Pre-Trained Image Processing Transformer
Open accessAbstract
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
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ViTs have become the new backbone for a diverse range of vision tasks, including image classification, object detection, image generation, autonomous driving, and visual question answering.
“subsequently establishing them as the new backbone for a diverse range of tasks, including image classification [7], object detection [8], image generation [9], autonomous driving [10], and visual question answering [11]”
From Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey (2024) · cites this paper as [9]
A large amount of image-language pretraining Transformer models are pretrained on large-scale image-text pairs.
“For instance, large amount of imagelanguage pretraining Transformer models are pretrained on large-scale image-text pairs, e.g., VisualBERT [104], VL-BERT [105], ViLBERT [102], LXMERT [103], UNITER [106].”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [102]
The two streams of cross-attention can be further concatenated and processed by another Transformer to model the global context.
“The two streams of cross-attention [102] can be further concatenated and processed by another Transformer to model the global context.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [102]
Transformer-based models pretrained on large-scale multimodal corpora clearly outperform other competitors in a wide range of multimodal downstream tasks and achieve zero-shot generalization ability.
“Recent work has demonstrated that if pretrained on large scale multimodal corpora Transformer based models [7], [102], [103], [104], [105], [106], [110] clearly outperform other competitors in a wide range of multimodal down-stream tasks, and moreover achieve the zero-shot generalization ability.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [102]
Cross-attention (coattention) was first proposed in VilBERT.
“This method is termed cross-attention or coattention [190], which was first proposed in VilBERT [102]”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [102]
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Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
Cited by
Sorted by citations
Adding Conditional Control to Text-to-Image Diffusion Models
Zhang, L., Rao, A., & Agrawala, M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00355 · Cited by 4,547 · see citing works
Attention mechanisms in computer vision: A survey
Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0271-y · Cited by 2,566 · see citing works
“[8], [9], [15], [21], [31], [32], [34], [35] , [22], [26], [62], [63], [64], [65], [66], [67] , [41], [68], [69], [70], [71], [72], [73], [74] , [8], [34], [42], [43], [75], [76], [77], [78] , [27], [44], [45], [46], [79], [80], [81], [82] , [61], [83], [84], [85], [86], [87], [88], [89] , [47], [90], [91], [92], [93]…”
Self-citationTokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z., Tay, F. E. H., Feng, J., & Yan, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00060 · Cited by 2,415 · see citing works
Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., & Wang, Y. (2021) · arXiv · doi:10.48550/arxiv.2103.00112 · Cited by 1,394 · see citing works
“, denosing and super-resolution) as a series of translations and develop the IPT model for handling multiple low-level computer vision problems [4].”
Self-citationTransReID: Transformer-based Object Re-Identification
He, S., Luo, H., Wang, P., Wang, F., Li, H., & Jiang, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01474 · Cited by 1,244 · see citing works
Multimodal learning with transformers: A survey
Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275156 · Cited by 1,072 · see citing works
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021) · arXiv · doi:10.48550/arxiv.2105.15203 · Cited by 5,090 · see citing works
Conditional Positional Encodings for Vision Transformers
Chu, X., Tian, Z., Zhang, B., Wang, X., & Shen, C. (2021) · arXiv · doi:10.48550/arxiv.2102.10882 · Cited by 489 · see citing works
No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
Golestaneh, S. A., Dadsetan, S., & Kitani, K. M. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00404 · Cited by 465 · see citing works
DiffIR: Efficient Diffusion Model for Image Restoration
Xia, B., Zhang, Y., Wang, S., Wang, Y., Wu, X., Tian, Y., Yang, W., & Van Gool, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01204 · Cited by 412 · see citing works
Self-citationPatch Slimming for Efficient Vision Transformers
Tang, Y., Han, K., Wang, Y., Xu, C., Guo, J., Xu, C., & Tao, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01185 · Cited by 201 · see citing works
Self-citationPost-Training Quantization for Vision Transformer
Liu, Z., Wang, Y., Han, K., Ma, S., & Gao, W. (2021) · arXiv · doi:10.48550/arxiv.2106.14156 · Cited by 192 · see citing works
Self-citationSPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning
Kong, Z., Dong, P., Ma, X., Meng, X., Niu, W., Sun, M., Shen, X., Yuan, G., Ren, B., Tang, H., et al. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20083-0_37 · Cited by 169 · see citing works
Self-citationWang, T., Zhang, K., Zhang, Y., Luo, W., Stenger, B., Lu, T., Kim, T.-K., & Liu, W. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111628 · Cited by 150 · see citing works
I-ViT: Integer-only Quantization for Efficient Vision Transformer Inference
Li, Z., & Gu, Q. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01565 · Cited by 141 · see citing works
“Furthermore, ViTs have also been applied to more complexed vision applications, such as object detection [2, 27] and semantic segmentation [3].”
