mlsanityData to 14 Oct 2026

Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

arXiv 1910.02190 ↗First posted 5 Oct 2019cs.CVcited by 232

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Abstract (arXiv)

This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems. The package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be inserted inside neural networks to train models to perform image transformations, camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations. Examples of classical vision problems implemented using our framework are provided including a benchmark comparing to existing vision libraries.

Key facts. Cited by 232 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Oct 2019.

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Citing papers per year: 2021: 34; 2022: 48; 2023: 52; 2024: 38; 2025: 37; 2026: 23.

232 papers.

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  12. 2022Equivariance with Learned Canonicalization Functionscited by 109
38 more
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  4. 2024Deep Patch Visual SLAMcited by 71
  5. 2021SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classificationcited by 68
  6. 2021Unsupervised Depth Completion with Calibrated Backprojection Layerscited by 61
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  8. 2023WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Modelscited by 53
  9. 2023NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotationscited by 53
  10. 2023TrustMark: Universal Watermarking for Arbitrary Resolution Imagescited by 51
  11. 2023FFCV: Accelerating Training by Removing Data Bottleneckscited by 49
  12. 2023Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensingcited by 48
  13. 2021Motion-aware Contrastive Video Representation Learning via Foreground-background Mergingcited by 45
  14. 2021SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policiescited by 43
  15. 2022MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Imagescited by 39
  16. 2022Revisiting Image Pyramid Structure for High Resolution Salient Object Detectioncited by 36
  17. 2022PyTorch Image Quality: Metrics for Image Quality Assessmentcited by 34
  18. 2022Generalized Differentiable RANSACcited by 30
  19. 2022On Implicit Bias in Overparameterized Bilevel Optimizationcited by 30
  20. 2022Proposal-Free Temporal Action Detection via Global Segmentation Mask Learningcited by 29
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  22. 2021Self-supervised Product Quantization for Deep Unsupervised Image Retrievalcited by 28
  23. 2021NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Resultscited by 26
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  26. 2022ReDFeat: Recoupling Detection and Description for Multimodal Feature Learningcited by 26
  27. 2024Video2Game: Real-time, Interactive, Realistic and Browser-Compatible Environment from a Single Videocited by 26
  28. 2021Learning by Planning: Language-Guided Global Image Editingcited by 25
  29. 2021Roto-translated Local Coordinate Frames For Interacting Dynamical Systemscited by 24
  30. 2021NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learningcited by 21
  31. 2024Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectorycited by 21
  32. 2021Improving the Perceptual Quality of 2D Animation Interpolationcited by 20
  33. 2022Cross-Domain Video Anomaly Detection without Target Domain Adaptationcited by 20
  34. 2021Reassessing the Limitations of CNN Methods for Camera Pose Regressioncited by 19
  35. 2021CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networkscited by 19
  36. 2022REAP: A Large-Scale Realistic Adversarial Patch Benchmarkcited by 18
  37. 2023Unsupervised Volumetric Animationcited by 18
  38. 2023AdvST: Revisiting Data Augmentations for Single Domain Generalizationcited by 16

50 most cited of 232 All 232 (JSON)