Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T13:22:31.205522Z
Paper Citation Record · LEDGER
As of 12 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 0 inbound Pith citation observations for arXiv:2412.13273.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T13:22:31.205522Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
72 of 72 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation ee60bfca-3522-4722-a8ff-aff9f0829421 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices tensorflow lite
Reference 1
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Reference 2
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Reference 3
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CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Large displacement optical flow: descriptor matching in variational motion estimation
Reference 4
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CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Improved optical flow for gesture-based human-robot interaction
Reference 7
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CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Learning on-road visual control for self-driving vehicles with auxiliary tasks
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CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Mfcflow: A motion feature compensated multi-frame recurrent network for optical flow estimation
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Observation a6e44f2e-99be-41c6-9b67-075060e7b002 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices End-to-end learning of motion representation for video understanding
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Observation b0b4b787-832a-4f4d-81e4-de3081d13c44 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Starflow: A spatiotemporal recurrent cell for lightweight multi-frame optical flow estimation
Reference 13
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CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Rethinking channel dimensions for efficient model design
Reference 14
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Observation 22fa44dd-f952-4fbd-96f5-e8c6e9fa6f1e · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Improving optical flow on a pyramid level
Reference 15
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CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Determining optical flow
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Observation e46df64c-4263-4c5a-b9c3-e7a776c2f160 · outbound
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Reference 17
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Observation 49e9c8f5-79c9-4fd0-ad91-1732b2a8982d · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
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Observation 773634d6-fb33-4e22-949e-5a4c856c6d70 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Flowformer: A transformer architecture for optical flow
Reference 19
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Observation 79ed9ab6-83c4-42d9-95da-0d0f30aef6e1 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Liteflownet3: Resolving correspondence ambiguity for more accurate optical flow estimation
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Reference 21
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CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Iterative residual refinement for joint optical flow and occlusion estimation
Reference 22
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CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Ai benchmark: Running deep neural networks on android smartphones
Reference 23
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Observation 717341e4-9a6f-4861-8309-4597bfbf16ce · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Flownet 2.0: Evolution of optical flow estimation with deep networks
Reference 24
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Observation 9761a8b7-ee6c-414a-93ae-5905bb76c019 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Perceiver IO: A General Architecture for Structured Inputs & Outputs
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Observation 210f39ed-ad09-4c0b-b31b-b101c1cc48b6 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Unsupervised learning of multi-frame optical flow with occlusions
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Observation 9b3292ea-8da9-4280-99c5-776f086b9e90 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Learning to estimate hidden motions with global motion aggregation
Reference 27
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Observation 154df96e-fca9-42f9-bb92-d3e13bc94683 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices The hci benchmark suite: Stereo and flow ground truth with uncertainties for urban autonomous driving
Reference 28
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Observation bd0dbdc0-8445-43b5-a94b-3c53470273dc · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Fdflownet: Fast optical flow estimation using a deep lightweight network
Reference 29
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Observation 2f64f808-cc1e-435b-8cb8-9e7e38c020f2 · outbound
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Reference 30
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Reference 31
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Observation 99b0df72-a44b-4fbb-8107-0444899572b8 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices On-Device Neural Net Inference with Mobile GPUs
Reference 32
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Observation 7a662570-b552-4902-9b97-14b3eac143c1 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Centermask: Real-time anchor-free instance segmentation
Reference 33
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Observation 2bcd703c-c273-4723-9e0a-f012a16d537f · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices An energy and gpu-computation efficient backbone network for real-time object detection
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Observation a0ef2c4e-0f1a-44e5-8ffd-cb0ce5de299f · outbound
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Observation a3de0e48-e93d-43ff-a87b-2364abde4190 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Gaflow: Incorporating gaussian attention into optical flow
Reference 36
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Observation dbeca0d9-bf27-42aa-8fc6-0078ab1b6725 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Reference 37
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Observation cb14652a-8b55-4e33-85c4-8ae3b5a76a35 · outbound
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Reference 38
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Observation b1c18878-6789-4b3e-b7ca-91b295c4269b · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Object scene flow for autonomous vehicles
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Reference 41
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CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Pytorch: An imperative style, high-performance deep learning library
Reference 42
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Observation 89db1f88-8405-45d1-b2fb-d5a905082047 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Optical flow estimation using a spatial pyramid network
Reference 43
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Reference 44
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Reference 45
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Reference 46
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CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation
Reference 47
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Reference 48
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Reference 49
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Reference 53
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Reference 54
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Reference 55
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Reference 56
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Reference 58
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Reference 59
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Reference 60
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Reference 61
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Reference 62
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Reference 63
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Observation 6f3dc381-63cf-45c7-93fc-7ed140587ab5 · outbound
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Gmflow: Learning optical flow via global matching
Reference 64
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CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices Accurate optical flow via direct cost volume processing
Reference 65
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Reference 66
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Reference 67
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Reference 68
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Reference 69
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Reference 70
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Reference 71
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Reference 72
Source-reported events for the cited work
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