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Paper Citation Record · LEDGER

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots

As of 21 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2411.14576.

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

pith.paper-citation-record.v1
2411.14576 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:13:54.274419Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

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

43 of 43 outbound references displayed

  • verified exact0
  • verified fuzzy37
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 85cdb4c7-790f-471c-8b69-3a9c9ce9f24a · outbound

This paper cites Science, technology and the future of small autonomous drones.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Science, technology and the future of small autonomous drones

Reference 1

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raw_fallback, observed 2026-08-12T15:13:56.242102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 8bc8fdbb-de8b-4436-a1b2-6dad630b22f2 · outbound

This paper cites Active Vision Based Embodied-AI Design for Nano-UAV Autonomy.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Active Vision Based Embodied-AI Design for Nano-UAV Autonomy

Reference 2

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raw_fallback, observed 2026-08-12T15:13:56.198008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:53.816280Z digest=sha256:00f4f43d8cd71008f160e2ec093a70f13461c3a0d31bb5bcc369b5b6cedb5417

Observation 5d1b681c-3c99-4ee5-9c4d-40d49dfb8e71 · outbound

This paper cites Life signs detector using a drone in disaster zones.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Life signs detector using a drone in disaster zones

Reference 3

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raw_fallback, observed 2026-08-12T15:13:56.172700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 47c02676-c50e-4266-badc-a69db1846f90 · outbound

This paper cites Efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone.IEEE Robotics and Automation Letters , 2(2):1070–1076, 2017.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone.IEEE Robotics and Automation Letters , 2(2):1070–1076, 2017

Reference 4

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raw_fallback, observed 2026-08-12T15:13:56.144683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:53.834114Z digest=sha256:a619197a476b8d9173329e13b194ea4e20d99912cb8b3928560865bcebcf7176

Observation be85c447-81d6-40f1-8e96-1d07cdfce91f · outbound

This paper cites Integrated navigation system using camera and gimbaled laser scanner for indoor and outdoor autonomous flight of uavs.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Integrated navigation system using camera and gimbaled laser scanner for indoor and outdoor autonomous flight of uavs

Reference 5

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raw_fallback, observed 2026-08-12T15:13:56.110970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:53.844585Z digest=sha256:7a51a46d3b9442ba47299084d0a9646c69be8c35c922c37f43db2729b729c8a0

Observation 657c888d-e0fe-4c2e-bc6e-93d40777c3ec · outbound

This paper cites Deepedgebench: Benchmarking deep neural networks on edge devices.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Deepedgebench: Benchmarking deep neural networks on edge devices

Reference 6

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raw_fallback, observed 2026-08-12T15:13:56.081268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:53.852707Z digest=sha256:bebbf96a8feef8a2ae43a3423e684a016fc04fd00fe31d1f6dcf443a31dc2904

Observation 291025bf-ac54-4fcd-a555-14fc2b44de59 · outbound

This paper cites Efficient visual odometry and mapping for unmanned aerial vehicle using arm-based stereo vision pre-processing system.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Efficient visual odometry and mapping for unmanned aerial vehicle using arm-based stereo vision pre-processing system

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:53.859172Z digest=sha256:c8481503a6c05531689fab19f1b055f7496a7ee1caee647dc7dde5d814d9fbbf

Observation 5417989f-ce0f-4a82-b83b-c75177aad822 · outbound

This paper cites Navion: A 2-mw fully integrated real-time visual-inertial odometry accelerator for autonomous navigation of nano drones.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Navion: A 2-mw fully integrated real-time visual-inertial odometry accelerator for autonomous navigation of nano drones

Reference 8

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raw_fallback, observed 2026-08-12T15:13:56.025675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:53.865497Z digest=sha256:57a73493bbc5b8eaad212ce15958dc6d2c898f86d1c19de88408bdbb36c2db8d

Observation ecc63cc5-9a92-4e33-9976-51a50ba3bfab · outbound

This paper cites Determining optical flow.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Determining optical flow

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation e6c40d63-aec1-4974-a3e4-7c90929b23d5 · outbound

