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Paper Citation Record · LEDGER

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution

As of 17 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2505.02159.

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

pith.paper-citation-record.v1
2505.02159 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T01:09:10.191768Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

50 of 50 outbound references displayed

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  • verified fuzzy30
  • unresolved17
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ab54a669-4784-4513-b304-2f37d68cbdcc · outbound

This paper cites Longformer: The Long-Document Transformer.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Longformer: The Long-Document Transformer

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation fe0d8285-68fa-4b11-b00e-d4206bdb7725 · outbound

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

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Real- time video super-resolution with spatio-temporal networks and motion compensation

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation d23daf56-037c-4008-b8dc-61fd03a0f6ea · outbound

This paper cites Video Super-Resolution Transformer.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Video Super-Resolution Transformer

Reference 3

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no resolver link, observed 2026-08-16T01:09:09.905142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 044d9f3a-3d39-46ca-bbd6-0f48309ba300 · outbound

This paper cites BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation fc0730b1-c0f0-4773-b52a-64701a937bc6 · outbound

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

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Basicvsr: The search for essential compo- nents in video super-resolution and beyond

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation ddc71bae-05d0-470e-a59e-779be4219610 · outbound

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

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Basicvsr++: Improving video super- resolution with enhanced propagation and alignment

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 8ae29442-efbc-4f54-9eba-6b1b9faf483d · outbound

This paper cites Investigating tradeoffs in real-world video super-resolution.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Investigating tradeoffs in real-world video super-resolution

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 9d2525a6-e813-420a-8eb4-85242d651e34 · outbound

This paper cites Two deterministic half-quadratic regular- ization algorithms for computed imaging.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Two deterministic half-quadratic regular- ization algorithms for computed imaging

Reference 8

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 1d341f44-d7dd-49de-b47f-fc9b2b0bb87e · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Generating Long Sequences with Sparse Transformers

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 45204a22-af48-43df-9d08-a387d79151e9 · outbound

This paper cites Learning temporal coherence via self- supervision for gan-based video generation.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Learning temporal coherence via self- supervision for gan-based video generation

Reference 10

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b7a4860b-1ed5-4389-848f-4c88adc12bda · outbound

This paper cites Deformable convolutional net- works.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Deformable convolutional net- works

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 487f2ae8-3c61-4c7e-bfdd-daa5c78e84c4 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Flashattention: Fast and memory-efficient exact attention with io-awareness

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T01:09:09.954723Z digest=sha256:bef58187641a36f9235ccdb39eebf0b1dcf4a61f269180943fd0457b54a72f62

Observation ba9cc3b6-ddc2-448c-989b-9a2f871ea893 · outbound

This paper cites LongNet: Scaling Transformers to 1,000,000,000 Tokens.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 275f777b-828d-4d41-9b8c-6a945997e542 · outbound

This paper cites Efficient video super-resolution through recurrent latent space propagation.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Efficient video super-resolution through recurrent latent space propagation

Reference 14

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation e10ee485-676c-4c44-8e6a-57a8a41b1685 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Efficiently Modeling Long Sequences with Structured State Spaces

Reference 15

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Source-reported events for the cited work

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Observation 6944ae22-81c6-49c6-8a8f-c2c644185b91 · outbound

This paper cites Video Super-Resolution with Recurrent Structure-Detail Network.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Video Super-Resolution with Recurrent Structure-Detail Network

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5a5ce5e1-c534-4b40-a223-1ed6757d8f69 · outbound

This paper cites Video super-resolution with temporal group attention.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Video super-resolution with temporal group attention

Reference 17

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 4c1ac9d2-a4e6-40fc-a25d-f1a8efaee74f · outbound

This paper cites Look back and forth: Video super-resolution with explicit temporal difference modeling.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Look back and forth: Video super-resolution with explicit temporal difference modeling

Reference 18

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 3505aeea-0cd4-4723-83ba-6d9df91b0a66 · outbound

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

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 87327aab-a809-4ccc-9889-3ccb0339218b · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation e9922734-07fc-47e2-a455-ab2d2197e796 · outbound

