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Paper Citation Record · LEDGER

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers

As of 21 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2411.09420.

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

pith.paper-citation-record.v1
2411.09420 v3

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:44:25.481054Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

54 of 54 outbound references displayed

  • verified exact0
  • verified fuzzy30
  • unresolved24
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 983f661d-3e97-47e0-a7a3-f184317555ec · outbound

This paper cites Uni4eye++: A general masked image modeling multi-modal pre-training framework for ophthalmic image classification and segmentation.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Uni4eye++: A general masked image modeling multi-modal pre-training framework for ophthalmic image classification and segmentation

Reference 1

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Source-reported events for the cited work

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Observation abb8a084-89a2-4846-90d0-3f4338afe7e0 · outbound

This paper cites End-to- end object detection with transformers, 2020.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers End-to- end object detection with transformers, 2020

Reference 2

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Source-reported events for the cited work

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Observation 25bb2213-6e22-44b4-ba4a-3bceb595725f · outbound

This paper cites Crossvit: Cross-attention multi-scale vision transformer for image classification.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Crossvit: Cross-attention multi-scale vision transformer for image classification

Reference 3

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Observation 6916e746-f155-47a7-8159-6490da88204d · outbound

This paper cites Chen et al.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Chen et al

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 7b811392-787c-4e00-99b9-6e382c5d278d · outbound

This paper cites Ef- ficient decoder-free object detection with transformers, 2022.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Ef- ficient decoder-free object detection with transformers, 2022

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation ba3d82ee-8479-4279-98bd-dfe8bfdbc474 · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 6

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Source-reported events for the cited work

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Observation 3bd97567-22d2-4d54-aa4a-d514b0957b74 · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 65fd272f-6fa3-405d-8dd2-b4966e090407 · outbound

This paper cites Remote sens- ing image scene classification: Benchmark and state of the art.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Remote sens- ing image scene classification: Benchmark and state of the art

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 3fb802e3-6cc1-4740-bfe8-1006044c1fc3 · outbound

This paper cites Dosovitskiy.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Dosovitskiy

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 284b5dbc-2b1b-49ea-9314-3cfa494d3ce7 · outbound

This paper cites Large- scale learnable graph convolutional networks.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Large- scale learnable graph convolutional networks

Reference 10

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 6776a8df-e616-4961-b19e-10f77f03bffd · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation ae6ada0c-adf6-461c-a966-ef5838e263a9 · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation bf2e751a-59f7-451a-a9a8-5e9c39d5f92d · outbound

This paper cites Deep residual learning for image recognition.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Deep residual learning for image recognition

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation de3d3995-f34f-4159-af34-df505622b63b · outbound

This paper cites Densely connected convolutional net- works.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Densely connected convolutional net- works

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 4b3203a0-c824-4e07-942a-336727bbac09 · outbound

This paper cites Polarformer: A transformer-based method for multi-lesion segmentation in intravascular oct.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Polarformer: A transformer-based method for multi-lesion segmentation in intravascular oct

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 8a31a3d3-04b4-4ff3-8e82-895c03c9b8ae · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 7151d80a-451c-41a4-a0c4-930fdde045cd · outbound

This paper cites Iandola, Song Han, Matthew W.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Iandola, Song Han, Matthew W

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 66bdffd6-b056-4528-a2c7-db0245ed8187 · outbound

This paper cites Nct-crc-he: Not all histopathological datasets are equally useful, 2024.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Nct-crc-he: Not all histopathological datasets are equally useful, 2024

Reference 18

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation f1356267-e50a-4e8a-b0d2-047beafe4255 · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 510f645e-6a30-49f3-9b07-ffd51d03285a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Adam: A Method for Stochastic Optimization

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d9d309a3-2c7a-4bcd-b833-251d0c990cac · outbound

This paper cites Learning multiple layers of features from tiny images.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Learning multiple layers of features from tiny images

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation f504d416-5f01-48cd-a6ca-b5fd13eb6242 · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation a7d3a0a8-581a-41b0-94f8-e27945567be5 · outbound

This paper cites Lee, J.S.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Lee, J.S

Reference 23

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 4e6aefaa-f1ca-4e50-8707-c9ad6f359075 · outbound

This paper cites Feature pyramid networks for object detection, 2016.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Feature pyramid networks for object detection, 2016

Reference 24

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 0b9140a9-f850-43e6-aaca-e46a8a30615f · outbound

This paper cites Feature pyramid networks for object detection, 2017.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Feature pyramid networks for object detection, 2017

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 17dd8b49-a639-4e43-846b-6b98844b625f · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 1ff58198-b241-459a-9ecb-2644b85ab323 · outbound

This paper cites Understanding the effective receptive field in deep convolu- tional neural networks.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Understanding the effective receptive field in deep convolu- tional neural networks

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation c6377d94-4a79-4413-8f8f-4f61e54d876a · outbound

