33 citations · 33 across the 4 of their papers we have counts for
5 papers · 1 filter
Diffusion Autoencoders are Scalable Image Tokenizers
Yinbo Chen, Rohit Girdhar, Xiaolong Wang +2
Tokenizing images into compact visual representations is a key step in learning efficient and high-quality image generative models. We present a simple diffusion tokenizer (DiTo) t…
Consistent Flow Distillation for Text-to-3D Generation
Runjie Yan, Yinbo Chen, Xiaolong Wang
Score Distillation Sampling (SDS) has made significant strides in distilling image-generative models for 3D generation. However, its maximum-likelihood-seeking behavior often leads…
Image Neural Field Diffusion Models
Yinbo Chen, Oliver Wang, Richard Zhang +3
Diffusion models have shown an impressive ability to model complex data distributions, with several key advantages over GANs, such as stable training, better coverage of the traini…
Learning Continuous Image Representation with Local Implicit Image Function
Yinbo Chen, Sifei Liu, Xiaolong Wang
How to represent an image? While the visual world is presented in a continuous manner, machines store and see the images in a discrete way with 2D arrays of pixels. In this paper,…
Rethinking Knowledge Graph Propagation for Zero-Shot Learning
Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang +3
Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning. These models are highly sample efficient as related concepts in the grap…