Paper Citation Record · LEDGER

Learning Optical Flow Field via Neural Ordinary Differential Equation

As of 8 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2506.03290.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.03290 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:11:05.664353Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

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verified fuzzy42
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 738582ce-f3d4-4b90-aa63-5e2d87b8bf86 · outbound

This paper cites Deep equilibrium optical flow estimation.

Learning Optical Flow Field via Neural Ordinary Differential Equation Deep equilibrium optical flow estimation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.425416Z

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Observation a4804f53-9dc4-4e1f-81a1-b285cb29b387 · outbound

This paper cites Learn- ing long-term dependencies with gradient descent is difficult.

Learning Optical Flow Field via Neural Ordinary Differential Equation Learn- ing long-term dependencies with gradient descent is difficult

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.411256Z

Source-reported events for the cited work

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Observation 22ace260-f5d6-472f-bc2a-5a2b71f93322 · outbound

This paper cites Lu- cas/kanade meets horn/schunck: Combining local and global 8 optic flow methods.

Learning Optical Flow Field via Neural Ordinary Differential Equation Lu- cas/kanade meets horn/schunck: Combining local and global 8 optic flow methods

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.396669Z

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Observation 46ef2dbd-2bde-4364-abbc-3121059a8bd1 · outbound

This paper cites A naturalistic open source movie for optical flow evaluation.

Learning Optical Flow Field via Neural Ordinary Differential Equation A naturalistic open source movie for optical flow evaluation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.381792Z

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Observation 59c32444-8ba2-4238-993e-9b86eac988db · outbound

This paper cites Neural ordinary differential equa- tions.

Learning Optical Flow Field via Neural Ordinary Differential Equation Neural ordinary differential equa- tions

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.364830Z

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Observation dfd77aca-dfd0-46b5-92a5-492ae9da9160 · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

Learning Optical Flow Field via Neural Ordinary Differential Equation Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:11:05.460939Z

Source-reported events for the cited work

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Observation c9501c9e-58ea-4f7d-8e41-dbbef179c282 · outbound

This paper cites Gru-ode-bayes: Continuous modeling of sporadically-observed time series.

Learning Optical Flow Field via Neural Ordinary Differential Equation Gru-ode-bayes: Continuous modeling of sporadically-observed time series

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.349612Z

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Observation f08b1daf-4be9-454e-a83c-37cc9be85940 · outbound

This paper cites Flownet: Learning optical flow with convolutional networks.

Learning Optical Flow Field via Neural Ordinary Differential Equation Flownet: Learning optical flow with convolutional networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.334721Z

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Observation 9786569e-c5d1-428c-b4b7-f5efd20400c2 · outbound

This paper cites Two-frame motion estimation based on polynomial expansion.

Learning Optical Flow Field via Neural Ordinary Differential Equation Two-frame motion estimation based on polynomial expansion

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.320912Z

Source-reported events for the cited work

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Observation b8f66667-be9d-4596-b1e4-d9cc33e1f3ae · outbound

This paper cites Vision meets robotics: The kitti dataset.

Learning Optical Flow Field via Neural Ordinary Differential Equation Vision meets robotics: The kitti dataset

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.306525Z

Source-reported events for the cited work

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Observation 161155f8-e8a5-44f7-9bc6-30d8920cb555 · outbound

This paper cites On robustness of neural ordinary differential equations.

Learning Optical Flow Field via Neural Ordinary Differential Equation On robustness of neural ordinary differential equations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.291854Z

Source-reported events for the cited work

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Observation 98f363c0-12cd-43c6-8bf5-0525aa865138 · outbound

This paper cites Determining optical flow.

Learning Optical Flow Field via Neural Ordinary Differential Equation Determining optical flow

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.277086Z

Source-reported events for the cited work

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Observation 393f69c4-0467-47ad-bb61-aeacd2f25614 · outbound

This paper cites (Implicit)2: Implicit layers for implicit representations.

Learning Optical Flow Field via Neural Ordinary Differential Equation (Implicit)2: Implicit layers for implicit representations

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.262939Z

Source-reported events for the cited work

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Observation 2d6e36d4-b248-489c-b604-229a9dbd7af2 · outbound

This paper cites Flowformer: A transformer architecture for optical flow.

Learning Optical Flow Field via Neural Ordinary Differential Equation Flowformer: A transformer architecture for optical flow

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.247653Z

Source-reported events for the cited work

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Observation e386d894-b930-48fc-b84a-e4e9ee1227c0 · outbound

This paper cites Lite- flownet: A lightweight convolutional neural network for op- tical flow estimation.

Learning Optical Flow Field via Neural Ordinary Differential Equation Lite- flownet: A lightweight convolutional neural network for op- tical flow estimation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.231393Z

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Observation e5806052-3bbb-433b-a4c7-4b2c292f8c95 · outbound

This paper cites A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization.

