5 papers
Multimodal Federated Learning under Dual-Axis Modality Missingness
Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin +5
Multimodal federated learning (FL) supports collaborative modeling in privacy-sensitive health-sensing and medical settings, but realistic deployments often exhibit dual-axis modal…
UniSAFE: A Comprehensive Benchmark for Safety Evaluation of Unified Multimodal Models
Segyu Lee, Boryeong Cho, Hojung Jung +8
Unified Multimodal Models (UMMs) offer powerful cross-modality capabilities but introduce new safety risks not observed in single-task models. Despite their emergence, existing saf…
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…
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…
Federated Learning for Time-Series Healthcare Sensing with Incomplete Modalities
Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin +1
Many healthcare sensing applications utilize multimodal time-series data from sensors embedded in mobile and wearable devices. Federated Learning (FL), with its privacy-preserving…