1 citations · 2 across the 3 of their papers we have counts for
4 papers
CoSP: Reconfigurable Multi-State Metamaterial Inverse Design via Contrastive Pretrained Large Language Model
Shujie Yang, Xuzhe Zhao, Yuqi Zhang +2
Metamaterials, known for their ability to manipulate light at subwavelength scales, face significant design challenges due to their complex and sophisticated structures. Consequent…
Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large Scene Reconstruction
Jixuan Fan, Wanhua Li, Yifei Han +2
3D Gaussian Splatting has demonstrated notable success in large-scale scene reconstruction, but challenges persist due to high training memory consumption and storage overhead. Hyb…
Lightweight Diffusion Models with Distillation-Based Block Neural Architecture Search
Siao Tang, Xin Wang, Hong Chen +3
Diffusion models have recently shown remarkable generation ability, achieving state-of-the-art performance in many tasks. However, the high computational cost is still a troubling…
Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation Relaxing
Siao Tang, Xin Wang, Hong Chen +4
High computational overhead is a troublesome problem for diffusion models. Recent studies have leveraged post-training quantization (PTQ) to compress diffusion models. However, mos…