MethodSegmenting Transparent Objects in the Wild with Transformer
Xie, E., Wang, W., Wang, W., Sun, P., Xu, H., Liang, D., & Luo, P. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/165 · Cited by 111 · see citing works
Delving Deep into the Generalization of Vision Transformers under Distribution Shifts
Zhang, C., Zhang, M., Zhang, S., Jin, D., Zhou, Q., Cai, Z., Zhao, H., Liu, X., & Liu, Z. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00713 · Cited by 107 · see citing works
Self-citationObject Detection with Transformers: A Review
Shehzadi, T., Hashmi, K. A., Liwicki, M., Stricker, D., & Afzal, M. Z. (2025) · Sensors · doi:10.3390/s25196025 · Cited by 93 · see citing works
PSAQ-ViT V2: Toward Accurate and General Data-Free Quantization for Vision Transformers
Li, Z., Chen, M., Xiao, J., & Gu, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3301007 · Cited by 41 · see citing works
Dong, P., Zhuang, J., Yang, Z., Ji, S., Li, Y., Xu, D., Huang, H., Hu, J., Jones, A. K., Shi, Y., et al. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3443692 · Cited by 35 · see citing works
“…(ViTs) [1], [2], [3] have shown remarkable versatility in a broad range of application domains, including computer vision (e.g., image classification [1], [3], object detection [4], [5], image processing [6], and video understanding [7]), and in complex scenarios that involve the multimodal data.”
Self-citationCAAT: Image super-resolution algorithm via channel attention and transformer
Chen, Y., Chen, L., Xia, R., Yang, K., & Zou, K. (2025) · Array · doi:10.1016/j.array.2025.100628 · Cited by 29 · see citing works
Std-net: spatio-temporal decomposition network for video demoiréing with sparse transformers
Niu, Y., Xu, R., Lin, Z., & Liu, W. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3386604 · Cited by 12 · see citing works
Wang, P., Zheng, W., Chen, T., & Wang, Z. (2022) · arXiv · doi:10.48550/arxiv.2203.05962 · Cited by 80 · see citing works
“…ViT to various vision tasks, such as object detection (Carion et al., 2020; Zhu et al., 2021; Zheng et al., 2021a; Sun et al., 2020), segmentation (Chen et al., 2021a; Wang et al., 2021), image generation (Parmar et al., 2018; Jiang et al., 2021a), video processing (Zhou et al., 2018; Arnab et…”
BackgroundMethodModel Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey
Du, D., Gong, G., & Chu, X. (2024) · arXiv · doi:10.48550/arxiv.2405.00314 · Cited by 4 · see citing works
“…establishing them as the new backbone for a diverse range of tasks, including image classification [7], object detection [8], image generation [9], autonomous driving [10], and visual question answering [11], showcasing their versatility and transformative impact in computer vision, as…”
Background
- Authors
- Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., & Gao, W.
- Affiliations
- Peking University (CN)
- Huawei Technologies (China) (CN)
- The University of Sydney (AU)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 12294-12305
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3171125843
- Semantic Scholar
- 6f6f73e69ee0d9d5d7d088bb882db1851d98175a
- Cited by
- 2,308
- References
- 87
- Citations by year
- 2026: 219 · 2025: 520 · 2024: 558 · 2023: 553 · 2022: 332 · 2021: 125
- FWCI
- 128.21
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- 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)
- Funders
- National Natural Science Foundation of China
- Australian Research Council
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- pre-trained models, image denoising, super-resolution, deraining, ImageNet, contrastive learning, fine-tuning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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