4 papers · 1 filter
Can Synthetic Images Conquer Forgetting? Beyond Unexplored Doubts in Few-Shot Class-Incremental Learning
Junsu Kim, Yunhoe Ku, Seungryul Baek
Few-shot class-incremental learning (FSCIL) is challenging due to extremely limited training data; while aiming to reduce catastrophic forgetting and learn new information. We prop…
Beyond Synthetic Replays: Turning Diffusion Features into Few-Shot Class-Incremental Learning Knowledge
Junsu Kim, Yunhoe Ku, Dongyoon Han +1
Few-shot class-incremental learning (FSCIL) is challenging due to extremely limited training data while requiring models to acquire new knowledge without catastrophic forgetting. R…
VLM-PL: Advanced Pseudo Labeling Approach for Class Incremental Object Detection via Vision-Language Model
Junsu Kim, Yunhoe Ku, Jihyeon Kim +2
In the field of Class Incremental Object Detection (CIOD), creating models that can continuously learn like humans is a major challenge. Pseudo-labeling methods, although initially…
Dynamic Appearance Modeling of Clothed 3D Human Avatars using a Single Camera
Hansol Lee, Junuk Cha, Yunhoe Ku +2
The appearance of a human in clothing is driven not only by the pose but also by its temporal context, i.e., motion. However, such context has been largely neglected by existing mo…