This paper cites Efficient memory management for large lan- guage model serving with pagedattention.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Efficient memory management for large lan- guage model serving with pagedattention

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 444b20d1-5d06-4556-a7e4-cc13d0cd0df1 · outbound

This paper cites Sequence Parallelism: Long Sequence Training from System Perspective.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Sequence Parallelism: Long Sequence Training from System Perspective

Reference 22

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Observation 98df0be7-10a7-432a-a225-c2da68ba2b37 · outbound

This paper cites MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution

Reference 23

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Source-reported events for the cited work

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Observation a142d09f-99af-419c-9f95-a989bcbbedc1 · outbound

This paper cites VRT: A Video Restoration Transformer.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution VRT: A Video Restoration Transformer

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation cbedc642-c7fe-4216-b532-154ada117e32 · outbound

This paper cites Recurrent video restoration transformer with guided deformable attention.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Recurrent video restoration transformer with guided deformable attention

Reference 25

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation e6ea2b44-6868-4520-b153-88219e1e123d · outbound

This paper cites Learning trajectory-aware transformer for video super- 9 resolution.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Learning trajectory-aware transformer for video super- 9 resolution

Reference 26

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Source-reported events for the cited work

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Observation ac3f869f-e45c-460c-8966-589607da5cc5 · outbound

This paper cites Robust video super-resolution with learned temporal dynamics.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Robust video super-resolution with learned temporal dynamics

Reference 27

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation a6cd9c30-c60e-413b-8056-56cd3620cff0 · outbound

This paper cites Generating Wikipedia by Summarizing Long Sequences.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Generating Wikipedia by Summarizing Long Sequences

Reference 28

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Source-reported events for the cited work

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Observation ef14bbad-5a0b-47fc-a20d-d6d7a4cfbe4c · outbound

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

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Ntire 2019 challenge on video deblurring and super- resolution: Dataset and study

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation a3b7fae8-0b40-45f5-94c8-09ff1d3cb160 · outbound

This paper cites Training recurrent networks online without backtracking.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Training recurrent networks online without backtracking

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 68170d3f-ef20-4529-97da-101bbb62c7f5 · outbound

This paper cites Pgt: A pro- gressive method for training models on long videos.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Pgt: A pro- gressive method for training models on long videos

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-16T01:09:11.056955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 7eb13227-2439-47a2-90c4-93367bea952c · outbound

This paper cites Learning spatiotemporal frequency-transformer for com- pressed video super-resolution.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Learning spatiotemporal frequency-transformer for com- pressed video super-resolution

Reference 32

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raw_fallback, observed 2026-08-16T01:09:11.041201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation aea92930-82d4-4c57-a8ee-6f928ab453ca · outbound

This paper cites Frame-recurrent video super-resolution.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Frame-recurrent video super-resolution

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation c8efe0e3-fe82-4c9f-8dcc-c6adc28589dd · outbound

This paper cites Fast Transformer Decoding: One Write-Head is All You Need.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Fast Transformer Decoding: One Write-Head is All You Need

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T01:09:10.105446Z digest=sha256:f57d268a25a3d27bd13575c70d09fb0173b02616fc95788c21a2a2560d64e093

Observation 502c0c2d-3ea7-4549-a515-802888dc4269 · outbound

This paper cites Rethinking Alignment in Video Super-Resolution Transformers.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Rethinking Alignment in Video Super-Resolution Transformers

Reference 35

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no resolver link, observed 2026-08-16T01:09:10.113240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a7313377-ef7a-47af-8b1a-41cb025ef6a3 · outbound

This paper cites Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Reference 36

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 18b886c7-74b0-46a8-80f1-e6e533c99d27 · outbound

This paper cites Unbiased Online Recurrent Optimization.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Unbiased Online Recurrent Optimization

Reference 37

Resolution
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no resolver link, observed 2026-08-16T01:09:10.132862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e09264e2-86ee-440e-9742-953b9d0b78ac · outbound

This paper cites Unbiasing Truncated Backpropagation Through Time.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Unbiasing Truncated Backpropagation Through Time

