Paper Citation Record · LEDGER

The Linear Attention Resurrection in Vision Transformer

As of 17 August 2026, this Paper Citation Record lists 85 of 85 outbound references and 4 inbound Pith citation observations for arXiv:2501.16182.

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

pith.paper-citation-record.v1
2501.16182 v1

Coverage vector

measured 85 of 85 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:41:54.684747Z

measured 89 of 89 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T21:45:51.507382Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T14:25:45.811889Z

Reference resolution

85 of 85 outbound references displayed

verified exact3
verified fuzzy53
unresolved29
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 53703317-4cbe-4592-a0b2-666ebf72107c · outbound

This paper cites Xcit: Cross-covariance image transformers.

The Linear Attention Resurrection in Vision Transformer Xcit: Cross-covariance image transformers

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39256775-ef11-41f9-bf77-f96edde37a0d · outbound

This paper cites Longformer: The Long-Document Transformer.

The Linear Attention Resurrection in Vision Transformer Longformer: The Long-Document Transformer

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cf6e4a6-a599-408f-a098-ee1eefffab6c · outbound

This paper cites Efficientvit: Lightweight multi-scale attention for high- resolution dense prediction.

The Linear Attention Resurrection in Vision Transformer Efficientvit: Lightweight multi-scale attention for high- resolution dense prediction

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T13:41:54.468855Z

Source-reported events for the cited work

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Observation dbdaa291-6273-4862-87c1-a329f6d9c787 · outbound

This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

The Linear Attention Resurrection in Vision Transformer MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T13:41:54.472432Z

Source-reported events for the cited work

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Observation e5b7277a-5860-4d95-ae2c-ba5a79aefcf6 · outbound

This paper cites Region- vit: Regional-to-local attention for vision transformers.

The Linear Attention Resurrection in Vision Transformer Region- vit: Regional-to-local attention for vision transformers

Reference 5

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Source-reported events for the cited work

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Observation ca343d34-7b53-4fab-be1f-5fc3a1108413 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

The Linear Attention Resurrection in Vision Transformer Generating Long Sequences with Sparse Transformers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T13:41:54.479011Z

Source-reported events for the cited work

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Observation 0bb3f15f-cf17-41db-800a-6557d9842fc3 · outbound

This paper cites Rethinking attention with performers.

The Linear Attention Resurrection in Vision Transformer Rethinking attention with performers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T13:41:54.482109Z

Source-reported events for the cited work

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Observation aeabe998-5f2e-48e6-b8d2-948c9130826e · outbound

This paper cites Twins: Revisiting the design of spatial attention in vision transformers.

The Linear Attention Resurrection in Vision Transformer Twins: Revisiting the design of spatial attention in vision transformers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T13:41:54.485541Z

Source-reported events for the cited work

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Observation 33b66a31-4a46-487a-928c-512e655ca41c · outbound

This paper cites Conditional Positional Encodings for Vision Transformers.

The Linear Attention Resurrection in Vision Transformer Conditional Positional Encodings for Vision Transformers

Reference 9

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Source-reported events for the cited work

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Observation 0b8cbbfd-69a7-4b85-bf7d-3a8fc0b20c59 · outbound

This paper cites MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark.

The Linear Attention Resurrection in Vision Transformer MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark

Reference 10

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Observation ce39a538-06e2-4029-84ed-ae0225964dfd · outbound

This paper cites Randaugment: Practical automated data augmentation with a reduced search space.

The Linear Attention Resurrection in Vision Transformer Randaugment: Practical automated data augmentation with a reduced search space

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.283106Z

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Observation 3e4d2130-7923-4071-92a1-676f24a78560 · outbound

This paper cites Coatnet: Marrying convolution and attention for all data sizes.

The Linear Attention Resurrection in Vision Transformer Coatnet: Marrying convolution and attention for all data sizes

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.276190Z

Source-reported events for the cited work

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Observation 24e0e125-0762-49eb-b7ea-59b42081a03c · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

The Linear Attention Resurrection in Vision Transformer Imagenet: A large-scale hierarchical image database

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.267592Z

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Observation 2df0ee9b-d1c5-4754-b585-1d2d86ba2fa3 · outbound

This paper cites DaViT: Dual Attention Vision Transformers.

