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Paper Citation Record · LEDGER

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution

As of 14 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2412.02234.

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

pith.paper-citation-record.v1
2412.02234 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:43:38.914760Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

53 of 53 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 78a1919c-5e40-4101-9362-060fa99dfe30 · outbound

This paper cites Fast, accurate, and lightweight super-resolution with cascading residual network.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Fast, accurate, and lightweight super-resolution with cascading residual network

Reference 1

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 1185978e-70b6-4926-b48b-ffc1129867d9 · outbound

This paper cites Pre-trained image processing transformer.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Pre-trained image processing transformer

Reference 2

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Observation af9cd5e8-fdec-4990-8775-8b6f2fbc3d99 · outbound

This paper cites Simple baselines for image restoration.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Simple baselines for image restoration

Reference 3

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Source-reported events for the cited work

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Observation 21acf323-dc98-4b1b-b050-371d3c30c93c · outbound

This paper cites Activating more pixels in image super- resolution transformer.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Activating more pixels in image super- resolution transformer

Reference 4

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Observation a7f6b927-8ce4-431d-b134-c5e1c1d27aa6 · outbound

This paper cites Mobile- former: Bridging mobilenet and transformer.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Mobile- former: Bridging mobilenet and transformer

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 95bbe9ab-9144-47f4-a512-41640618a4ab · outbound

This paper cites Rethinking coarse-to-fine approach in sin- gle image deblurring.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Rethinking coarse-to-fine approach in sin- gle image deblurring

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation ad17187d-6f72-4eba-bbb0-c16fe02be329 · outbound

This paper cites Fast, accurate and lightweight super-resolution with neural architecture search.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Fast, accurate and lightweight super-resolution with neural architecture search

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 226880d2-2fc4-4745-b2ba-3af59492eb1a · outbound

This paper cites Image super-resolution using deep convolutional net- works.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Image super-resolution using deep convolutional net- works

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation cc40d460-b826-4e9a-bd2c-90d5a5ab256b · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:43:36.302981Z digest=sha256:ca139f84198ba9c72c3dbf8eeeb999c5c98661366a03fc9bddf511d22dd7259a

Observation fdc8712a-6c1f-471b-ab32-aef6dcb9ff53 · outbound

This paper cites Image super-resolution using knowledge distillation.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Image super-resolution using knowledge distillation

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation adf9bce6-08a3-43a7-85a6-083e909c5ab5 · outbound

This paper cites Mixer-based local residual network for lightweight image super-resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Mixer-based local residual network for lightweight image super-resolution

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:36.402296Z digest=sha256:638329852f32327ef7ab2565b6a1cacf072eb18af1475e761ef7bccbb21fe572

Observation 24104215-2c43-4964-b416-25bc7b60e0f8 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Distilling the Knowledge in a Neural Network

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation fc568952-31bd-49b3-881f-a414dddd4f4c · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 13

Resolution
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no resolver link, observed 2026-08-11T23:43:36.554753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:43:36.554753Z digest=sha256:89e118f842d5020cdb8fa5c18cf15cb569ab2f11c1cb3ed0a2b548d499de9d17

Observation 0ebcbdda-f2f1-488d-ab3c-cd930973cbc2 · outbound

This paper cites Squeeze-and-excitation net- works.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Squeeze-and-excitation net- works

Reference 14

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:36.584750Z digest=sha256:30d95c309cd5187f47254a0f9a88b80ae29b9e8c9e9d80e758fd3c117b669c7b

Observation 56d54767-fc8a-43e0-bd2b-9acd755e9dab · outbound

This paper cites Sin- gle image super-resolution from transformed self-exemplars.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Sin- gle image super-resolution from transformed self-exemplars

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:46.104755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:36.630054Z digest=sha256:36457dc98463cca265a78d6f8b369251b540d064646fe8cd6a1f365e88d09847

Observation a4cdb1d8-3717-4997-8c34-c3efaf0b4ed9 · outbound

This paper cites LightViT: Towards Light-Weight Convolution-Free Vision Transformers.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution LightViT: Towards Light-Weight Convolution-Free Vision Transformers

