3 papers
cs.LG2025
Dynamic Rank Adjustment for Accurate and Efficient Neural Network Training
Hyuntak Shin, Aecheon Jung, Sungeun Hong +1
Low-rank training methods reduce the number of trainable parameters by re-parameterizing the weights with matrix decompositions (e.g., singular value decomposition). However, enfor…
cs.LG2025
Task Vector Quantization for Memory-Efficient Model Merging
Youngeun Kim, Seunghwan Lee, Aecheon Jung +2
Model merging enables efficient multi-task models by combining task-specific fine-tuned checkpoints. However, storing multiple task-specific checkpoints requires significant memory…
cs.CV2025
IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks
Aecheon Jung, Soyun Choi, Junhong Min +1
Image segmentation is a vital task for providing human assistance and enhancing autonomy in our daily lives. In particular, RGB-D segmentation-leveraging both visual and depth cues…