2 citations · 3 across the 4 of their papers we have counts for
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cs.CV2026
Centering before Pruning: Lightweight Geometry Correction for Diversity-Based Visual Token Pruning in LVLMs
Shunjie Wen, Jaeyeon Lee, Dong-Wan Choi
Large vision-language models (LVLMs) incur substantial inference costs due to their long and highly redundant visual-token sequences. Diversity-based pruning mitigates this cost by…
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★ 2 cited
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…