Reference 16

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no resolver link, observed 2026-08-11T23:43:36.654741Z

Source-reported events for the cited work

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Observation 4d4be8cf-9898-428e-9c78-a3e7d9d9481c · outbound

This paper cites Fast and ac- curate single image super-resolution via information distil- lation network.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Fast and ac- curate single image super-resolution via information distil- lation network

Reference 17

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 1399d563-2519-4f5e-ba77-da72dd10499a · outbound

This paper cites Lightweight image super-resolution with information multi- distillation network.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Lightweight image super-resolution with information multi- distillation network

Reference 18

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation c863b80b-eb09-48ff-ad53-93e0e2d1d9d6 · outbound

This paper cites Accurate image super-resolution using very deep convolutional net- works.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Accurate image super-resolution using very deep convolutional net- works

Reference 19

Resolution
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Source-reported events for the cited work

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Observation 8b6af5cb-1e89-489f-8d42-965b9042fa22 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Adam: A Method for Stochastic Optimization

Reference 20

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Source-reported events for the cited work

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Observation d33b5524-54f2-49c9-9102-7951dc2230ac · outbound

This paper cites Residual local feature network for efficient super-resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Residual local feature network for efficient super-resolution

Reference 21

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 348ea66f-a2cc-4a85-99a6-4a333907c284 · outbound

This paper cites Srconvnet: A transformer-style con- vnet for lightweight image super-resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Srconvnet: A transformer-style con- vnet for lightweight image super-resolution

Reference 22

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Observation 4943b258-7ade-440b-85d5-a3faff511e5a · outbound

This paper cites On Efficient Transformer-Based Image Pre-training for Low-Level Vision.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution On Efficient Transformer-Based Image Pre-training for Low-Level Vision

Reference 23

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Source-reported events for the cited work

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Observation 32efea84-c7ff-4323-a74a-8e621ef5355a · outbound

This paper cites Effi- cient and explicit modelling of image hierarchies for image restoration.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Effi- cient and explicit modelling of image hierarchies for image restoration

Reference 24

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation ab42e444-12b7-4aec-81c9-0869706200df · outbound

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

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Swinir: Image restoration us- ing swin transformer

Reference 25

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 138f1ac3-92ef-46cb-9b63-d27bf48b1ec7 · outbound

This paper cites Enhanced deep residual networks for sin- gle image super-resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Enhanced deep residual networks for sin- gle image super-resolution

Reference 26

Resolution
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raw_fallback, observed 2026-08-11T23:43:44.527491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:37.314836Z digest=sha256:6d931c233c86aa9823dc80cebb09921106bf06fceae76dabf3f0c6ee9e04e100

Observation 875127f8-870a-4c51-9d94-59c980c550de · outbound

This paper cites Residual feature dis- tillation network for lightweight image super-resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Residual feature dis- tillation network for lightweight image super-resolution

Reference 27

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 19aa203a-1924-41a2-a596-a7a90b7daf43 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Swin transformer: Hierarchical vision transformer using shifted windows

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:44.128342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation d974bb70-d0bb-4906-9435-9f7a0be770e7 · outbound

This paper cites Transformer for single image super-resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Transformer for single image super-resolution

Reference 29

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:37.381913Z digest=sha256:50eae784cd46ba1cf2e55f32a17ff7c98b4df1e017c8e7e42d8fdfd836c9b4a3

Observation 2759b13f-36aa-4b1b-b703-f2d3de452337 · outbound

This paper cites Latticenet: Towards lightweight image super-resolution with lattice block.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Latticenet: Towards lightweight image super-resolution with lattice block

Reference 30

Resolution
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raw_fallback, observed 2026-08-11T23:43:43.702870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:37.392566Z digest=sha256:2adcc9a2e37196124c1e69c90a1ee41a84634054a51379d4c485588a60bc5814

Observation ab98c3cd-51dc-4dff-8df9-b3ff4f2c21c0 · outbound

This paper cites Shufflenet v2: Practical guidelines for efficient cnn architec- ture design.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Shufflenet v2: Practical guidelines for efficient cnn architec- ture design