The Linear Attention Resurrection in Vision Transformer DaViT: Dual Attention Vision Transformers

Reference 14

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Observation 49ea40d0-400b-4123-960d-31fd35cb8900 · outbound

This paper cites Scaling up your kernels to 31x31: Revisiting large kernel design in cnns.

The Linear Attention Resurrection in Vision Transformer Scaling up your kernels to 31x31: Revisiting large kernel design in cnns

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.258909Z

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Observation f8f4c90b-4369-4fea-af96-325f690a7628 · outbound

This paper cites Cswin transformer: A general vision transformer backbone with cross-shaped windows.

The Linear Attention Resurrection in Vision Transformer Cswin transformer: A general vision transformer backbone with cross-shaped windows

Reference 16

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Observation a99b16dd-96b1-47e7-8620-9f3dd794a9c6 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

The Linear Attention Resurrection in Vision Transformer An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.240701Z

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Observation cdb75960-9efc-49c4-9943-2463a69ba0ff · outbound

This paper cites Multiscale vision transformers.

The Linear Attention Resurrection in Vision Transformer Multiscale vision transformers

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.233079Z

Source-reported events for the cited work

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Observation c6c60a2a-33aa-4292-9a9b-2e99575eeaeb · outbound

This paper cites Cmt: Convolutional neural networks meet vision transformers.

The Linear Attention Resurrection in Vision Transformer Cmt: Convolutional neural networks meet vision transformers

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.226073Z

Source-reported events for the cited work

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Observation cd2183b1-38ef-4550-b5fa-374bc45fcb86 · outbound

This paper cites Flatten transformer: Vision transformer using fo- cused linear attention.

The Linear Attention Resurrection in Vision Transformer Flatten transformer: Vision transformer using fo- cused linear attention

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.220427Z

Source-reported events for the cited work

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Observation 659ce1f6-e444-4b39-b056-472b2df311a2 · outbound

This paper cites Agent Attention: On the Integration of Softmax and Linear Attention.

The Linear Attention Resurrection in Vision Transformer Agent Attention: On the Integration of Softmax and Linear Attention

Reference 21

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Observation 9905e0dc-dbbd-4f97-9da7-471277c87176 · outbound

This paper cites FasterViT: Fast Vision Transformers with Hierarchical Attention.

The Linear Attention Resurrection in Vision Transformer FasterViT: Fast Vision Transformers with Hierarchical Attention

Reference 22

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Source-reported events for the cited work

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Observation b67f19e9-b2ab-45ce-80cb-9ef437fc4c8f · outbound

This paper cites Mask r-cnn.

The Linear Attention Resurrection in Vision Transformer Mask r-cnn

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.214428Z

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Observation 54915517-0e49-4f84-928d-fc75368ed735 · outbound

This paper cites Deep residual learning for image recognition.

The Linear Attention Resurrection in Vision Transformer Deep residual learning for image recognition

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.208590Z

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Observation 43342e25-08cc-43ae-a245-e2aade7dbeb6 · outbound

This paper cites Fair Comparison between Efficient Attentions.

The Linear Attention Resurrection in Vision Transformer Fair Comparison between Efficient Attentions

Reference 25

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Observation 0b150885-ca32-47b3-8c01-a2431e4b870d · outbound

This paper cites Deep networks with stochastic depth.

The Linear Attention Resurrection in Vision Transformer Deep networks with stochastic depth

Reference 26

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Observation dbe210f3-af68-467e-9dbe-6fd453481fff · outbound

This paper cites Perceiver IO: A General Architecture for Structured Inputs & Outputs.

The Linear Attention Resurrection in Vision Transformer Perceiver IO: A General Architecture for Structured Inputs & Outputs

Reference 27

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Observation f319842a-fc72-409d-abe7-ee3f97147935 · outbound

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

The Linear Attention Resurrection in Vision Transformer Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.196396Z

Source-reported events for the cited work

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Observation 498c3ad6-a224-4e65-973a-8a5a0a722963 · outbound

This paper cites SimA: Simple Softmax-free Attention for Vision Transformers.

