5 citations · 7 across the 4 of their papers we have counts for
5 papers
Evaluating Visual Prompts with Eye-Tracking Data for MLLM-Based Human Activity Recognition
Jae Young Choi, Seon Gyeom Kim, Hyungjun Yoon +7
Large Language Models (LLMs) have emerged as foundation models for IoT applications such as human activity recognition (HAR). However, directly applying high-frequency and multi-di…
Beyond Hearing: Learning Task-Agnostic ExG Representations from Earphones via Physiology-Informed Tokenization
Hyungjun Yoon, Seungjoo Lee, Yu Yvonne Wu +11
Electrophysiological (ExG) signals offer valuable insights into human physiology, yet building foundation models that generalize across everyday tasks remains challenging due to tw…
Test-Time Adaptation with Binary Feedback
Taeckyung Lee, Sorn Chottananurak, Junsu Kim +3
Deep learning models perform poorly when domain shifts exist between training and test data. Test-time adaptation (TTA) is a paradigm to mitigate this issue by adapting pre-trained…
AETTA: Label-Free Accuracy Estimation for Test-Time Adaptation
Taeckyung Lee, Sorn Chottananurak, Taesik Gong +1
Test-time adaptation (TTA) has emerged as a viable solution to adapt pre-trained models to domain shifts using unlabeled test data. However, TTA faces challenges of adaptation fail…
SoTTA: Robust Test-Time Adaptation on Noisy Data Streams
Taesik Gong, Yewon Kim, Taeckyung Lee +2
Test-time adaptation (TTA) aims to address distributional shifts between training and testing data using only unlabeled test data streams for continual model adaptation. However, m…