Reference 31

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raw_fallback, observed 2026-08-11T23:43:43.551671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:37.485729Z digest=sha256:fa3e306c9f9a6b7c7ae9ea48d8d63025d1d630e4b792a4bcac5295c43e08fd0e

Observation ce76d141-30ac-4b02-a14c-0100894dea72 · outbound

This paper cites A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

Reference 32

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:37.548746Z digest=sha256:f6a2e82ac25e18604b7d04ea776f5d72b8efdedd97d46e7f726f4f0c5f7930ce

Observation dd758866-bf07-4a0a-9aad-0ee41d14f263 · outbound

This paper cites Sketch-based manga retrieval using manga109 dataset.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Sketch-based manga retrieval using manga109 dataset

Reference 33

Resolution
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raw_fallback, observed 2026-08-11T23:43:43.208442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:37.594826Z digest=sha256:ddb9fd79cdc51c69da1a6800b1ff39884c0d2b5f4cff5a417eba35e4e8f2c593

Observation d305b1d6-dc19-49d7-bb2d-d17249dd4293 · outbound

This paper cites MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 34

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no resolver link, observed 2026-08-11T23:43:37.621363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:43:37.621363Z digest=sha256:6beb6994dee6a505d4893b248a8d4d5eac26c839d6d661aecb7b25ad58df245f

Observation 13f6bd5c-abfa-412a-916c-32567cf9a107 · outbound

This paper cites Multi-attention based ul- tra lightweight image super-resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Multi-attention based ul- tra lightweight image super-resolution

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:42.980908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:37.673426Z digest=sha256:d5c269baedca3d6d01e2a74520bf3ab905b6337f78a4b316f47a9508947d7014

Observation fabc9955-783e-4b06-bd83-5171ad805ecf · outbound

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

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:42.773055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:37.804753Z digest=sha256:6e55db3472e9def56b6290261d4f63c2cde677deb5ebd6b21962d9e7576b079f

Observation b17464ce-2a5d-4d7b-9c66-0e17c9221cc0 · outbound

This paper cites Hy- brid pixel-unshuffled network for lightweight image super- resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Hy- brid pixel-unshuffled network for lightweight image super- resolution

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:42.586475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:37.904205Z digest=sha256:889f2d19d7477eec50a01b5e63b0e4c0c5b55a302a5824b63910f04fdffe44d5

Observation ac9c5d20-154b-4a44-83dd-03718a201cca · outbound

This paper cites Shufflemixer: An efficient convnet for image super-resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Shufflemixer: An efficient convnet for image super-resolution

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:42.345100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:37.958480Z digest=sha256:c60100b03e93d7eb8aaba81b3c7ca42f780d607c841a8efc1d11f16556d3c666

Observation 151450d3-c7ac-47c0-afb2-3b9a234cbe77 · outbound

This paper cites Spatially-adaptive feature modulation for efficient image super-resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Spatially-adaptive feature modulation for efficient image super-resolution

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:42.147761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:37.979561Z digest=sha256:c34f25c366e5f39189653d1d45c405c6d63fcd1596033d1dff8439bfcccfe06e

Observation bec208b9-8767-4d2e-a4cc-402f3e049776 · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Methods and results.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Ntire 2017 challenge on single image super-resolution: Methods and results

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:42.004747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:38.003304Z digest=sha256:78b4cd80917ffac8ffd1a76e1785330f577aad06c1fb639b9de1815991f88a3e

Observation 228830c7-d47e-453d-8c93-547df28786e6 · outbound

This paper cites Attention is all you need.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Attention is all you need

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:41.826195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:38.044754Z digest=sha256:79a8d00f182012cbcc9be4b6f3ba5350ba007dbeda590f65648d12690a321d88

Observation 5f0a886f-b044-4d0e-98bc-1e3c7a52280f · outbound

This paper cites Omni aggregation networks for lightweight im- age super-resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Omni aggregation networks for lightweight im- age super-resolution

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:41.681738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:38.157063Z digest=sha256:cb941f41f27576d297a7de9de19c2331944e965e53caa75106c5465c00b812a0