The Linear Attention Resurrection in Vision Transformer SimA: Simple Softmax-free Attention for Vision Transformers

Reference 29

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Observation f53ff697-b55d-42d4-b795-e7f0ae1f6ebd · outbound

This paper cites Imagenet classification with deep convolutional neural net- works.

The Linear Attention Resurrection in Vision Transformer Imagenet classification with deep convolutional neural net- works

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.189731Z

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Observation 667ad20d-5f34-4533-83a3-a6121f39d9fd · outbound

This paper cites Set transformer: A frame- work for attention-based permutation-invariant neural net- works.

The Linear Attention Resurrection in Vision Transformer Set transformer: A frame- work for attention-based permutation-invariant neural net- works

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.182762Z

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Observation 74e26e97-924e-44fb-88e0-c79f5004ae24 · outbound

This paper cites Mpvit: Multi-path vision transformer for dense pre- diction.

The Linear Attention Resurrection in Vision Transformer Mpvit: Multi-path vision transformer for dense pre- diction

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.175382Z

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Observation 608cf3a1-2885-4fa8-aca2-824f9335ae84 · outbound

This paper cites Focal loss for dense object detection.

The Linear Attention Resurrection in Vision Transformer Focal loss for dense object detection

Reference 33

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Observation 9c551716-9a48-4aed-9996-99987ad869c6 · outbound

This paper cites Microsoft coco: Common objects in context.

The Linear Attention Resurrection in Vision Transformer Microsoft coco: Common objects in context

Reference 34

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Observation dce7776b-9f0f-4d48-8f65-7c522a6a9583 · outbound

This paper cites Dynamic group transformer: A general vision transformer backbone with dynamic group attention.

The Linear Attention Resurrection in Vision Transformer Dynamic group transformer: A general vision transformer backbone with dynamic group attention

Reference 35

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Observation 2eacca5a-81c3-49e0-848d-bcf614fa3224 · outbound

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

The Linear Attention Resurrection in Vision Transformer Swin transformer: Hierarchical vision transformer using shifted windows

Reference 36

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Observation 0cc4e467-c168-40c2-a016-23f75fca6a3f · outbound

This paper cites A convnet for the 2020s.

The Linear Attention Resurrection in Vision Transformer A convnet for the 2020s

Reference 37

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Observation 1787b545-6413-48c2-8eaa-89a6c63124ab · outbound

This paper cites Decoupled Weight Decay Regularization.

The Linear Attention Resurrection in Vision Transformer Decoupled Weight Decay Regularization

Reference 38

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This paper cites How do vision transformers work? In ICLR, 2022.

The Linear Attention Resurrection in Vision Transformer How do vision transformers work? In ICLR, 2022

Reference 39

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Observation f361c433-cd01-480d-b4e4-0216223ec8dd · outbound

This paper cites Random feature attention.

The Linear Attention Resurrection in Vision Transformer Random feature attention

Reference 40

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Observation 1f7dad0d-dc52-47c5-b996-8d21b654fc74 · outbound

This paper cites Acceleration of stochastic approximation by averaging.

The Linear Attention Resurrection in Vision Transformer Acceleration of stochastic approximation by averaging

Reference 41

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Observation dccae38d-9b8a-46e3-bd55-4b0c685e6731 · outbound

This paper cites cosformer: Rethinking softmax in attention.

The Linear Attention Resurrection in Vision Transformer cosformer: Rethinking softmax in attention

Reference 42

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Observation f4d76945-09d5-4d2f-8fd4-86236f627b68 · outbound

This paper cites Amixer: Adaptive weight mixing for self-attention free vi- sion transformers.

