metadata
datasets:
- ILSVRC/imagenet-1k
- ljnlonoljpiljm/places365-256px
language:
- en
- zh
license: mit
pipeline_tag: class-conditional-image-generation
library_name: pytorch
This is an official model card of the paper Equivariant Image Modeling.
In this paper, we propose a novel equivariant image modeling framework that inherently aligns optimization targets across subtasks in autoregressive image modeling by leveraging the translation invariance of natural visual signals. Our method introduces:
- Column-wise tokenization which enhances translational symmetry along the horizontal axis.
- Autoregressive generative models using windowed causal attention which enforces consistent contextual relationships across positions.
Evaluated on class-conditioned ImageNet generation at 256×256 resolution, our approach achieves performance comparable to state-of-the-art AR models while using fewer computational resources. Moreover, our approach significantly improving zero-shot generalization and enabling ultra-long image synthesis.
Bibtex
@misc{dong2025equivariantimagemodeling,
title={Equivariant Image Modeling},
author={Ruixiao Dong and Mengde Xu and Zigang Geng and Li Li and Han Hu and Shuyang Gu},
year={2025},
eprint={2503.18948},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2503.18948},
}