Paper Citation Record · LEDGER

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers

As of 7 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2405.13901.

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

pith.paper-citation-record.v1
2405.13901 v6

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T00:33:22.959973Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T18:47:08.624927Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T02:59:25.081929Z

Reference resolution

45 of 45 outbound references displayed

verified exact4
verified fuzzy41
unresolved0
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 40499097-381c-4585-b35e-ecd8d49e2f8d · outbound

This paper cites Discrete cosine transform.IEEE trans- actions on Computers, 100(1):90–93.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Discrete cosine transform.IEEE trans- actions on Computers, 100(1):90–93

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.327852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8590d013-797f-4cbb-983a-5126317f8100 · outbound

This paper cites Toeplitz approximation to empirical correlation matrix of asset returns: A signal processing perspective.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Toeplitz approximation to empirical correlation matrix of asset returns: A signal processing perspective

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.331232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d6be680d-ff84-4972-9918-c4cafe022bf0 · outbound

This paper cites Hydra at- tention: Efficient attention with many heads.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Hydra at- tention: Efficient attention with many heads

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.324164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1075b532-aa99-4077-a50b-6c5c0b122698 · outbound

This paper cites Reminder of the first paper on transfer learning in neural networks, 1976.Infor- matica, 44(3).

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Reminder of the first paper on transfer learning in neural networks, 1976.Infor- matica, 44(3)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.343765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7952c76c-5090-4a21-b4ec-46076a4d465e · outbound

This paper cites Fourier image transformer.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Fourier image transformer

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.339423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fd0b0c57-6421-40a6-b0fe-3ff4faf6e0f5 · outbound

This paper cites Cascade r-cnn: Delving into high quality object detection.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Cascade r-cnn: Delving into high quality object detection

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.350692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 75d80bae-096f-4d5c-8da5-ea5c0f9c72c5 · outbound

This paper cites A fast computational algorithm for the discrete cosine transform.IEEE Transactions on communications, 25(9):1004–1009.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers A fast computational algorithm for the discrete cosine transform.IEEE Transactions on communications, 25(9):1004–1009

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.260426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b06833dc-335f-41eb-a007-20637a68b189 · outbound

This paper cites Fast fourier convolution.Advances in Neural Information Pro- cessing Systems, 33:4479–4488.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Fast fourier convolution.Advances in Neural Information Pro- cessing Systems, 33:4479–4488

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.256079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 07247c1a-2adb-49ef-8066-9bce04c12de1 · outbound

This paper cites AutoAugment: Learning Augmentation Policies from Data.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers AutoAugment: Learning Augmentation Policies from Data

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-24T00:33:39.799856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cdc9412d-8799-4a87-970f-cce60d2708ed · outbound

This paper cites Karhunen-loeve transform.The transform and data compression hand- book, 1(1-34):29.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Karhunen-loeve transform.The transform and data compression hand- book, 1(1-34):29

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.334977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f5a23c9f-0811-4905-bb57-a57c968235a1 · outbound

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

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers An image is worth 16x16 words: Transformers for image recognition at scale

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.220075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a61941f5-d72d-4ff2-a209-d3785308c6a6 · outbound

This paper cites Mpeg-4 natural video coding–an overview.Sig- nal Processing: Image Communication, 15(4-5):365–385.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Mpeg-4 natural video coding–an overview.Sig- nal Processing: Image Communication, 15(4-5):365–385

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.236845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 560bdd26-4296-4ead-a7b0-5d43cf48a013 · outbound

This paper cites Understanding the difficulty of training deep feedfor- ward neural networks.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Understanding the difficulty of training deep feedfor- ward neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.194833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4927b0bf-7205-4f44-a666-6a1fa8504f81 · outbound

This paper cites gswin: Gated mlp vision model with hi- erarchical structure of shifted window.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers gswin: Gated mlp vision model with hi- erarchical structure of shifted window

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.282055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0235cdba-e0be-4426-baf3-73f4b4e00c67 · outbound

This paper cites Delving deep into rectifiers: Surpass- ing human-level performance on imagenet classification.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Delving deep into rectifiers: Surpass- ing human-level performance on imagenet classification

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.215502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0fb722c1-92b2-42bb-b6ea-d3b57568694b · outbound

This paper cites Deep residual learning for image recog- nition.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Deep residual learning for image recog- nition

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.285700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 40ec8ce1-c27a-44ed-a10a-0a738711c100 · outbound

This paper cites Colorformer: Image colorization via color memory assisted hybrid-attention transformer.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Colorformer: Image colorization via color memory assisted hybrid-attention transformer

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.292766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2db5d130-9286-4e29-80d4-29cea069a97a · outbound

This paper cites Discrete cosin transformer: Im- age modeling from frequency domain.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Discrete cosin transformer: Im- age modeling from frequency domain

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.268110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 083c1f5b-5a2d-4fa6-b976-e32f46058504 · outbound

This paper cites Microsoft coco: Com- mon objects in context.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Microsoft coco: Com- mon objects in context

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.264411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7912a83f-590e-4801-9175-212c2e222cab · outbound

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

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Swin transformer: Hierarchical vision transformer using shifted windows

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.278657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6f92e9dd-a337-4698-8bf5-d1d33646c5e2 · outbound

This paper cites Deep learn- ing via hessian-free optimization.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Deep learn- ing via hessian-free optimization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.224595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a033a052-bb58-417f-ac50-f5f3f99dde66 · outbound