The Linear Attention Resurrection in Vision Transformer Amixer: Adaptive weight mixing for self-attention free vi- sion transformers

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.106648Z

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Observation 188a6295-32d4-4d39-80d8-1e6fcc6b585c · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

The Linear Attention Resurrection in Vision Transformer Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.099492Z

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Observation 5b481d09-7545-4826-8ead-aa8284e7a1b3 · outbound

This paper cites Efficient attention: Attention with linear complexities.

The Linear Attention Resurrection in Vision Transformer Efficient attention: Attention with linear complexities

Reference 45

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Observation bef7f0c8-6da9-45de-a6a4-91a51f8f9091 · outbound

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The Linear Attention Resurrection in Vision Transformer Inception Transformer

Reference 46

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This paper cites Segmenter: Transformer for semantic segmenta- tion.

The Linear Attention Resurrection in Vision Transformer Segmenter: Transformer for semantic segmenta- tion

Reference 47

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This paper cites Augmenting Self-attention with Persistent Memory.

The Linear Attention Resurrection in Vision Transformer Augmenting Self-attention with Persistent Memory

Reference 48

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The Linear Attention Resurrection in Vision Transformer Rethinking the inception ar- chitecture for computer vision

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T13:41:54.586985Z

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This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

The Linear Attention Resurrection in Vision Transformer Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.075890Z

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Observation 0317fcbe-6499-4e9e-9bb8-1e703e88224f · outbound

This paper cites Efficient transformers: A survey.

The Linear Attention Resurrection in Vision Transformer Efficient transformers: A survey

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.067443Z

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Observation 7ce4c454-ed5d-4a27-9a95-2c48bf176266 · outbound

This paper cites Training data-efficient image transformers & distillation through at- tention.

The Linear Attention Resurrection in Vision Transformer Training data-efficient image transformers & distillation through at- tention

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.059611Z

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Observation cfa3323d-b45e-4a49-af3a-ef616dcf0755 · outbound

This paper cites Are Convolutional Neural Networks or Transformers more like human vision?.

The Linear Attention Resurrection in Vision Transformer Are Convolutional Neural Networks or Transformers more like human vision?

Reference 53

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Observation c04b02fa-b8ea-4f46-a5f8-a5bf04e6c35f · outbound

This paper cites Attention is all you need.

The Linear Attention Resurrection in Vision Transformer Attention is all you need

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.051071Z

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Observation 80c01e02-0a26-4bd0-98cc-b1c564dbe15e · outbound

This paper cites Repvit: Revisiting mobile cnn from vit perspective.

The Linear Attention Resurrection in Vision Transformer Repvit: Revisiting mobile cnn from vit perspective

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.043224Z

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Observation 6570c2a8-1fc1-4668-be33-bb8c617bce0c · outbound

This paper cites Convolutional Embedding Makes Hierarchical Vision Transformer Stronger.

The Linear Attention Resurrection in Vision Transformer Convolutional Embedding Makes Hierarchical Vision Transformer Stronger

Reference 56

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Observation 04811f4b-de6c-44e1-bac8-2a185ba97dc5 · outbound

This paper cites Scaled relu matters for training vision transformers.

The Linear Attention Resurrection in Vision Transformer Scaled relu matters for training vision transformers

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.034447Z

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Observation 834ecf1b-2767-4701-841e-89a33622e4f2 · outbound

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

The Linear Attention Resurrection in Vision Transformer Linformer: Self-Attention with Linear Complexity

Reference 58

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Observation 859760b1-426f-4d72-9613-96915e7a82cf · outbound

This paper cites Pyramid vision transformer: A versatile backbone for dense prediction without convolutions.

The Linear Attention Resurrection in Vision Transformer Pyramid vision transformer: A versatile backbone for dense prediction without convolutions

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-10T13:41:54.613210Z

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Observation c079d26e-444c-466c-aeb7-f066ab8b6e4a · outbound

This paper cites Pvt v2: Improved baselines with pyramid vision transformer.

The Linear Attention Resurrection in Vision Transformer Pvt v2: Improved baselines with pyramid vision transformer

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.021780Z

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Observation 4f8bc03d-2dcf-4036-bcbf-01419997f057 · outbound

This paper cites Crossformer: A versatile vision transformer hinging on cross-scale attention.

