mlsanityData to 7 Oct 2026

Dual Aggregation Transformer for Image Super-Resolution

arXiv 2308.03364 ↗First posted 7 Aug 2023cs.CVcited by 124

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Cited by 20 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Transformer has recently gained considerable popularity in low-level vision tasks, including image super-resolution (SR). These networks utilize self-attention along different dimensions, spatial or channel, and achieve impressive performance. This inspires us to combine the two dimensions in Transformer for a more powerful representation capability. Based on the above idea, we propose a novel Transformer model, Dual Aggregation Transformer (DAT), for image SR. Our DAT aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner. Specifically, we alternately apply spatial and channel self-attention in consecutive Transformer blocks. The alternate strategy enables DAT to capture the global context and realize inter-block feature aggregation. Furthermore, we propose the adaptive interaction module (AIM) and the spatial-gate feed-forward network (SGFN) to achieve intra-block feature aggregation. AIM complements two self-attention mechanisms from corresponding dimensions. Meanwhile, SGFN introduces additional non-linear spatial information in the feed-forward network. Extensive experiments show that our DAT surpasses current methods. Code and models are obtainable at https://github.com/zhengchen1999/DAT.

Key facts. Cited by 124 papers in the map (data through 7 Oct 2026). Builds on 49 of its 66 references that are in the map. First posted on arXiv 7 Aug 2023.

Builds on

49 of 66 references.

References · 49

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
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  10. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
  11. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
  12. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,618
37 more
  1. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
  2. 2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,441
  3. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
  4. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
  5. 2018Dual Attention Network for Scene Segmentationcited by 1,001
  6. 2020Pre-Trained Image Processing Transformercited by 891
  7. 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 864
  8. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  9. 2020Image Quality Assessment: Unifying Structure and Texture Similaritycited by 792
  10. 2022Simple Baselines for Image Restorationcited by 701
  11. 2017The Perception-Distortion Tradeoffcited by 582
  12. 2022MaxViT: Multi-Axis Vision Transformercited by 476
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  14. 2021XCiT: Cross-Covariance Image Transformerscited by 335
  15. 2015Sketch-based Manga Retrieval using Manga109 Datasetcited by 326
  16. 2022MAXIM: Multi-Axis MLP for Image Processingcited by 301
  17. 2020Single Image Super-Resolution via a Holistic Attention Networkcited by 265
  18. 2019Residual Non-local Attention Networks for Image Restorationcited by 236
  19. 2017Learning a Single Convolutional Super-Resolution Network for Multiple Degradationscited by 227
  20. 2018Deep Back-Projection Networks For Super-Resolutioncited by 224
  21. 2016SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioningcited by 219
  22. 2022DaViT: Dual Attention Vision Transformerscited by 174
  23. 2021Conformer: Local Features Coupling Global Representations for Visual Recognitioncited by 164
  24. 2018Non-Local Recurrent Network for Image Restorationcited by 160
  25. 2019Feedback Network for Image Super-Resolutioncited by 155
  26. 2022Efficient Long-Range Attention Network for Image Super-resolutioncited by 151
  27. 2022Understanding The Robustness in Vision Transformerscited by 144
  28. 2021CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attentioncited by 139
  29. 2020Interpreting Super-Resolution Networks with Local Attribution Mapscited by 136
  30. 2020Cross-Scale Internal Graph Neural Network for Image Super-Resolutioncited by 94
  31. 2022Cross Aggregation Transformer for Image Restorationcited by 92
  32. 2021On the Connection between Local Attention and Dynamic Depth-wise Convolutioncited by 61
  33. 2022MixFormer: Mixing Features across Windows and Dimensionscited by 43
  34. 2021Dual-stream Network for Visual Recognitioncited by 33
  35. 2023Recursive Generalization Transformer for Image Super-Resolutioncited by 28
  36. 2021Attention in Attention Network for Image Super-Resolutioncited by 20
  37. 2023CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale Attentioncited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 11; 2024: 50; 2025: 44; 2026: 19.

124 papers.

