1 citations · 2 across the 8 of their papers we have counts for
8 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…
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…
QuRe: Query-Relevant Retrieval through Hard Negative Sampling in Composed Image Retrieval
Jaehyun Kwak, Ramahdani Muhammad Izaaz Inhar, Se-Young Yun +1
Composed Image Retrieval (CIR) retrieves relevant images based on a reference image and accompanying text describing desired modifications. However, existing CIR methods only focus…
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…
By My Eyes: Grounding Multimodal Large Language Models with Sensor Data via Visual Prompting
Hyungjun Yoon, Biniyam Aschalew Tolera, Taesik Gong +2
Large language models (LLMs) have demonstrated exceptional abilities across various domains. However, utilizing LLMs for ubiquitous sensing applications remains challenging as exis…
SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay
Hyungjun Yoon, Jaehyun Kwak, Biniyam Aschalew Tolera +5
Self-supervised learning has emerged as a method for utilizing massive unlabeled data for pre-training models, providing an effective feature extractor for various mobile sensing a…
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…