This paper cites Prgflow: Unified swap-aware deep global optical flow for aerial robot navigation.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Prgflow: Unified swap-aware deep global optical flow for aerial robot navigation

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 777a5f06-4b1b-4980-a5df-2fcb55c4091e · outbound

This paper cites CUAHN-VIO: Content-and-Uncertainty-Aware Homography Network for Visual-Inertial Odometry.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots CUAHN-VIO: Content-and-Uncertainty-Aware Homography Network for Visual-Inertial Odometry

Reference 11

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no resolver link, observed 2026-08-12T15:13:53.897311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:13:53.897311Z digest=sha256:029013e4f3ec8a01882cdcf46a93599a931b321a8222983b4080f4407b176c36

Observation 549e5bf6-964e-4702-81fe-5f8a6f32e080 · outbound

This paper cites Fast optical flow estimation and its application to real-time obstacle avoidance.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Fast optical flow estimation and its application to real-time obstacle avoidance

Reference 12

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raw_fallback, observed 2026-08-12T15:13:55.899905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:53.905020Z digest=sha256:d5e27b721fd702d0f14c2b5e15e1b0787f3abb95f73859d08fbfeb58e3954640

Observation 77ba1b74-7918-41eb-9a03-b326294077d0 · outbound

This paper cites Reactive obstacle avoidance for highly maneuverable vehicles based on a two-stage optical flow clustering.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Reactive obstacle avoidance for highly maneuverable vehicles based on a two-stage optical flow clustering

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:53.916120Z digest=sha256:b4d05a222384533084b1f9787fbe5d37612606c0c93e27259704ada616509417

Observation 3e32aeda-e940-4c94-9bbb-50ec3fc5c763 · outbound

This paper cites Evdodgenet: Deep dynamic obstacle dodging with event cameras.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Evdodgenet: Deep dynamic obstacle dodging with event cameras

Reference 14

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raw_fallback, observed 2026-08-12T15:13:55.833988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:53.926532Z digest=sha256:28a0fb50b76a06a2036c32069daf442fb4108d58e457ec070d06a5c4dd4046bb

Observation 38da5d41-4a40-4688-af22-54a410ec4e08 · outbound

This paper cites On-board velocity estimation and closed-loop control of a quadrotor uav based on optical flow.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots On-board velocity estimation and closed-loop control of a quadrotor uav based on optical flow

Reference 15

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:53.952864Z digest=sha256:b249423d402418ab44e53218b0d708c9f4b86d30b3b079e6cddf7d9ffd59f08e

Observation 4921ffdf-9ecc-418b-8dda-daeff7980f9d · outbound

This paper cites Optical-flow based self-supervised learning of obstacle appearance applied to MA V landing.Robotics and Autonomous Systems, 100:78–94, 2018.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Optical-flow based self-supervised learning of obstacle appearance applied to MA V landing.Robotics and Autonomous Systems, 100:78–94, 2018

Reference 16

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:53.964931Z digest=sha256:0f812de3d4f4c74fa942b8e9f0431ef7f350e4191d80b99795a6a7b7816c9574

Observation 55a6c030-ed7c-48fc-a3f8-52df02428fbc · outbound

This paper cites Gapflyt: Active vision based minimalist structure-less gap detection for quadrotor flight.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Gapflyt: Active vision based minimalist structure-less gap detection for quadrotor flight

Reference 17

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:53.975617Z digest=sha256:a44bf57cb0d2d0b7d65eec962c3df322e4f7e0350817ab8432a573b4ff79d3a7

Observation 7fc2fb5f-cb38-4655-a8f0-5cd00a648f60 · outbound

This paper cites The computation of optical flow.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots The computation of optical flow

Reference 18

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 432f33d2-8049-4f82-a48a-4ad00af9cd78 · outbound

This paper cites Iterative image registration technique with an application to stereo vision.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Iterative image registration technique with an application to stereo vision

Reference 19

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 2ca3686a-5f73-4bfc-bb18-10cf3cfeea4d · outbound

This paper cites an unresolved cited work.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Unresolved cited work

