# SKFlow: Learning Optical Flow with Super Kernels

arXiv: https://arxiv.org/abs/2205.14623 · HTML: https://mlsanity.com/paper/2205.14623/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 55 references that are in the map.
- First posted on arXiv 29 May 2022.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017)
- [Learning to Estimate Hidden Motions with Global Motion Aggregation](https://mlsanity.com/paper/2104.02409/) (2021)
- [Local Relation Networks for Image Recognition](https://mlsanity.com/paper/1904.11491/) (2019)
- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network](https://mlsanity.com/paper/1703.02719/) (2017)
- [Understanding Convolution for Semantic Segmentation](https://mlsanity.com/paper/1702.08502/) (2017)
- [Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation](https://mlsanity.com/paper/1904.05290/) (2019)
- [CKConv: Continuous Kernel Convolution For Sequential Data](https://mlsanity.com/paper/2102.02611/) (2021)
- [MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask](https://mlsanity.com/paper/2003.10955/) (2020)
- [Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation](https://mlsanity.com/paper/1809.05571/) (2018)
- [Hierarchical Discrete Distribution Decomposition for Match Density Estimation](https://mlsanity.com/paper/1812.06264/) (2018)
- [A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization](https://mlsanity.com/paper/1903.07414/) (2019)
- [FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes](https://mlsanity.com/paper/2110.08059/) (2021)
- [High-Resolution Optical Flow from 1D Attention and Correlation](https://mlsanity.com/paper/2104.13918/) (2021)
- [Learning Optical Flow from a Few Matches](https://mlsanity.com/paper/2104.02166/) (2021)
- [Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation](https://mlsanity.com/paper/2010.14851/) (2020)
- [Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids](https://mlsanity.com/paper/1604.03513/) (2016)
- [Learning Optical Flow with Adaptive Graph Reasoning](https://mlsanity.com/paper/2202.03857/) (2022)

## Built on by (most cited first, 30 of 56)

- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow](https://mlsanity.com/paper/2405.14793/) (2024)
- [CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow](https://mlsanity.com/paper/2211.10408/) (2022)
- [The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation](https://mlsanity.com/paper/2306.01923/) (2023)
- [FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation](https://mlsanity.com/paper/2303.01237/) (2023)
- [VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation](https://mlsanity.com/paper/2303.08340/) (2023)
- [A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift](https://mlsanity.com/paper/2206.10810/) (2022)
- [MemFlow: Optical Flow Estimation and Prediction with Memory](https://mlsanity.com/paper/2404.04808/) (2024)
- [WAFT: Warping-Alone Field Transforms for Optical Flow](https://mlsanity.com/paper/2506.21526/) (2025)
- [Rethinking Optical Flow from Geometric Matching Consistent Perspective](https://mlsanity.com/paper/2303.08384/) (2023)
- [DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework](https://mlsanity.com/paper/2503.14880/) (2025)
- [Learning Optical Flow from Event Camera with Rendered Dataset](https://mlsanity.com/paper/2303.11011/) (2023)
- [Mask Propagation for Efficient Video Semantic Segmentation](https://mlsanity.com/paper/2310.18954/) (2023)
- [VideoGen-Eval: Agent-based System for Video Generation Evaluation](https://mlsanity.com/paper/2503.23452/) (2025)
- [DropMAE: Learning Representations via Masked Autoencoders with Spatial-Attention Dropout for Temporal Matching Tasks](https://mlsanity.com/paper/2304.00571/) (2023)
- [GAFlow: Incorporating Gaussian Attention into Optical Flow](https://mlsanity.com/paper/2309.16217/) (2023)
- [AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation](https://mlsanity.com/paper/2303.16493/) (2023)
- [SAMFlow: Eliminating Any Fragmentation in Optical Flow with Segment Anything Model](https://mlsanity.com/paper/2307.16586/) (2023)
- [CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning](https://mlsanity.com/paper/2311.02661/) (2023)
- [Win-Win: Training High-Resolution Vision Transformers from Two Windows](https://mlsanity.com/paper/2310.00632/) (2023)
- [StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences](https://mlsanity.com/paper/2311.17099/) (2023)
- [Searching a Compact Architecture for Robust Multi-Exposure Image Fusion](https://mlsanity.com/paper/2305.12236/) (2023)
- [LayeredFlow: A Real-World Benchmark for Non-Lambertian Multi-Layer Optical Flow](https://mlsanity.com/paper/2409.05688/) (2024)
- [FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases](https://mlsanity.com/paper/2509.05297/) (2025)
- [Context-PIPs: Persistent Independent Particles Demands Spatial Context Features](https://mlsanity.com/paper/2306.02000/) (2023)
- [Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume](https://mlsanity.com/paper/2312.03790/) (2023)
- [Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images](https://mlsanity.com/paper/2506.07740/) (2025)
- [Flow4Agent: Long-form Video Understanding via Motion Prior from Optical Flow](https://mlsanity.com/paper/2510.05836/) (2025)
- [H-MoRe: Learning Human-centric Motion Representation for Action Analysis](https://mlsanity.com/paper/2504.10676/) (2025)
- [MVFlow: Deep Optical Flow Estimation of Compressed Videos with Motion Vector Prior](https://mlsanity.com/paper/2308.01568/) (2023)

## Abstract (arXiv)

Optical flow estimation is a classical yet challenging task in computer vision. One of the essential factors in accurately predicting optical flow is to alleviate occlusions between frames. However, it is still a thorny problem for current top-performing optical flow estimation methods due to insufficient local evidence to model occluded areas. In this paper, we propose the Super Kernel Flow Network (SKFlow), a CNN architecture to ameliorate the impacts of occlusions on optical flow estimation. SKFlow benefits from the super kernels which bring enlarged receptive fields to complement the absent matching information and recover the occluded motions. We present efficient super kernel designs by utilizing conical connections and hybrid depth-wise convolutions. Extensive experiments demonstrate the effectiveness of SKFlow on multiple benchmarks, especially in the occluded areas. Without pre-trained backbones on ImageNet and with a modest increase in computation, SKFlow achieves compelling performance and ranks 1st among currently published methods on the Sintel benchmark. On the challenging Sintel clean and final passes (test), SKFlow surpasses the best-published result in the unmatched areas (7.96 and 12.50) by 9.09% and 7.92%. The code is available at hrefhttps://github.com/littlespray/SKFlowhttps://github.com/littlespray/SKFlow.