Learning Optical Flow Field via Neural Ordinary Differential Equation A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:11:05.729389Z

Source-reported events for the cited work

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Observation be3f50b3-20fa-43d2-8582-ff6b6d222ae0 · outbound

This paper cites A lightweight optical flow cnn—revisiting data fidelity and regu- larization.

Learning Optical Flow Field via Neural Ordinary Differential Equation A lightweight optical flow cnn—revisiting data fidelity and regu- larization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.215892Z

Source-reported events for the cited work

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Observation 173f0c17-4e30-4688-aa80-a476decece10 · outbound

This paper cites Flownet 2.0: Evolution of optical flow estimation with deep networks.

Learning Optical Flow Field via Neural Ordinary Differential Equation Flownet 2.0: Evolution of optical flow estimation with deep networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.201853Z

Source-reported events for the cited work

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Observation ef87265a-7471-4ba7-b33b-2f87252427f5 · outbound

This paper cites Perceiver io: A general architecture for structured inputs & outputs.

Learning Optical Flow Field via Neural Ordinary Differential Equation Perceiver io: A general architecture for structured inputs & outputs

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.188190Z

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Observation 385e90a8-bf45-4ec2-bf64-4a8b2c666b24 · outbound

This paper cites Imposing consistency for optical flow estimation.

Learning Optical Flow Field via Neural Ordinary Differential Equation Imposing consistency for optical flow estimation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.174316Z

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Observation 3ed4117d-2f39-4334-8f13-a23f8c36924c · outbound

This paper cites Learning to estimate hidden motions with global motion aggregation.

Learning Optical Flow Field via Neural Ordinary Differential Equation Learning to estimate hidden motions with global motion aggregation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.160530Z

Source-reported events for the cited work

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Observation 94d070bc-11c0-4181-9969-a1acfa2bebe6 · outbound

This paper cites Beitrag zur näherungsweisen Integration totaler Differentialgleichungen.

Learning Optical Flow Field via Neural Ordinary Differential Equation Beitrag zur näherungsweisen Integration totaler Differentialgleichungen

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.146559Z

Source-reported events for the cited work

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Observation 9d84c1ab-ccb9-4ec0-b493-6138228b8804 · outbound

This paper cites Recurrent convolutional neural network for object recognition.

Learning Optical Flow Field via Neural Ordinary Differential Equation Recurrent convolutional neural network for object recognition

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.132488Z

Source-reported events for the cited work

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Observation 5bd23e79-e540-4f3a-9a88-edbde8304945 · outbound

This paper cites Decoupled Weight Decay Regularization.

Learning Optical Flow Field via Neural Ordinary Differential Equation Decoupled Weight Decay Regularization

Reference 24

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Source-reported events for the cited work

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Observation 43bec7af-719f-407c-b48f-85731c7302a4 · outbound

This paper cites An iterative image reg- istration technique with an application to stereo vision.

Learning Optical Flow Field via Neural Ordinary Differential Equation An iterative image reg- istration technique with an application to stereo vision

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.117402Z

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Observation 50804198-fc9c-4ff8-981b-4a696d05abba · outbound

This paper cites A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation.

Learning Optical Flow Field via Neural Ordinary Differential Equation A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.099902Z

Source-reported events for the cited work

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Observation 036b1639-aa2a-4199-920f-6e94f063b422 · outbound

This paper cites Object scene flow for autonomous vehicles.

Learning Optical Flow Field via Neural Ordinary Differential Equation Object scene flow for autonomous vehicles

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.085416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0092760c-46ab-4ff6-8f71-a6a3e72a0df3 · outbound

This paper cites Implicit sur- face representations as layers in neural networks.

Learning Optical Flow Field via Neural Ordinary Differential Equation Implicit sur- face representations as layers in neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.071520Z

Source-reported events for the cited work

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Observation ab52447f-cd4a-4cce-b6b3-7f2eedfaa40e · outbound

This paper cites Mathematical theory of optimal processes.

Learning Optical Flow Field via Neural Ordinary Differential Equation Mathematical theory of optimal processes

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.057160Z

Source-reported events for the cited work

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Observation 6a781333-5dc6-4296-bf9e-a6dc2ff638df · outbound

This paper cites Optical flow estima- tion using a spatial pyramid network.

Learning Optical Flow Field via Neural Ordinary Differential Equation Optical flow estima- tion using a spatial pyramid network

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.038817Z

Source-reported events for the cited work

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Observation 534b9eb0-da6b-4a24-b839-12b33c1ba50e · outbound

This paper cites an unresolved cited work.

Learning Optical Flow Field via Neural Ordinary Differential Equation Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:11:06.022866Z

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Observation 6e4179ce-8bea-4403-a383-8d45bef57050 · outbound

This paper cites Über die numerische auflösung von differential- gleichungen.