Reference 20

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:54.015202Z digest=sha256:0a8c9fe9126b40b82788736b8e8756f20eacaf424a9094e152c18f72e3ad0c31

Observation 0cdcf2f8-bc59-40ac-9388-9391f1f2d78e · outbound

This paper cites PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume

Reference 21

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raw_fallback, observed 2026-08-12T15:13:55.576692Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:54.029240Z digest=sha256:3fb34babca34a097f4337dddbb559d693f9c5b0867cc05e02bcd740bda15a91b

Observation ae840f7f-2909-472b-90f9-bb20d30d3d83 · outbound

This paper cites Raft: Recurrent all-pairs field transforms for optical flow (extended abstract).

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Raft: Recurrent all-pairs field transforms for optical flow (extended abstract)

Reference 22

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source=pdf_text observed=2026-08-12T15:13:54.043401Z digest=sha256:11d79369d8bfeb2a34f4d215487d16b6d591f710538874551707b58047424edf

Observation a9e3940f-2acf-4b2c-906b-48a7a600218d · outbound

This paper cites Flowformer++: Masked cost volume autoencoding for pretraining optical flow estimation.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Flowformer++: Masked cost volume autoencoding for pretraining optical flow estimation

Reference 23

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raw_fallback, observed 2026-08-12T15:13:55.479221Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:54.050524Z digest=sha256:8109cc23df6e54f69df5833b8a94249071a5135c30af3cd468756eb159cbf687

Observation 96b401c7-c1f3-4360-a4f5-020693997df1 · outbound

This paper cites Skflow: Learning optical flow with super kernels.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Skflow: Learning optical flow with super kernels

Reference 24

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raw_fallback, observed 2026-08-12T15:13:55.444595Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:54.064895Z digest=sha256:f8b94e774e24908b6cd0cf3c8a460a0dcdeab48b6a620b439ce08299be177688

Observation 4bc48830-ba22-4a9e-9591-86d35bbb2c41 · outbound

This paper cites Accflow: Backward accumulation for long-range optical flow.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Accflow: Backward accumulation for long-range optical flow

Reference 25

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raw_fallback, observed 2026-08-12T15:13:55.405178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:54.073111Z digest=sha256:185addfa05c39b1738f18223ae8490c1ac9d63ae61407d0d9981b4b9fe756df6

Observation f9263597-c604-4038-83c2-807086bb1ff5 · outbound

This paper cites A lightweight optical flow cnn —revisiting data fidelity and regularization.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots A lightweight optical flow cnn —revisiting data fidelity and regularization

Reference 26

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raw_fallback, observed 2026-08-12T15:13:55.368299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:54.081488Z digest=sha256:ef84b4df4ff03d5a97dcb709b44e7144734c5bb57c8fc879c121ccdced8a5a49

Observation 5d12375a-369e-49c4-8b11-81ecf5948c93 · outbound

This paper cites Fdflownet: Fast optical flow estimation using a deep lightweight network.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Fdflownet: Fast optical flow estimation using a deep lightweight network

Reference 27

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raw_fallback, observed 2026-08-12T15:13:55.336430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:54.092882Z digest=sha256:1daf2e5eeeaa4177fe75155f3046045d35d9d27de368cdb0c586050df4be0f4f

Observation 0fe9c71d-e7aa-4c53-8651-de72b7978e58 · outbound

This paper cites Nanoflownet: Real-time dense optical flow on a nano quadcopter.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Nanoflownet: Real-time dense optical flow on a nano quadcopter

Reference 28

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raw_fallback, observed 2026-08-12T15:13:55.304105Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:54.104199Z digest=sha256:4103d3c7cee68f1b6dd7b085c81b6938bc4d3e6153e6cb3a7e78dd71fc99683a

Observation 08f89390-ac58-470c-b7b0-fe422e032dc2 · outbound

This paper cites An evaluation of edge tpu accelerators for convolutional neural networks.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots An evaluation of edge tpu accelerators for convolutional neural networks

Reference 29

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raw_fallback, observed 2026-08-12T15:13:55.273882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:54.114232Z digest=sha256:be93faec38bb0b145ac8c0378e19620aa7f292a0e7605ca46f32a0a912078ed8

