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cs.CV2025
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…
cs.CV2025
Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment
Youjia Zhang, Youngeun Kim, Young-Geun Choi +3
Test-time adaptation (TTA) enhances the zero-shot robustness under distribution shifts by leveraging unlabeled test data during inference. Despite notable advances, several challen…
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…