The Linear Attention Resurrection in Vision Transformer Crossformer: A versatile vision transformer hinging on cross-scale attention

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.013229Z

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Observation 2aac9602-def6-48d7-a2ae-108d5cea60b5 · outbound

This paper cites Cvt: Introducing convolutions to vision transformers.

The Linear Attention Resurrection in Vision Transformer Cvt: Introducing convolutions to vision transformers

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.007134Z

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Observation 129fba07-ef5a-4fb1-9525-867ce9bd9e4b · outbound

This paper cites Pale transformer: A general vision transformer backbone with pale-shaped attention.

The Linear Attention Resurrection in Vision Transformer Pale transformer: A general vision transformer backbone with pale-shaped attention

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:55.000498Z

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Observation 49dece36-a2d2-4f3d-922f-0dd84050cb6d · outbound

This paper cites Unified perceptual parsing for scene understand- ing.

The Linear Attention Resurrection in Vision Transformer Unified perceptual parsing for scene understand- ing

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.993336Z

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Observation 65326ba6-0fc5-4794-bb68-f7bf27f457e1 · outbound

This paper cites Aggregated residual transformations for deep neural networks.

The Linear Attention Resurrection in Vision Transformer Aggregated residual transformations for deep neural networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.986716Z

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Observation 5c0e3033-a4b7-4d4b-a182-aff0b78b290f · outbound

This paper cites Nystr¨omformer: A nystr¨om-based algorithm for approximat- ing self-attention.

The Linear Attention Resurrection in Vision Transformer Nystr¨omformer: A nystr¨om-based algorithm for approximat- ing self-attention

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.979235Z

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Observation 2c553c67-1ad3-4e6d-8058-6848e1e39132 · outbound

This paper cites MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models.

The Linear Attention Resurrection in Vision Transformer MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-08-10T13:41:54.730837Z

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Observation b09c4d58-dfe3-4a53-8a7f-66656b0bb54b · outbound

This paper cites Focal attention for long-range interactions in vision transformers.

The Linear Attention Resurrection in Vision Transformer Focal attention for long-range interactions in vision transformers

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.972414Z

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Observation bdddb86f-80d0-4e73-88e6-61f78e2aa32e · outbound

This paper cites MetaFormer Baselines for Vision.

The Linear Attention Resurrection in Vision Transformer MetaFormer Baselines for Vision

Reference 69

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Observation 7f3003e8-f2df-463f-97da-a617546d1bc9 · outbound

This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.

The Linear Attention Resurrection in Vision Transformer Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.964345Z

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Observation bd128eb7-c02e-4ffb-bde4-15812275094e · outbound

This paper cites Shvit: Single-head vision transformer with memory efficient macro design.

The Linear Attention Resurrection in Vision Transformer Shvit: Single-head vision transformer with memory efficient macro design

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.954231Z

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Observation af5cc667-9c1f-4d35-9c6e-b1086646134b · outbound

This paper cites Big bird: Transformers for longer sequences.

The Linear Attention Resurrection in Vision Transformer Big bird: Transformers for longer sequences

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.944515Z

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Observation 978ac9f7-5421-42df-b3c9-f33194e6de93 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

The Linear Attention Resurrection in Vision Transformer mixup: Beyond Empirical Risk Minimization

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-10T13:41:54.651246Z

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Observation e0cdd8e0-5d83-4bdc-9b85-56fe77a8d6a4 · outbound

This paper cites Poolingformer: Long document modeling with pooling attention.

The Linear Attention Resurrection in Vision Transformer Poolingformer: Long document modeling with pooling attention

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.936358Z

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Observation 6d7e9fb4-ef25-481c-b6d8-23f1cbc388da · outbound

This paper cites Multi-scale vision long- former: A new vision transformer for high-resolution image encoding.

The Linear Attention Resurrection in Vision Transformer Multi-scale vision long- former: A new vision transformer for high-resolution image encoding

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-10T13:41:54.655424Z

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Observation 14f3b03c-b0c8-48f8-a72b-4689136af9da · outbound

This paper cites ResT V2: Simpler, Faster and Stronger.

