3 papers
cs.CV2026
Setting-Matched and Semantics-Scaled Benchmarking of One-Step Generative Models Against Multistep Diffusion and Flow Models
Advaith Ravishankar, Serena Liu, Mingyang Wang +11
State-of-the-art text-to-image models produce high-quality images, but inference remains expensive as generation requires several sequential ODE or denoising steps. Native one-step…
cs.CL2026
Explain in Your Own Words: Improving Reasoning via Token-Selective Dual Knowledge Distillation
Minsang Kim, Seung Jun Baek
Knowledge Distillation (KD) can transfer the reasoning abilities of large models to smaller ones, which can reduce the costs to generate Chain-of-Thoughts for reasoning tasks. KD m…
cs.CV2025
DynASyn: Multi-Subject Personalization Enabling Dynamic Action Synthesis
Yongjin Choi, Chanhun Park, Seung Jun Baek
Recent advances in text-to-image diffusion models spurred research on personalization, i.e., a customized image synthesis, of subjects within reference images. Although existing pe…