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  1. 2023SeeSR: Towards Semantics-Aware Real-World Image Super-Resolutioncited by 311
  2. 2024One-Step Effective Diffusion Network for Real-World Image Super-Resolutioncited by 243
  3. 2024MambaIRv2: Attentive State Space Restorationcited by 90
  4. 2023A Comparative Study of Image Restoration Networks for General Backbone Network Designcited by 56
  5. 2025DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolutioncited by 50
  6. 2024DRCT: Saving Image Super-resolution away from Information Bottleneckcited by 40
  7. 2024NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Resultscited by 40
  8. 2025One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillationcited by 40
  9. 2024A Survey of Camouflaged Object Detection and Beyondcited by 37
  10. 2025The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimizationcited by 31
  11. 2025AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineagecited by 29
  12. 2024QMambaBSR: Burst Image Super-Resolution with Query State Space Modelcited by 27
38 more
  1. 2024See More Details: Efficient Image Super-Resolution by Experts Miningcited by 23
  2. 2023Image Super-Resolution with Text Prompt Diffusioncited by 22
  3. 2025DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-Resolutioncited by 21
  4. 2025Fine-structure Preserved Real-world Image Super-resolution via Transfer VAE Trainingcited by 21
  5. 2024Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learningcited by 19
  6. 2024Binarized Diffusion Model for Image Super-Resolutioncited by 18
  7. 2024Unleashing the Power of One-Step Diffusion based Image Super-Resolution via a Large-Scale Diffusion Discriminatorcited by 18
  8. 2025CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolutioncited by 15
  9. 2023TULIP: Transformer for Upsampling of LiDAR Point Cloudscited by 12
  10. 20242DQuant: Low-bit Post-Training Quantization for Image Super-Resolutioncited by 12
  11. 2025NTIRE 2025 Challenge on Image Super-Resolution (x4): Methods and Resultscited by 12
  12. 2024Efficient Degradation-aware Any Image Restorationcited by 11
  13. 2024Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolutioncited by 10
  14. 2024Strengthening Layer Interaction via Dynamic Layer Attentioncited by 10
  15. 2026The Fourth Challenge on Image Super-Resolution (×4) at NTIRE 2026: Benchmark Results and Method Overviewcited by 10
  16. 2024CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolutioncited by 8
  17. 2024PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolutioncited by 8
  18. 2025Acquire and then Adapt: Squeezing out Text-to-Image Model for Image Restorationcited by 8
  19. 2024Partial Large Kernel CNNs for Efficient Super-Resolutioncited by 7
  20. 2024Swin2-MoSE: A New Single Image Super-Resolution Model for Remote Sensingcited by 7
  21. 2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
  22. 2023Learning Many-to-Many Mapping for Unpaired Real-World Image Super-resolution and Downscalingcited by 6
  23. 2024A Preliminary Exploration Towards General Image Restorationcited by 6
  24. 2024Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolutioncited by 6
  25. 2026Training-Free Model Ensemble for Single-Image Super-Resolution via Strong-Branch Compensationcited by 6
  26. 2026The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overviewcited by 6
  27. 2026The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overviewcited by 6
  28. 2024Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolutioncited by 5
  29. 2024Real-Time Multi-Scene Visibility Enhancement for Promoting Navigational Safety of Vessels Under Complex Weather Conditionscited by 5
  30. 2024MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolutioncited by 5
  31. 2025BiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix Compression for Real Super-Resolutioncited by 5
  32. 2025Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networkscited by 5
  33. 2025Enhancing Image Restoration Transformer via Adaptive Translation Equivariancecited by 5
  34. 2025Degradation-Modeled Multipath Diffusion for Tunable Metalens Photographycited by 5
  35. 2025VSRM: A Robust Mamba-Based Framework for Video Super-Resolutioncited by 5
  36. 2023Entangled View-Epipolar Information Aggregation for Generalizable Neural Radiance Fieldscited by 4
  37. 2024Enhanced Control for Diffusion Bridge in Image Restorationcited by 4
  38. 2024Chain-of-Restoration: Multi-Task Image Restoration Models are Zero-Shot Step-by-Step Universal Image Restorerscited by 4

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