4 papers · 1 filter
ZOO-Prune: Training-Free Token Pruning via Zeroth-Order Gradient Estimation in Vision-Language Models
Youngeun Kim, Youjia Zhang, Huiling Liu +3
Large Vision-Language Models (VLMs) enable strong multimodal reasoning but incur heavy inference costs from redundant visual tokens. Token pruning alleviates this issue, yet existi…
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…
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…
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…