Paper Citation Record · LEDGER

Joint Flow And Feature Refinement Using Attention For Video Restoration

As of 9 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2505.16434.

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

pith.paper-citation-record.v1
2505.16434 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:03:17.464894Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:45:00.403277Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T10:45:00.716255Z

Reference resolution

62 of 62 outbound references displayed

verified exact3
verified fuzzy50
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f12ba373-5eee-4d6a-a0f9-1ff4061654b4 · outbound

This paper cites Restoration of video frames from a single blurred image with motion understanding.

Joint Flow And Feature Refinement Using Attention For Video Restoration Restoration of video frames from a single blurred image with motion understanding

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:28.214746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 25c620b9-165d-4f25-97a2-fd1e3bc8b6ad · outbound

This paper cites Video denoising via empirical bayesian estimation of space-time patches.Journal of Mathematical Imaging and Vision, 60:70–93, 2018.

Joint Flow And Feature Refinement Using Attention For Video Restoration Video denoising via empirical bayesian estimation of space-time patches.Journal of Mathematical Imaging and Vision, 60:70–93, 2018

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:28.005077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bdbc82e7-57bd-4d59-a2f4-a86cbe7545d8 · outbound

This paper cites Patch-based video denoising with optical flow estimation.

Joint Flow And Feature Refinement Using Attention For Video Restoration Patch-based video denoising with optical flow estimation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:27.791366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 60ad5b78-709b-4b6f-8e16-a776a2878a21 · outbound

This paper cites Real- time video super-resolution with spatio-temporal networks and motion compensation.

Joint Flow And Feature Refinement Using Attention For Video Restoration Real- time video super-resolution with spatio-temporal networks and motion compensation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:27.529190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 48b63ead-af51-4183-aea8-e9cdf96ed057 · outbound

This paper cites Video super-resolution transformer.

Joint Flow And Feature Refinement Using Attention For Video Restoration Video super-resolution transformer

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:27.235760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b08e2912-c8f4-4997-8a57-b559fcc0c791 · outbound

This paper cites Reference-based image super-resolution with deformable attention trans- former.

Joint Flow And Feature Refinement Using Attention For Video Restoration Reference-based image super-resolution with deformable attention trans- former

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:27.041328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 34aec6ad-df8d-49bb-8fef-f43a47da8698 · outbound

This paper cites Basicvsr: The search for essential compo- nents in video super-resolution and beyond.

Joint Flow And Feature Refinement Using Attention For Video Restoration Basicvsr: The search for essential compo- nents in video super-resolution and beyond

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:26.704231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 855fb0d5-754e-47be-8883-914ac533f3cb · outbound

This paper cites Basicvsr++: Improving video super- resolution with enhanced propagation and alignment.

Joint Flow And Feature Refinement Using Attention For Video Restoration Basicvsr++: Improving video super- resolution with enhanced propagation and alignment

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:26.373566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d801226a-b6c3-46a1-ad5e-5e608ec7520a · outbound

This paper cites Seeing motion in the dark.

Joint Flow And Feature Refinement Using Attention For Video Restoration Seeing motion in the dark

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:26.103258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f9bf3880-6118-43c8-9acd-27850009d2f5 · outbound

This paper cites Videnn: Deep blind video denoising.

Joint Flow And Feature Refinement Using Attention For Video Restoration Videnn: Deep blind video denoising

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:25.746600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 94a7a0d2-6410-46cc-a2b7-98b6a30167ad · outbound

This paper cites Non-Local Video Denoising by CNN.

Joint Flow And Feature Refinement Using Attention For Video Restoration Non-Local Video Denoising by CNN

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:14.508292Z

Source-reported events for the cited work

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Observation 9d1c82b9-47ad-4686-ba81-5de4f656ec92 · outbound

This paper cites FlowNet: Learning Optical Flow with Convolutional Networks.

Joint Flow And Feature Refinement Using Attention For Video Restoration FlowNet: Learning Optical Flow with Convolutional Networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:14.552302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14836c44-2417-425c-b7b5-c06c899a3278 · outbound

This paper cites Recurrent back-projection network for video super- resolution.

Joint Flow And Feature Refinement Using Attention For Video Restoration Recurrent back-projection network for video super- resolution

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:14.627351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bca81146-08c8-477b-82b6-ed3198c2f51c · outbound

This paper cites Bidirectional recurrent convolutional networks for multi-frame super- resolution.

Joint Flow And Feature Refinement Using Attention For Video Restoration Bidirectional recurrent convolutional networks for multi-frame super- resolution

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:25.431815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c9776ccc-2143-4ade-bfee-03df1b555a7a · outbound

This paper cites Deep video super-resolution network using dynamic upsampling filters without explicit motion compen- sation.

