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MaskGIT: Masked Generative Image Transformer
Open accessAbstract
Generative transformers have experienced rapid popularity growth in the computer vision community in synthesizing high-fidelity and high-resolution images. The best generative transformer models so far, however, still treat an image naively as a sequence of tokens, and decode an image sequentially following the raster scan ordering (i.e. line-by-line). We find this strategy neither optimal nor efficient. This paper proposes a novel image synthesis paradigm using a bidirectional transformer decoder, which we term MaskGIT. During training, MaskGIT learns to predict randomly masked tokens by attending to tokens in all directions. At inference time, the model begins with generating all tokens of an image simultaneously, and then refines the image iteratively conditioned on the previous generation. Our experiments demonstrate that MaskGIT significantly outperforms the state-of-the-art transformer model on the ImageNet dataset, and accelerates autoregressive decoding by up to 48x. Besides, we illustrate that MaskGIT can be easily extended to various image editing tasks, such as inpainting, extrapolation, and image manipulation. Project page: masked-generative-image-transformer.github.io.
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“Thus, discrete tokenizers are deployed in various visual understanding (Sun et al., 2024; Team, 2024; Wang et al., 2024; Wu et al., 2024) as well as image (Chang et al., 2022; Esser et al., 2021; Ramesh et al., 2021; Sun et al., 2024; Yu et al., 2022) and video generation tasks (Ge et al., 2022; Hong et al., 2023; Kondratyuk et al., 2024; Luo et al., 2024; Villegas et al., 2023; Wu et al., 2022;”
From Cosmos World Foundation Model Platform for Physical AI (2025) · cites this paper as Chang et al., 2022
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“Recent advances in deep learning have facilitated significant progress for various data modalities [29,161,205], such as images [38,239], audio [110,170], and text [14,20,75,159,162].”
From Diffusion Models: A Comprehensive Survey of Methods and Applications (2024) · cites this paper as [29]
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Metadata
- Authors
- Chang, H., Zhang, H., Jiang, L., Liu, C., & Freeman, W. T.
- Affiliations
- Google (United States) (US)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 11305-11315
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4313021454
- Semantic Scholar
- 7c597874535c1537d7ddff3b3723015b4dc79d30
- Cited by
- 446
- References
- 52
- Citations by year
- 2026: 112 · 2025: 132 · 2024: 121 · 2023: 72 · 2022: 9
- FWCI
- 30.28
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- image generation, non-autoregressive decoding, image inpainting, image extrapolation, image editing, token masking
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