1 citations · 1 across the 3 of their papers we have counts for
5 papers
Test-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration
Hyeongheon Cha, Young D. Kwon, Sung-Ju Lee
Post-training quantization is a standard route to fitting vision transformers (ViTs) into edge compute and memory budgets, yet quantized models become especially brittle under dist…
Later Is Better: Token Reduction for ViTs Under Distribution Shift
Hyeongheon Cha, Hyungjun Yoon, Sung-Ju Lee
Training-free token reduction accelerates vision transformers by removing redundant tokens across layers, recovering most of the original accuracy at a fraction of the compute. The…
SNAP: Low-Latency Test-Time Adaptation with Sparse Updates
Hyeongheon Cha, Dong Min Kim, Hye Won Chung +2
Test-Time Adaptation (TTA) adjusts models using unlabeled test data to handle dynamic distribution shifts. However, existing methods rely on frequent adaptation and high computatio…
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…
IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications
Hyungjun Yoon, Hyeongheon Cha, Hoang C. Nguyen +2
Pre-training representations acquired via self-supervised learning could achieve high accuracy on even tasks with small training data. Unlike in vision and natural language process…