UmiSonoda16/EquivariantModeling

  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}, 
}