Joint Flow And Feature Refinement Using Attention For Video Restoration Deep video super-resolution network using dynamic upsampling filters without explicit motion compen- sation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:25.020245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2c6dc7e3-b3dc-490d-a487-ab14805b62dd · outbound

This paper cites Video object segmentation with language referring expressions.

Joint Flow And Feature Refinement Using Attention For Video Restoration Video object segmentation with language referring expressions

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:14.989743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9950a123-c008-4c88-b747-3e3a91829dfa · outbound

This paper cites Towards Real-world Event-guided Low-light Video Enhancement and Deblurring.

Joint Flow And Feature Refinement Using Attention For Video Restoration Towards Real-world Event-guided Low-light Video Enhancement and Deblurring

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:03:17.866739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1eca3a24-cd21-44c3-9c31-ddd8d144af04 · outbound

This paper cites Spatio-temporal transformer network for video restoration.

Joint Flow And Feature Refinement Using Attention For Video Restoration Spatio-temporal transformer network for video restoration

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:24.697720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 060d2db9-8429-49e5-bdb4-9f02c0791f51 · outbound

This paper cites Arvo: Learning all-range volumetric correspondence for video deblurring.

Joint Flow And Feature Refinement Using Attention For Video Restoration Arvo: Learning all-range volumetric correspondence for video deblurring

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:24.426637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ada3e38c-4dfd-4324-9611-9f06a8420fcd · outbound

This paper cites A simple baseline for video restoration with grouped spatial- temporal shift.

Joint Flow And Feature Refinement Using Attention For Video Restoration A simple baseline for video restoration with grouped spatial- temporal shift

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:24.226536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cbd0affc-c67d-4f55-b451-d236791aff0a · outbound

This paper cites Swinir: Image restoration us- ing swin transformer.

Joint Flow And Feature Refinement Using Attention For Video Restoration Swinir: Image restoration us- ing swin transformer

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:15.027731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d1317c4-fcae-4015-8778-93b1f3e4f753 · outbound

This paper cites Recurrent video restoration trans- former with guided deformable attention.

Joint Flow And Feature Refinement Using Attention For Video Restoration Recurrent video restoration trans- former with guided deformable attention

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:24.029290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7e5acb20-1ae1-46c0-9fc7-d99265515a7a · outbound

This paper cites Vrt: A video restoration transformer.

Joint Flow And Feature Refinement Using Attention For Video Restoration Vrt: A video restoration transformer

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:23.912054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e1d1e970-3f6e-4766-bca4-3c6cb81a4d3a · outbound

This paper cites Video denoising, deblocking, and en- hancement through separable 4-d nonlocal spatiotemporal transforms.

Joint Flow And Feature Refinement Using Attention For Video Restoration Video denoising, deblocking, and en- hancement through separable 4-d nonlocal spatiotemporal transforms

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:23.758808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6d151d29-c22a-4d70-a388-edf4191ab508 · outbound

This paper cites Efficient multi-stage video denoising with recurrent spatio-temporal fusion.

Joint Flow And Feature Refinement Using Attention For Video Restoration Efficient multi-stage video denoising with recurrent spatio-temporal fusion

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:23.508552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 936e4c07-f197-4441-ba3f-8bf971448815 · outbound

This paper cites Deep multi-scale convolutional neural network for dynamic scene deblurring, 2018.

Joint Flow And Feature Refinement Using Attention For Video Restoration Deep multi-scale convolutional neural network for dynamic scene deblurring, 2018

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:23.365746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 155070ba-a079-4bdc-9600-38285b8518ed · outbound

This paper cites Ntire 2019 challenge on video deblurring and super- resolution: Dataset and study.

Joint Flow And Feature Refinement Using Attention For Video Restoration Ntire 2019 challenge on video deblurring and super- resolution: Dataset and study

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:23.089318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a21affa4-3a2b-4e97-a66f-1ca44d3227a5 · outbound

This paper cites Cascaded deep video deblurring using temporal sharpness prior.

Joint Flow And Feature Refinement Using Attention For Video Restoration Cascaded deep video deblurring using temporal sharpness prior

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:22.855497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a7b8174d-d0ad-489c-99cf-ebec2b1fa839 · outbound

This paper cites Meshflow video denoising.

Joint Flow And Feature Refinement Using Attention For Video Restoration Meshflow video denoising

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:22.653189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 55294774-949a-41e0-a538-a6b3a6cabeb8 · outbound

This paper cites Unsupervised deep video denoising.

