mlsanityData to 7 Oct 2026

Spatial Transformer Networks

arXiv 1506.02025 ↗First posted 5 Jun 2015cs.CVcited by 1,423

Not on the map. Why?

Cited by 27 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. In this work we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimisation process. We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations.

Key facts. Cited by 1,423 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jun 2015.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 389; 2022: 358; 2023: 277; 2024: 185; 2025: 131; 2026: 82.

1,423 papers.

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  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2021Dynamic Neural Networks: A Surveycited by 405
  3. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 364
  4. 2022V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformercited by 320
  5. 2021Pre-Trained Models: Past, Present and Futurecited by 319
  6. 2021Vector Neurons: A General Framework for SO(3)-Equivariant Networkscited by 304
  7. 2022Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuningcited by 292
  8. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  9. 2022Unsupervised Semantic Segmentation by Distilling Feature Correspondencescited by 241
  10. 2021FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Camerascited by 239
  11. 2021Attention Mechanisms in Computer Vision: A Surveycited by 232
  12. 2022CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformerscited by 227
38 more
  1. 2023OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perceptioncited by 218
  2. 2021K-Net: Towards Unified Image Segmentationcited by 211
  3. 2021ReDet: A Rotation-equivariant Detector for Aerial Object Detectioncited by 202
  4. 2021COTR: Correspondence Transformer for Matching Across Imagescited by 182
  5. 2023Conditional Image-to-Video Generation with Latent Flow Diffusion Modelscited by 180
  6. 2021TimeLens: Event-based Video Frame Interpolationcited by 150
  7. 2023PolyFormer: Referring Image Segmentation as Sequential Polygon Generationcited by 147
  8. 2022PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmarkcited by 140
  9. 2021Fine-Grained Image Analysis with Deep Learning: A Surveycited by 135
  10. 2023Cross-Image Attention for Zero-Shot Appearance Transfercited by 135
  11. 2022Registration based Few-Shot Anomaly Detectioncited by 133
  12. 2021Unconstrained Scene Generation with Locally Conditioned Radiance Fieldscited by 130
  13. 2021Query2Label: A Simple Transformer Way to Multi-Label Classificationcited by 126
  14. 2021XVFI: eXtreme Video Frame Interpolationcited by 123
  15. 2023RepViT: Revisiting Mobile CNN From ViT Perspectivecited by 123
  16. 2021Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypescited by 120
  17. 2023ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformationcited by 120
  18. 2022Towards Large-Scale Small Object Detection: Survey and Benchmarkscited by 119
  19. 2021Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regressioncited by 112
  20. 2021Encoder Fusion Network with Co-Attention Embedding for Referring Image Segmentationcited by 111
  21. 2022Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusioncited by 110
  22. 2023Affordance Diffusion: Synthesizing Hand-Object Interactionscited by 110
  23. 2021GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinementcited by 109
  24. 2022Equivariance with Learned Canonicalization Functionscited by 106
  25. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  26. 2021Model-Based Domain Generalizationcited by 103
  27. 2021Pose Recognition with Cascade Transformerscited by 99
  28. 2022Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesiscited by 97
  29. 2021Progressive Domain Expansion Network for Single Domain Generalizationcited by 96
  30. 2023MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generationcited by 96
  31. 2021UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehiclescited by 95
  32. 2022KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradientscited by 95
  33. 2023Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modelingcited by 92
  34. 2021Lite-HRNet: A Lightweight High-Resolution Networkcited by 91
  35. 2021Contextual Transformer Networks for Visual Recognitioncited by 90
  36. 2022Object Scene Representation Transformercited by 90
  37. 2021ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registrationcited by 87
  38. 2021Iterative Filter Adaptive Network for Single Image Defocus Deblurringcited by 85

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