mlsanityData to 14 Oct 2026

Pre-Trained Image Processing Transformer

arXiv 2012.00364 ↗First posted 1 Dec 2020cs.CV cs.LGcited by 893

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Cited by 60 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

As the computing power of modern hardware is increasing strongly, pre-trained deep learning models (e.g., BERT, GPT-3) learned on large-scale datasets have shown their effectiveness over conventional methods. The big progress is mainly contributed to the representation ability of transformer and its variant architectures. In this paper, we study the low-level computer vision task (e.g., denoising, super-resolution and deraining) and develop a new pre-trained model, namely, image processing transformer (IPT). To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs. The IPT model is trained on these images with multi-heads and multi-tails. In addition, the contrastive learning is introduced for well adapting to different image processing tasks. The pre-trained model can therefore efficiently employed on desired task after fine-tuning. With only one pre-trained model, IPT outperforms the current state-of-the-art methods on various low-level benchmarks. Code is available at https://github.com/huawei-noah/Pretrained-IPT and https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/IPT

Key facts. Cited by 893 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Dec 2020.

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Citing papers per year: 2021: 136; 2022: 199; 2023: 208; 2024: 184; 2025: 108; 2026: 58.

893 papers.

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  3. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
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  9. 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 866
  10. 2022AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognitioncited by 719
  11. 2022Simple Baselines for Image Restorationcited by 708
  12. 2021Transformer in Transformercited by 633
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  14. 2021Remote Sensing Image Change Detection with Transformerscited by 306
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  16. 2021DeepViT: Towards Deeper Vision Transformercited by 289
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  22. 2023DiffIR: Efficient Diffusion Model for Image Restorationcited by 255
  23. 2021Incorporating Convolution Designs into Visual Transformerscited by 248
  24. 2024One-Step Effective Diffusion Network for Real-World Image Super-Resolutioncited by 246
  25. 2022Transformers in Medical Imaging: A Surveycited by 245
  26. 2021TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditionscited by 244
  27. 2021Attention Mechanisms in Computer Vision: A Surveycited by 232
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  29. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  30. 2022CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusioncited by 223
  31. 2022VRT: A Video Restoration Transformercited by 220
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  33. 2021ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Biascited by 216
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  35. 2022UniFormer: Unifying Convolution and Self-attention for Visual Recognitioncited by 206
  36. 2023Learning A Sparse Transformer Network for Effective Image Derainingcited by 205
  37. 2021StyTr2: Image Style Transfer with Transformerscited by 187
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