The Linear Attention Resurrection in Vision Transformer ResT V2: Simpler, Faster and Stronger

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-10T13:41:54.658436Z

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Observation 40e3b34e-c3b2-4b06-a4ec-a6fbc96b0e23 · outbound

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The Linear Attention Resurrection in Vision Transformer Random erasing data augmentation

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.923515Z

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Observation 68ba0622-2d50-431a-ba76-3644a306287d · outbound

This paper cites Scene parsing through ade20k dataset.

The Linear Attention Resurrection in Vision Transformer Scene parsing through ade20k dataset

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.915800Z

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Observation c66b08be-7f29-4988-9a03-e4b4ad987537 · outbound

This paper cites Long-short transformer: Efficient transformers for language and vision.

The Linear Attention Resurrection in Vision Transformer Long-short transformer: Efficient transformers for language and vision

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.908606Z

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Observation 618a4871-b231-4769-9da6-5879aa2ea8a1 · outbound

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The Linear Attention Resurrection in Vision Transformer Unresolved cited work

Reference 80

Resolution: unresolved; raw_fallback, observed 2026-08-10T13:41:54.900197Z

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Observation d05fb27a-9529-4353-8334-e1ee393b0ec9 · outbound

This paper cites Comparison of different lower-bound Cmin when clamping the denominator of linear attention into the range [Cmin, +∞).

The Linear Attention Resurrection in Vision Transformer Comparison of different lower-bound Cmin when clamping the denominator of linear attention into the range [Cmin, +∞)

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.891804Z

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Observation 331bac28-e3f1-4998-8870-1d2d8ee6965c · outbound

This paper cites Apply our proposed enhanced linear attention on the plain ViT architectures.

The Linear Attention Resurrection in Vision Transformer Apply our proposed enhanced linear attention on the plain ViT architectures

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.883366Z

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Observation d0c83913-23af-42bb-8f40-a676136e5579 · outbound

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Reference 83

Resolution: unresolved; raw_fallback, observed 2026-08-10T13:41:54.875117Z

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Observation 6652b9b3-7183-4684-ab69-620d80f68a6c · outbound

This paper cites may not be compensated by convolution since they show global patterns instead of local patterns.

The Linear Attention Resurrection in Vision Transformer may not be compensated by convolution since they show global patterns instead of local patterns

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.866494Z

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Observation 591e8058-89b5-46bc-9230-9f434e78c76c · outbound

This paper cites Unlike the non-overlapping patchify stem in Swin [36], we adopt a two-layer convolu- tional stem to extract more important local structure infor- mation for each patch.

The Linear Attention Resurrection in Vision Transformer Unlike the non-overlapping patchify stem in Swin [36], we adopt a two-layer convolu- tional stem to extract more important local structure infor- mation for each patch

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:41:54.859414Z

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Pith citing papers

Observation 5d99074f-0a10-4a38-839c-62788197f37c · inbound

ELSA: Exact Linear-Scan Attention for Fast and Memory-Light Vision Transformers cites this paper.

ELSA: Exact Linear-Scan Attention for Fast and Memory-Light Vision Transformers The Linear Attention Resurrection in Vision Transformer

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:11:17.683547Z

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Observation cbc314fb-f538-4f79-bf58-2462437e06a6 · inbound

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Exact Linear Attention The Linear Attention Resurrection in Vision Transformer

Reference 26

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Observation 640ef7b2-4772-491a-9207-b4d7caf76afa · inbound

Exact Linear Attention cites this paper.

Exact Linear Attention The Linear Attention Resurrection in Vision Transformer

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:25:45.814038Z

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Observation 561fabb7-9448-498d-83f5-02e55a67378b · inbound

Quantum Parameterized Self-Attention Network for Image Classification cites this paper.

Quantum Parameterized Self-Attention Network for Image Classification The Linear Attention Resurrection in Vision Transformer

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:04:00.001735Z

Source-reported events for the cited work

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