Observation 284f4699-af05-4f98-a167-18e3fc37a143 · outbound

This paper cites EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following

Reference 30

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no resolver link, observed 2026-08-12T15:13:54.124177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:13:54.124177Z digest=sha256:89209144930d8b71d2cc20f21cca0cdb25a35d99b0fb5ac363bc8e7ce2f461e2

Observation 0672008f-a106-4fa5-a2a4-bddbd788f9ae · outbound

This paper cites Nudgeseg: Zero-shot object segmentation by repeated physical interaction.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Nudgeseg: Zero-shot object segmentation by repeated physical interaction

Reference 31

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raw_fallback, observed 2026-08-12T15:13:55.240826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:54.133086Z digest=sha256:de866019da8aac5cc7abf4516047a4eda388c20b93d548c09ecf376d7f387246

Observation 675cfef4-2ba6-4ceb-9d38-f6164757bd19 · outbound

This paper cites Ajna: Generalized deep uncertainty for minimal perception on parsimonious robots.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Ajna: Generalized deep uncertainty for minimal perception on parsimonious robots

Reference 32

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raw_fallback, observed 2026-08-12T15:13:55.198789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T15:13:54.140503Z digest=sha256:a8d0a04099b0a73ee0d238c65ec05cae7ea33d91a09c355d6672f6e08cdbf28d

Observation 9468f917-d849-4b70-a173-ae85d3fa25bf · outbound

This paper cites an unresolved cited work.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Unresolved cited work

Reference 33

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raw_fallback, observed 2026-08-12T15:13:55.164138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 8b4c6cb3-7bd2-4439-838c-3dfe5dc5c322 · outbound

This paper cites A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

Reference 34

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 40ba12aa-9c02-47f0-8b7b-3efb08d07fe9 · outbound

This paper cites Vision based forward sensitive reactive control for a quadrotor vtol.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Vision based forward sensitive reactive control for a quadrotor vtol

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:13:55.134985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation f79e83e4-c545-4d37-bf1a-8f66356a1458 · outbound

This paper cites Morpheyes: Variable baseline stereo for quadrotor navigation.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Morpheyes: Variable baseline stereo for quadrotor navigation

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:13:55.093812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 40dbb855-6c4f-472d-8b3c-75e3859eeb9e · outbound

This paper cites Vision transformers for dense prediction.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Vision transformers for dense prediction

Reference 37

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 01962f74-ce33-4a48-84df-31e4c6a25db7 · outbound

This paper cites Realsense depth camera d435i.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Realsense depth camera d435i

Reference 38

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 936f9d2b-6bc4-445f-ae5a-8b66cb683bdd · outbound

This paper cites 0-mms: Zero-shot multi-motion segmentation with a monocular event camera.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots 0-mms: Zero-shot multi-motion segmentation with a monocular event camera

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:13:54.974722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation f0a95811-cefb-48f3-acc4-ab4e519ec1c7 · outbound

This paper cites Ranjan and Michael J.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Ranjan and Michael J

Reference 40

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation dce8ccb4-2e33-47a5-b840-119c45056c82 · outbound

This paper cites How fast is too fast? the role of perception latency in high-speed sense and avoid.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots How fast is too fast? the role of perception latency in high-speed sense and avoid

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:13:54.904199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 61baeda9-3d7a-49cb-bd60-ee5564b2b614 · outbound

This paper cites Opportunities and challenges with autonomous micro aerial vehicles.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Opportunities and challenges with autonomous micro aerial vehicles

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:13:54.862402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation fd0d3145-2845-402b-8bf2-40d440309c30 · outbound

This paper cites Maskflownet: Asymmetric feature matching with learnable occlusion mask.

EdgeFlowNet: 100FPS@1W Dense Optical Flow For Tiny Mobile Robots Maskflownet: Asymmetric feature matching with learnable occlusion mask

Reference 43

Resolution
metadata mismatch
raw_fallback, observed 2026-08-12T15:13:54.714073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Pith citing papers

No inbound Pith citation observations are available.