Joint Flow And Feature Refinement Using Attention For Video Restoration Unsupervised deep video denoising

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:22.526937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f28ad3c0-dfb2-457e-8ed7-5934ed4f0b0e · outbound

This paper cites Recurrent video deblurring with blur-invariant motion estimation and pixel volumes.

Joint Flow And Feature Refinement Using Attention For Video Restoration Recurrent video deblurring with blur-invariant motion estimation and pixel volumes

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:22.374152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b2930789-f794-4c34-9216-324f6443091f · outbound

This paper cites Temp- former: Temporally consistent transformer for video denois- ing.

Joint Flow And Feature Refinement Using Attention For Video Restoration Temp- former: Temporally consistent transformer for video denois- ing

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:22.139665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 67497aee-d82b-4604-aa00-ae2973f247d4 · outbound

This paper cites Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks.

Joint Flow And Feature Refinement Using Attention For Video Restoration Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:15.663093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89b208f3-320c-4b07-a05b-fa6d2340ec3e · outbound

This paper cites Deep video deblurring for hand-held cameras.

Joint Flow And Feature Refinement Using Attention For Video Restoration Deep video deblurring for hand-held cameras

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:21.941311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 81fa050f-40e9-4f21-b7e4-e52e52fe623b · outbound

This paper cites Gated spatio-temporal attention-guided video deblurring.

Joint Flow And Feature Refinement Using Attention For Video Restoration Gated spatio-temporal attention-guided video deblurring

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:21.798722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7b6e69fb-adbd-4a98-88e7-2db4b7b91cf2 · outbound

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

Joint Flow And Feature Refinement Using Attention For Video Restoration Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:15.709752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 417777cf-92a9-4b63-bfa3-d6539535495c · outbound

This paper cites Dvdnet: A fast network for deep video denoising.

Joint Flow And Feature Refinement Using Attention For Video Restoration Dvdnet: A fast network for deep video denoising

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:21.605344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a092a412-f051-491d-a5b2-a2590d2eeb35 · outbound

This paper cites Fastdvdnet: Towards real-time deep video denoising without flow estima- tion.

Joint Flow And Feature Refinement Using Attention For Video Restoration Fastdvdnet: Towards real-time deep video denoising without flow estima- tion

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:21.416461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 40658f1e-32a4-4ff8-8f5e-e4c048a9409c · outbound

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

Joint Flow And Feature Refinement Using Attention For Video Restoration Raft: Recurrent all-pairs field transforms for optical flow

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:15.809549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee50be19-a7ee-45e0-a50c-04eaa98d17e4 · outbound

This paper cites Tdan: Temporally-deformable alignment network for video super-resolution.

Joint Flow And Feature Refinement Using Attention For Video Restoration Tdan: Temporally-deformable alignment network for video super-resolution

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:21.231579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dfaf7767-182b-449e-9201-1d73903a1327 · outbound

This paper cites Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention.

Joint Flow And Feature Refinement Using Attention For Video Restoration Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:03:17.746904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fb9382f8-b2d0-43b4-9b88-34ad591b9f58 · outbound

This paper cites Patch craft: Video denoising by deep modeling and patch matching.

Joint Flow And Feature Refinement Using Attention For Video Restoration Patch craft: Video denoising by deep modeling and patch matching

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:21.047068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 89474667-9c0c-4133-bb22-e031f6734cc1 · outbound

This paper cites Patch craft: Video denoising by deep modeling and patch matching.

Joint Flow And Feature Refinement Using Attention For Video Restoration Patch craft: Video denoising by deep modeling and patch matching

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:20.939048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 194eb3e1-2060-478a-ad61-d77089b1d829 · outbound

This paper cites Attention is all you need.

Joint Flow And Feature Refinement Using Attention For Video Restoration Attention is all you need

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:16.173538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f9e7187-112e-40ef-8205-71df98bc1095 · outbound

This paper cites Edvr: Video restoration with enhanced deformable convolutional networks.

Joint Flow And Feature Refinement Using Attention For Video Restoration Edvr: Video restoration with enhanced deformable convolutional networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:20.792041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b717c576-2da6-438e-8c3c-cb84aa278a7b · outbound

This paper cites Occlusion aware unsupervised learning of optical flow.

Joint Flow And Feature Refinement Using Attention For Video Restoration Occlusion aware unsupervised learning of optical flow

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:20.593646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9ad8f7d4-73c0-4498-9cf5-afe1b8941ca2 · outbound

This paper cites Neural video depth stabilizer.

Joint Flow And Feature Refinement Using Attention For Video Restoration Neural video depth stabilizer

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:20.391932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 79700624-b8d9-4f1d-bf39-ae34c25e8014 · outbound

This paper cites Vision transformer with deformable attention.

