# Learning Optical Flow with Adaptive Graph Reasoning

arXiv: https://arxiv.org/abs/2202.03857 · HTML: https://mlsanity.com/paper/2202.03857/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [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)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Optical Flow Estimation using a Spatial Pyramid Network](https://mlsanity.com/paper/1611.00850/) (2016)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [Learning to Estimate Hidden Motions with Global Motion Aggregation](https://mlsanity.com/paper/2104.02409/) (2021)
- [Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation](https://mlsanity.com/paper/1904.05290/) (2019)
- [MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask](https://mlsanity.com/paper/2003.10955/) (2020)
- [Deep Flow-Guided Video Inpainting](https://mlsanity.com/paper/1905.02884/) (2019)
- [Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation](https://mlsanity.com/paper/1809.05571/) (2018)
- [Learning feed-forward one-shot learners](https://mlsanity.com/paper/1606.05233/) (2016)
- [Hierarchical Discrete Distribution Decomposition for Match Density Estimation](https://mlsanity.com/paper/1812.06264/) (2018)
- [Bridge to Answer: Structure-aware Graph Interaction Network for Video Question Answering](https://mlsanity.com/paper/2104.14085/) (2021)
- [Learning Optical Flow from a Few Matches](https://mlsanity.com/paper/2104.02166/) (2021)
- [Person Re-identification with Deep Similarity-Guided Graph Neural Network](https://mlsanity.com/paper/1807.09975/) (2018)
- [Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation](https://mlsanity.com/paper/2010.14851/) (2020)
- [Cascade Graph Neural Networks for RGB-D Salient Object Detection](https://mlsanity.com/paper/2008.03087/) (2020)
- [ScopeFlow: Dynamic Scene Scoping for Optical Flow](https://mlsanity.com/paper/2002.10770/) (2020)

## Built on by (most cited first, 30 of 33)

- [Unifying Flow, Stereo and Depth Estimation](https://mlsanity.com/paper/2211.05783/) (2022)
- [SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow](https://mlsanity.com/paper/2405.14793/) (2024)
- [The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation](https://mlsanity.com/paper/2306.01923/) (2023)
- [SKFlow: Learning Optical Flow with Super Kernels](https://mlsanity.com/paper/2205.14623/) (2022)
- [Rethinking Optical Flow from Geometric Matching Consistent Perspective](https://mlsanity.com/paper/2303.08384/) (2023)
- [Disentangling Architecture and Training for Optical Flow](https://mlsanity.com/paper/2203.10712/) (2022)
- [TransFlow: Transformer as Flow Learner](https://mlsanity.com/paper/2304.11523/) (2023)
- [Learning Optical Flow from Event Camera with Rendered Dataset](https://mlsanity.com/paper/2303.11011/) (2023)
- [RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos](https://mlsanity.com/paper/2207.11075/) (2022)
- [AccFlow: Backward Accumulation for Long-Range Optical Flow](https://mlsanity.com/paper/2308.13133/) (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)
- [FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection](https://mlsanity.com/paper/2407.13133/) (2024)
- [StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences](https://mlsanity.com/paper/2311.17099/) (2023)
- [AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies](https://mlsanity.com/paper/2211.05709/) (2022)
- [Searching a Compact Architecture for Robust Multi-Exposure Image Fusion](https://mlsanity.com/paper/2305.12236/) (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)
- [Rethinking RAFT for Efficient Optical Flow](https://mlsanity.com/paper/2401.00833/) (2024)
- [UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model](https://mlsanity.com/paper/2405.02608/) (2024)
- [MPI-Flow: Learning Realistic Optical Flow with Multiplane Images](https://mlsanity.com/paper/2309.06714/) (2023)
- [Supervised Homography Learning with Realistic Dataset Generation](https://mlsanity.com/paper/2307.15353/) (2023)
- [ProMotion: Prototypes As Motion Learners](https://mlsanity.com/paper/2406.04999/) (2024)
- [Prototypical Transformer as Unified Motion Learners](https://mlsanity.com/paper/2406.01559/) (2024)
- [DensePercept-NCSSD: Vision Mamba towards Real-time Dense Visual Perception with Non-Causal State Space Duality](https://mlsanity.com/paper/2511.12671/) (2025)
- [FlowNAS: Neural Architecture Search for Optical Flow Estimation](https://mlsanity.com/paper/2207.01271/) (2022)
- [CGCV:Context Guided Correlation Volume for Optical Flow Neural Networks](https://mlsanity.com/paper/2212.10174/) (2022)
- [Modelling Human Visual Motion Processing with Trainable Motion Energy Sensing and a Self-attention Network](https://mlsanity.com/paper/2305.09156/) (2023)
- [Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation](https://mlsanity.com/paper/2312.07180/) (2023)
- [GraVITON: Graph based garment warping with attention guided inversion for Virtual-tryon](https://mlsanity.com/paper/2406.02184/) (2024)

## Abstract (arXiv)

Estimating per-pixel motion between video frames, known as optical flow, is a long-standing problem in video understanding and analysis. Most contemporary optical flow techniques largely focus on addressing the cross-image matching with feature similarity, with few methods considering how to explicitly reason over the given scene for achieving a holistic motion understanding. In this work, taking a fresh perspective, we introduce a novel graph-based approach, called adaptive graph reasoning for optical flow (AGFlow), to emphasize the value of scene/context information in optical flow. Our key idea is to decouple the context reasoning from the matching procedure, and exploit scene information to effectively assist motion estimation by learning to reason over the adaptive graph. The proposed AGFlow can effectively exploit the context information and incorporate it within the matching procedure, producing more robust and accurate results. On both Sintel clean and final passes, our AGFlow achieves the best accuracy with EPE of 1.43 and 2.47 pixels, outperforming state-of-the-art approaches by 11.2% and 13.6%, respectively.