3 papers
cs.CV2026
MDS-DETR: DETR with Masked Duplicate Suppressor
Chanho Lee, Seunghee Koh, Yunho Jeon +1
The DEtection TRansformer (DETR) is a powerful end-to-end object detector, yet its one-to-one matching strategy suffers from slow convergence and low recall. A common approach to a…
cs.CL2026
Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens
Seunghee Koh, Sunghyun Baek, Youngdong Kim +1
Unlearning in large language models (LLMs) has emerged as a promising safeguard against adversarial behaviors. When the forgetting loss is applied uniformly without considering tok…
cs.CV2026
IMSE: Intrinsic Mixture of Spectral Experts Fine-tuning for Test-Time Adaptation
Sunghyun Baek, Jaemyung Yu, Seunghee Koh +3
Test-time adaptation (TTA) has been widely explored to prevent performance degradation when test data differ from the training distribution. However, fully leveraging the rich repr…