Learning Optical Flow Field via Neural Ordinary Differential Equation Über die numerische auflösung von differential- gleichungen

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:06.007818Z

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Observation dbb296d6-9a32-4125-8c21-2b469ac6d98e · outbound

This paper cites The surprising effectiveness of diffusion models for optical flow and monocular depth estimation.

Learning Optical Flow Field via Neural Ordinary Differential Equation The surprising effectiveness of diffusion models for optical flow and monocular depth estimation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.992844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f8881c1a-4a9e-4f9b-9ece-80c188778d9a · outbound

This paper cites Convolutional lstm network: A machine learning approach for precipitation now- casting.

Learning Optical Flow Field via Neural Ordinary Differential Equation Convolutional lstm network: A machine learning approach for precipitation now- casting

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.977532Z

Source-reported events for the cited work

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Observation aa107e13-180a-49ac-9985-69d48bd89f33 · outbound

This paper cites Implicit neural representa- tions with periodic activation functions.

Learning Optical Flow Field via Neural Ordinary Differential Equation Implicit neural representa- tions with periodic activation functions

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.961518Z

Source-reported events for the cited work

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Observation b06f7c75-96cd-4be9-a5f1-49c9c06e03c3 · outbound

This paper cites Physics-informed implicit represen- tations of equilibrium network flows.

Learning Optical Flow Field via Neural Ordinary Differential Equation Physics-informed implicit represen- tations of equilibrium network flows

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.943942Z

Source-reported events for the cited work

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Observation d2273981-09a3-4d12-8640-7d6c466ff78c · outbound

This paper cites Super-convergence: Very fast training of neural networks using large learning rates.

Learning Optical Flow Field via Neural Ordinary Differential Equation Super-convergence: Very fast training of neural networks using large learning rates

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.928282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8fd06319-5a6d-4472-9ce2-eddb9e20f3f5 · outbound

This paper cites Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume.

Learning Optical Flow Field via Neural Ordinary Differential Equation Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.912176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 01f6114c-5891-4de0-8d43-ba4a74928c01 · outbound

This paper cites Models matter, so does training: An empirical study of cnns for optical flow estimation.

Learning Optical Flow Field via Neural Ordinary Differential Equation Models matter, so does training: An empirical study of cnns for optical flow estimation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.894886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation da9cd37a-c48d-4c52-b2e4-995851609a4b · outbound

This paper cites Raft: Recurrent all-pairs field transforms for optical flow.

Learning Optical Flow Field via Neural Ordinary Differential Equation Raft: Recurrent all-pairs field transforms for optical flow

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.879916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 877f3e10-c162-4b8f-96ad-c1b8ccd7c639 · outbound

This paper cites Attention is all you need.

Learning Optical Flow Field via Neural Ordinary Differential Equation Attention is all you need

Reference 41

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Source-reported events for the cited work

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Observation b150c0ec-4cd7-4a01-9ad1-2eb27f6dea85 · outbound

This paper cites Backpropagation through time: what it does and how to do it.Proceedings of the IEEE, 78(10):1550–1560,.

Learning Optical Flow Field via Neural Ordinary Differential Equation Backpropagation through time: what it does and how to do it.Proceedings of the IEEE, 78(10):1550–1560,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.854245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bab06840-4a39-4097-a7be-5eae128608ee · outbound

This paper cites Gmflow: Learning optical flow via global matching.

Learning Optical Flow Field via Neural Ordinary Differential Equation Gmflow: Learning optical flow via global matching

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T11:11:05.639623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13cd54ba-a5a0-45ee-9459-90cecc3d71c4 · outbound

This paper cites Unifying flow, stereo and depth estimation.

Learning Optical Flow Field via Neural Ordinary Differential Equation Unifying flow, stereo and depth estimation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.826562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1c8d89a-622b-4689-8cd1-c423ed7fc5b7 · outbound

This paper cites V olumetric correspon- dence networks for optical flow.

Learning Optical Flow Field via Neural Ordinary Differential Equation V olumetric correspon- dence networks for optical flow

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.810868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7bc06a5f-4906-447c-804b-5a4e798c1c77 · outbound

This paper cites Separable flow: Learning motion cost volumes for optical flow estimation.

Learning Optical Flow Field via Neural Ordinary Differential Equation Separable flow: Learning motion cost volumes for optical flow estimation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.793808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3d9f84b6-01eb-46b0-afed-c944262a2574 · outbound

This paper cites Global matching with overlapping at- tention for optical flow estimation.

Learning Optical Flow Field via Neural Ordinary Differential Equation Global matching with overlapping at- tention for optical flow estimation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.778362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8e8c103b-59b2-4c0b-9c5f-de0e9440897f · outbound

This paper cites Adaptive checkpoint adjoint method for gradient estimation in neural ode.

Learning Optical Flow Field via Neural Ordinary Differential Equation Adaptive checkpoint adjoint method for gradient estimation in neural ode

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:11:05.762731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.