Reference 38

Resolution
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no resolver link, observed 2026-08-16T01:09:10.139896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T01:09:10.139896Z digest=sha256:2d54b81b72aa4bd40ee3261262b124c37d5dc3509e85f64a5ee53bf85c36cea8

Observation 6e851af7-51bf-4b82-9a0f-bd53387afbe6 · outbound

This paper cites Detail-revealing deep video super-resolution.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Detail-revealing deep video super-resolution

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T01:09:10.989735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 9ceddd83-470f-4cb8-994b-cba04e09488c · outbound

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

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Tdan: Temporally-deformable alignment network for video super- resolution

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T01:09:10.973969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 12493779-4d68-4370-80ff-60ee65387604 · outbound

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

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Edvr: Video restoration with enhanced deformable convolutional networks

Reference 41

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation c2414183-3759-45d5-815d-45618d236c86 · outbound

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

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Backpropagation through time: what it does and how to do it.Proceedings of the IEEE, 78(10):1550–1560,

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-16T01:09:10.161070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T01:09:10.161070Z digest=sha256:3d1b965231cff39fc4ba420a76a0f41cc078efef9fca238082f80e5e255b6aae

Observation 96585d4b-67e1-480b-a0b6-2bb3d88a5b64 · outbound

This paper cites Gradient-based learning algorithms for recurrent networks and their computational complexity.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Gradient-based learning algorithms for recurrent networks and their computational complexity

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T01:09:10.931565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T01:09:10.166104Z digest=sha256:fe5d54620a89f973810e7daf44f7de4f45e8f2f63b24d75576dd812195c0310a

Observation 6ef542c7-4d59-40e6-8d48-74e8fb40fa8b · outbound

This paper cites Enhancing Video Super-Resolution via Implicit Resampling-based Alignment.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Enhancing Video Super-Resolution via Implicit Resampling-based Alignment

Reference 44

Resolution
verified exact
local_arxiv, observed 2026-08-16T01:09:10.244828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T01:09:10.170206Z digest=sha256:428224e77034bd643ed9277096fb131d6e8dbd8a71c4b46f8a5fc284fd39c090

Observation 9f2cfd6a-377c-43d8-b134-457e36cb1f92 · outbound

This paper cites Video enhancement with task-oriented flow.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Video enhancement with task-oriented flow

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T01:09:10.917216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T01:09:10.175159Z digest=sha256:cbaa196ece272d9a4516e334f2d09e980e5009993ef5da8aafeabb307bf453f8

Observation d9a4edc6-9e70-4876-9123-7f47ba6384f1 · outbound

This paper cites Video super-resolution trans- former with masked inter&intra-frame attention.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Video super-resolution trans- former with masked inter&intra-frame attention

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T01:09:10.903582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T01:09:10.178591Z digest=sha256:909fc80ca5d5c8bfbd54c401f1b63d651a1f24c05d683114ff17156b6d6fda0b

Observation ff3accb7-5bc2-4b74-b183-dc7d756e0b72 · outbound

This paper cites an unresolved cited work.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Unresolved cited work

Reference 48

Resolution
unresolved
raw_fallback, observed 2026-08-16T01:09:10.889189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 5e24d05d-5b17-4e72-8456-e144321c937a · outbound

This paper cites It is obvious in the Fig.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution It is obvious in the Fig

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T01:09:10.874443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T01:09:10.187150Z digest=sha256:feb2c6818c1caa5b23607ef1c128b6ab55e0732eb0465fd6f5e09aa6d1aa4608

Observation 52b21d61-9829-44ef-91c1-0ef1c73255b3 · outbound

This paper cites We calculate PSNR and SSIM on the RGB channel for these datatsets.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution We calculate PSNR and SSIM on the RGB channel for these datatsets

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T01:09:10.861074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 9119e2d2-6041-43e5-be66-1db572910e00 · outbound

This paper cites an unresolved cited work.

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution Unresolved cited work

Reference 2022

Resolution
parse uncertain
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T01:09:10.121667Z digest=sha256:7d1b417135a52d6ae2576f244327a507fcac53fffc1f952911853a7447dac8cc

Pith citing papers

No inbound Pith citation observations are available.