planning by Inverse Prompting with Large Language Models
Jiho Lee, Hayun Lee, Jonghyeon Kim +2
In robot task planning, large language models (LLMs) have shown significant promise in generating complex and long-horizon action sequences. However, it is observed that LLMs often…