This paper cites Training neural network with zero weight ini- tialization.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Training neural network with zero weight ini- tialization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.271809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5cabb2c0-6808-48ca-a405-9c6d1f8f4b0e · outbound

This paper cites Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.244664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9561427b-1f5e-4947-bbef-15ea58b6295b · outbound

This paper cites A hybrid quantum-classical approach based on the hadamard transform for the convolutional layer.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers A hybrid quantum-classical approach based on the hadamard transform for the convolutional layer

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.228954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6f47854e-0fba-40c2-8874-c72b8bbaf807 · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library.Advances in neural in- formation processing systems, 32.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Pytorch: An imperative style, high- performance deep learning library.Advances in neural in- formation processing systems, 32

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.188934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1698a8a1-8006-4857-ae5b-bde3ba9e581b · outbound

This paper cites Spectformer: Frequency and atten- tion is what you need in a vision transformer.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Spectformer: Frequency and atten- tion is what you need in a vision transformer

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.296517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation eca3ff06-2bac-4802-85d8-eddb46eab583 · outbound

This paper cites Low-complexity rounded klt approx- imation for image compression.Journal of Real-Time Im- age Processing, pages 1–11.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Low-complexity rounded klt approx- imation for image compression.Journal of Real-Time Im- age Processing, pages 1–11

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.301071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8545fb89-dab4-4fd6-b328-5d9f748efe58 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.211422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 17f8aa6e-2386-4178-a1e0-d02e5f016e7e · outbound

This paper cites Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-24T00:33:39.794169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7ac4414b-711f-495d-a034-a6c6095ca088 · outbound

This paper cites Dct-former: Ef- ficient self-attention with discrete cosine transform.Jour- nal of Scientific Computing, 94(3):67.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Dct-former: Ef- ficient self-attention with discrete cosine transform.Jour- nal of Scientific Computing, 94(3):67

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.312393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b5fe1205-3982-4af8-b72a-5f5388451c9a · outbound

This paper cites Mi- crovit: a vision transformer with low complexity self atten- tion for edge device.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Mi- crovit: a vision transformer with low complexity self atten- tion for edge device

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.240791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f8771983-2c38-46b9-a645-f0c9b996caca · outbound

This paper cites Efficient attention: Attention with linear complexities.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Efficient attention: Attention with linear complexities

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.304770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 747a6959-6b51-44de-a046-5fb60cbff418 · outbound

This paper cites Compressive estimation and imaging based on autoregres- sive models.IEEE Transactions on Image Processing, 25(11):5077–5087.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Compressive estimation and imaging based on autoregres- sive models.IEEE Transactions on Image Processing, 25(11):5077–5087

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.308552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 42993867-c768-4e6f-95b0-09f70b137391 · outbound

This paper cites Mimetic Initialization of Self-Attention Layers.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Mimetic Initialization of Self-Attention Layers

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-24T00:33:39.812038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b6932c60-e759-43dc-8a50-65e352b963b1 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Attention is all you need.Advances in neural information processing systems, 30

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.248673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d28f8729-9d75-405e-8dcc-2bb9736737f4 · outbound

This paper cites The jpeg still pic- ture compression standard.Communications of the ACM, 34(4):30–44.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers The jpeg still pic- ture compression standard.Communications of the ACM, 34(4):30–44

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.252462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a250b8f7-28b3-4f14-bfa9-e4a88c371750 · outbound

This paper cites A survey of transfer learning.Journal of Big data, 3(1):1–40.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers A survey of transfer learning.Journal of Big data, 3(1):1–40

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.207462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ae2535d0-ab10-4b62-a65c-b9fce348a3b8 · outbound

This paper cites Initializing models with larger ones.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Initializing models with larger ones

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.316585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5ee20aaf-96ff-48ad-b185-c4d065979262 · outbound

This paper cites Metaformer is actually what you need for vision.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Metaformer is actually what you need for vision

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.203138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 67816cd1-f0e1-41f6-bf57-06e1bbdc102e · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Cutmix: Regularization strategy to train strong classifiers with localizable features

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.320410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fa05e2ca-f7c2-4ebb-9742-a76eed9166e7 · outbound

This paper cites mixup: Beyond empirical risk minimization.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers mixup: Beyond empirical risk minimization

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.275359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1b10d02c-549c-4af7-8803-8cafd1443e82 · outbound

This paper cites Improving deep transformer with depth-scaled ini- tialization and merged attention.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Improving deep transformer with depth-scaled ini- tialization and merged attention

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.199064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation aa821ee9-b734-45db-8d0b-e79c561db64d · outbound

This paper cites Zero initialization: Initializing neural networks with only zeros and ones.Transactions on Machine Learning Research.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Zero initialization: Initializing neural networks with only zeros and ones.Transactions on Machine Learning Research

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.289357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ea854e97-1489-4080-a830-d3a43e804b1a · outbound

This paper cites Structured Initialization for Attention in Vision Transformers.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Structured Initialization for Attention in Vision Transformers

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-24T00:33:39.806228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a978fd43-613c-4159-9759-d1a3fdf1ba5c · outbound

This paper cites Random erasing data aug- mentation.

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Random erasing data aug- mentation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T00:33:40.233032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

Observation cc143847-4546-48da-b7d3-cbe108ad72bd · inbound

FrequencyFormer: A Co-Designed Sensor-to-Processor Pipeline for Frequency-Domain Vision Transformer Inference cites this paper.

FrequencyFormer: A Co-Designed Sensor-to-Processor Pipeline for Frequency-Domain Vision Transformer Inference Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-07-04T02:59:25.083890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.