2 citations · 6 across the 7 of their papers we have counts for
7 papers
Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection
Lichen Bai, Shitong Shao, Zikai Zhou +4
Diffusion models, the most popular generative paradigm so far, can inject conditional information into the generation path to guide the latent towards desired directions. However,…
Pre-trained Molecular Language Models with Random Functional Group Masking
Tianhao Peng, Yuchen Li, Xuhong Li +7
Recent advancements in computational chemistry have leveraged the power of trans-former-based language models, such as MoLFormer, pre-trained using a vast amount of simplified mole…
Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization
Zipeng Qi, Lichen Bai, Haoyi Xiong +1
Diffusion models that can generate high-quality data from randomly sampled Gaussian noises have become the mainstream generative method in both academia and industry. Are randomly…
SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior
Zhongrui Yu, Haoran Wang, Jinze Yang +6
Novel View Synthesis (NVS) for street scenes play a critical role in the autonomous driving simulation. The current mainstream technique to achieve it is neural rendering, such as…
Neural Field Classifiers via Target Encoding and Classification Loss
Xindi Yang, Zeke Xie, Xiong Zhou +6
Neural field methods have seen great progress in various long-standing tasks in computer vision and computer graphics, including novel view synthesis and geometry reconstruction. A…
HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models
Hanzhang Wang, Haoran Wang, Jinze Yang +7
The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference into a given image/video. Existing methods usually focus on pursuing the balance…