4 papers
Online Versatile Incremental Learning: Towards Class and Domain-Agnostic Adaptation at Any Time
Jae-Ho Lee, Min-Yeong Park, Jun-Yeong Moon +2
Continual learning enables vision systems to adapt to ever-changing data distributions. Despite significant advances, existing approaches fail to capture continuous and concurrent…
Open Your Model's Eyes: Video and Context-Aware Multimodal Backchannel Prediction
Min-Jae Kim, Jun-Yeong Moon, Mujeen Sung +1
Backchannels, which signal listener states like empathy and understanding, are fundamental to natural human interaction. However, current approaches rely solely on audio and text.…
Towards Model-Agnostic Dataset Condensation by Heterogeneous Models
Jun-Yeong Moon, Jung Uk Kim, Gyeong-Moon Park
Abstract. The advancement of deep learning has coincided with the proliferation of both models and available data. The surge in dataset sizes and the subsequent surge in computatio…
Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning
Jun-Yeong Moon, Keon-Hee Park, Jung Uk Kim +1
Continual learning aims to learn a model from a continuous stream of data, but it mainly assumes a fixed number of data and tasks with clear task boundaries. However, in real-world…