3 papers
cs.CV2026
Moving Beyond Diversity: Visual Token Pruning as Subspace Reconstruction for Efficient VLMs
Jaeyeon Lee, Shunjie Wen, Dong-Wan Choi
Despite their remarkable performance, Vision Language Models (VLMs) incur substantial computational overhead due to the large number of visual tokens. While diversity maximization…
cs.CV2025
Lossless Token Merging Even Without Fine-Tuning in Vision Transformers
Jaeyeon Lee, Dong-Wan Choi
Although Vision Transformers (ViTs) have become the standard architecture in computer vision, their massive sizes lead to significant computational overhead. Token compression tech…
cs.CL2025
PGB: One-Shot Pruning for BERT via Weight Grouping and Permutation
Hyemin Lim, Jaeyeon Lee, Dong-Wan Choi
Large pretrained language models such as BERT suffer from slow inference and high memory usage, due to their huge size. Recent approaches to compressing BERT rely on iterative prun…