7 citations · 7 across the 11 of their papers we have counts for
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cs.CV2026
ORBIS: Output-Guided Token Reduction with Distribution-Aware Matching for Video Diffusion Acceleration
Hangyeol Lee, Joo-Young Kim
Diffusion Transformer (DiT) has emerged as a powerful model architecture for generating high-quality images and videos. In the case of video DiT, 3D Spatio-Temporal Attention incre…
cs.CV2026
Rethinking Token Reduction for Diffusion Models via Output-Similarity-Awareness
Hangyeol Lee, Hyojeong Lee, Joo-Young Kim
Diffusion Transformers (DiTs) achieve superior image generation quality but suffer from quadratic computational complexity relative to token count. While various token reduction (T…
cs.CV2025★ 7 cited
AoP-SAM: Automation of Prompts for Efficient Segmentation
Yi Chen, Mu-Young Son, Chuanbo Hua +1
The Segment Anything Model (SAM) is a powerful foundation model for image segmentation, showing robust zero-shot generalization through prompt engineering. However, relying on manu…