6 papers
VIRO: Robust and Efficient Neuro-Symbolic Reasoning with Verification for Referring Expression Comprehension
Hyejin Park, Junhyuk Kwon, Suha Kwak +1
Referring Expression Comprehension (REC) aims to localize the image region corresponding to a natural language query. Recent neuro-symbolic REC approaches leverage large language m…
Learned split-spectrum metalens for obstruction-free broadband imaging in the visible
Seungwoo Yoon, Dohyun Kang, Eunsue Choi +9
Obstructions such as raindrops, fences, or dust degrade captured images, especially when mechanical cleaning is infeasible. Conventional solutions to obstructions rely on a bulky c…
GroupCoOp: Group-robust Fine-tuning via Group Prompt Learning
Nayeong Kim, Seong Joon Oh, Suha Kwak
Parameter-efficient fine-tuning (PEFT) of vision-language models (VLMs) excels in various vision tasks thanks to the rich knowledge and generalization ability of VLMs. However, rec…
Enhancing Cost Efficiency in Active Learning with Candidate Set Query
Yeho Gwon, Sehyun Hwang, Hoyoung Kim +2
This paper introduces a cost-efficient active learning (AL) framework for classification, featuring a novel query design called candidate set query. Unlike traditional AL queries r…
GaRA-SAM: Robustifying Segment Anything Model with Gated-Rank Adaptation
Sohyun Lee, Yeho Gwon, Lukas Hoyer +1
Improving robustness of the Segment Anything Model (SAM) to input degradations is critical for its deployment in high-stakes applications such as autonomous driving and robotics. O…
TestDG: Test-time Domain Generalization for Continual Test-time Adaptation
Sohyun Lee, Nayeong Kim, Juwon Kang +2
This paper studies continual test-time adaptation (CTTA), the task of adapting a model to constantly changing unseen domains in testing while preserving previously learned knowledg…