Joint Flow And Feature Refinement Using Attention For Video Restoration Vision transformer with deformable attention

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:20.261712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5fc16a96-0bb8-4e27-b2a8-1d59c2ae4c5b · outbound

This paper cites Monocular relative depth percep- tion with web stereo data supervision.

Joint Flow And Feature Refinement Using Attention For Video Restoration Monocular relative depth percep- tion with web stereo data supervision

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:20.132894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1866c6f4-6682-4c6e-bdbf-6726fa4238e0 · outbound

This paper cites Deep video deblurring using sharpness features from exemplars.

Joint Flow And Feature Refinement Using Attention For Video Restoration Deep video deblurring using sharpness features from exemplars

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:19.981340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7416e072-6703-4d11-8248-c2b83c8eac82 · outbound

This paper cites Video enhancement with task-oriented flow.

Joint Flow And Feature Refinement Using Attention For Video Restoration Video enhancement with task-oriented flow

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:19.744574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3b0da74c-fa17-4944-979f-ebc4f925d510 · outbound

This paper cites Video enhancement with task-oriented flow.

Joint Flow And Feature Refinement Using Attention For Video Restoration Video enhancement with task-oriented flow

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:19.549644Z

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Observation 95e85752-e2b9-4dc4-a6d7-edd8961dbe69 · outbound

This paper cites Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations.

Joint Flow And Feature Refinement Using Attention For Video Restoration Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:19.416374Z

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Observation 23e816f9-b5b5-433e-8c45-f15125c18c83 · outbound

This paper cites Deep it- erative down-up cnn for image denoising.

Joint Flow And Feature Refinement Using Attention For Video Restoration Deep it- erative down-up cnn for image denoising

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:19.241470Z

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Observation 83462d3f-6d80-4d5d-bb9e-135ba93166c8 · outbound

This paper cites Joint learning of blind video denoising and optical flow estimation.

Joint Flow And Feature Refinement Using Attention For Video Restoration Joint learning of blind video denoising and optical flow estimation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:19.054614Z

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Observation aa4aad3c-acd8-4d6f-a3b5-f12017ca2d8b · outbound

This paper cites A review of recurrent neural networks: Lstm cells and net- work architectures.

Joint Flow And Feature Refinement Using Attention For Video Restoration A review of recurrent neural networks: Lstm cells and net- work architectures

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:18.894374Z

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Observation c1ba3770-f205-4406-a0f0-593fedb0dc80 · outbound

This paper cites Supervised raw video denoising with a benchmark dataset on dynamic scenes.

Joint Flow And Feature Refinement Using Attention For Video Restoration Supervised raw video denoising with a benchmark dataset on dynamic scenes

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:18.713661Z

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Observation 9ef7e265-5497-4dd1-bdde-07c2c7042bc4 · outbound

This paper cites Blur-aware spatio-temporal sparse transformer for video deblurring.

Joint Flow And Feature Refinement Using Attention For Video Restoration Blur-aware spatio-temporal sparse transformer for video deblurring

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:18.516019Z

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Observation ad1a2476-17d1-494b-a3ba-a7f2bec6ef3b · outbound

This paper cites Adversarial spatio-temporal learning for video deblurring.

Joint Flow And Feature Refinement Using Attention For Video Restoration Adversarial spatio-temporal learning for video deblurring

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:18.336364Z

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Observation 877d8f69-0608-4a06-be70-88c67e465cec · outbound

This paper cites Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis.

Joint Flow And Feature Refinement Using Attention For Video Restoration Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:03:17.632913Z

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Observation c96c3842-aa73-4f0d-bb63-1dbcc1000fea · outbound

This paper cites Spatio-temporal filter adaptive network for video deblurring.

Joint Flow And Feature Refinement Using Attention For Video Restoration Spatio-temporal filter adaptive network for video deblurring

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:18.153317Z

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Observation 1d990dfe-865e-4713-b148-3da4df15f313 · outbound

This paper cites De- formable convnets v2: More deformable, better results.

Joint Flow And Feature Refinement Using Attention For Video Restoration De- formable convnets v2: More deformable, better results

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:17.984891Z

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Pith citing papers

Observation 23738adc-91d9-4312-8744-7807ce2b9329 · inbound

StatsMerging: Statistics-Guided Model Merging via Task-Specific Teacher Distillation cites this paper.

StatsMerging: Statistics-Guided Model Merging via Task-Specific Teacher Distillation Joint Flow And Feature Refinement Using Attention For Video Restoration

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:45:00.721999Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.