Observation 07dfb02c-3eb8-4d43-8ae4-e4c1e228c45b · outbound

This paper cites Global aligned structured sparsity learning for efficient image super-resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Global aligned structured sparsity learning for efficient image super-resolution

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:41.605050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:38.264751Z digest=sha256:a7a2505a6f8e22d798cea17dfd8d899449f6238de8c4c2a3eec209205d9f8bfb

Observation 3e5e97e4-6068-4d9c-9778-694204230880 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Linformer: Self-Attention with Linear Complexity

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T23:43:38.306309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:43:38.306309Z digest=sha256:932e9a3516061b84490e149737d6964dc0be2b6f600a4dc9dfd2765b140d0d03

Observation 7265f3a0-f79c-4f97-9c74-212f4c3ae5ac · outbound

This paper cites Osffnet: Omni-stage feature fu- sion network for lightweight image super-resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Osffnet: Omni-stage feature fu- sion network for lightweight image super-resolution

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:41.464755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:38.355602Z digest=sha256:edceb0da440a31a880f14975a69ebabc02082fae80c0fc392cd1cd3b00c1f35b

Observation 9d60ddbc-ba0c-4ac6-9538-e0d212196204 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Image quality assessment: from error visibility to structural similarity

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:41.270492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:38.424856Z digest=sha256:7218430645b0bf8182c2af420fd25f06442ebc0cd11e6cfe77922516e4a72be0

Observation f3f1195c-7c8c-4bfd-be69-81cdc99c1b16 · outbound

This paper cites Lite vision trans- former with enhanced self-attention.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Lite vision trans- former with enhanced self-attention

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:41.218726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:38.475874Z digest=sha256:1f7d668af0f837f3c8c1f88fc36c3ee476b721f684f794c8c92a2c9c0a260c21

Observation 2df1aac6-9fec-473d-85f9-84decbf22850 · outbound

This paper cites See more details: Efficient image super- resolution by experts mining.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution See more details: Efficient image super- resolution by experts mining

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:41.094998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:38.594750Z digest=sha256:0a7a27c7b9d8b80a605c6f47eb6f2ca7235fa5e9b4c1a8a12ac99de19c1c71c0

Observation 48e1c35d-947c-4201-8b40-d84a060bbde6 · outbound

This paper cites Restormer: Efficient transformer for high-resolution image restoration.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Restormer: Efficient transformer for high-resolution image restoration

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:40.892402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:38.684751Z digest=sha256:b4aee65513a2f02c9af0362e1c83b28f462155f5902564aaa1f2f27c4358182c

Observation a2d677f6-e14f-4949-b8d4-b2dc85593f29 · outbound

This paper cites Learning deep cnn denoiser prior for image restoration.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Learning deep cnn denoiser prior for image restoration

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:40.814963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:38.733186Z digest=sha256:45872e8b7a4aa58ea6eccf7d277dd5834abf4d3f45a4283e6d8d9b0898ea9623

Observation 4d0ae19e-3432-488c-9599-5907bb4d74cb · outbound

This paper cites Shufflenet: An extremely efficient convolutional neural net- work for mobile devices.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Shufflenet: An extremely efficient convolutional neural net- work for mobile devices

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:40.684905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:38.776398Z digest=sha256:420d82a2469b849374e00ab394f941d0187a0860a6ca9da99ad939acf970c8a8

Observation a4e85807-f68b-46f9-917e-f3d46574f32e · outbound

This paper cites Image super-resolution using very deep residual channel attention networks.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Image super-resolution using very deep residual channel attention networks

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:40.454750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:38.844751Z digest=sha256:34b7de0c99d7057ea7146454d21e964b9f1acac249f141438589b0dc5e10030c

Observation 33e28902-560c-4a53-992d-e7477caec667 · outbound

This paper cites Data-free knowledge dis- tillation for image super-resolution.

CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution Data-free knowledge dis- tillation for image super-resolution

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:43:40.244824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T23:43:38.914760Z digest=sha256:ec713dfe324d9111c98d556924cd19bd1e99910fa517b90e65ab3e349e7f5406

Pith citing papers

No inbound Pith citation observations are available.