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

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Shufflenet v2: Practical guidelines for efficient cnn architec- ture design

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation eb7d2557-d003-4db1-9359-a537f8112413 · outbound

This paper cites Mobilevit: Light- weight, general-purpose, and mobile-friendly vision trans- former, 2021.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Mobilevit: Light- weight, general-purpose, and mobile-friendly vision trans- former, 2021

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 2bd26d01-f3c2-4654-b909-36bb1195e151 · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 012c5f7b-1d2a-4208-a68d-da443ccada64 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 31

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no resolver link, observed 2026-08-12T20:44:25.390613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 353c0412-45c4-4210-b066-ecd068b300ba · outbound

This paper cites Vidt: An efficient and effective fully transformer-based object detector, 2021.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Vidt: An efficient and effective fully transformer-based object detector, 2021

Reference 32

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raw_fallback, observed 2026-08-12T20:44:25.819670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T20:44:25.394728Z digest=sha256:b0eeed4aead365a5e6e8b0ff62ed06488dfda053b8e685a9bf0fc4c1dcf8baef

Observation 5cd6935a-e49e-4c54-a827-5529f6925f77 · outbound

This paper cites Srinivas.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Srinivas

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T20:44:25.398622Z digest=sha256:72f2c56b517032fe200ad336a6fcbaabeb1abf535db02d9e81831f58f05aac74

Observation e7d8562b-288d-4d27-acd0-614cddcd63c6 · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 34

Resolution
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raw_fallback, observed 2026-08-12T20:44:25.793896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation ac68f6a4-c6a3-481d-9089-34c437506dbe · outbound

This paper cites Going deeper with convolutions.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Going deeper with convolutions

Reference 35

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation fcdfaf01-d5dc-40ec-98ca-323ccbc45e07 · outbound

This paper cites Rethinking the inception archi- tecture for computer vision.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Rethinking the inception archi- tecture for computer vision

Reference 36

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 191bf31a-a120-49f8-a0ab-a240e659b6ff · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 37

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raw_fallback, observed 2026-08-12T20:44:25.756288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation bfbdc98f-6979-4036-a55b-9eedd1c99970 · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-12T20:44:25.744036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 77cc4263-5a40-49c7-9126-c259d3769c52 · outbound

This paper cites Category feature transformer for semantic segmentation, 2023.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Category feature transformer for semantic segmentation, 2023

Reference 39

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation fc9eb6a1-0524-4372-a5d9-811258f72609 · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-12T20:44:25.718967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation ded668ac-8857-488f-8c94-104d28beef4e · outbound

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

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Training data-efficient image transformers & distillation through attention

Reference 41

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e0f784c9-7cd7-45a1-b891-ccf486bd79ec · outbound

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

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Training data-efficient image transformers & distillation through at- tention

Reference 42

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation e269bf1a-accd-41c6-9707-e3547ab8bb2a · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 43

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raw_fallback, observed 2026-08-12T20:44:25.693297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 5e3bf1af-d61d-49a7-a3d0-41a61f3b4139 · outbound

This paper cites Graph at- tention networks.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Graph at- tention networks

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-12T20:44:25.680966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation f455ac74-9552-4c81-8c8e-b0509a7abb85 · outbound

This paper cites Deep learning inno- vations for underwater waste detection: An in-depth analy- sis, 2024.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Deep learning inno- vations for underwater waste detection: An in-depth analy- sis, 2024

Reference 45

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verified fuzzy
raw_fallback, observed 2026-08-12T20:44:25.667387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 40a6bbc7-eca8-4b39-ade2-c3d103d50d3e · outbound

This paper cites Optimized custom dataset for efficient detection of underwater trash.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Optimized custom dataset for efficient detection of underwater trash

Reference 46

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raw_fallback, observed 2026-08-12T20:44:25.653065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T20:44:25.449323Z digest=sha256:9c55aa4bad5cbdce7db092ca00d9f02a194e591cf0bf71e4966588155632b975

Observation a3fc2678-ce7e-480e-ba2d-bbb280fb8653 · outbound

This paper cites Non-local neural networks.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Non-local neural networks

Reference 47

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raw_fallback, observed 2026-08-12T20:44:25.640056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation c2f10580-6b6b-4c86-8e8a-fc19a43ecc41 · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 48

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unresolved
raw_fallback, observed 2026-08-12T20:44:25.627049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T20:44:25.457085Z digest=sha256:72ac81378152537ce9ecd33633efc59ce3fcd74b5c2e63b934b0c1def8949f13

Observation 2d9f27ba-9c6e-45db-93f7-89d2b453595b · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 49

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unresolved
raw_fallback, observed 2026-08-12T20:44:25.613319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 85f723d8-0bbe-450a-b710-318ecef25471 · outbound

This paper cites Multi-label chest x-ray image classification with single positive labels.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Multi-label chest x-ray image classification with single positive labels

Reference 50

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verified fuzzy
raw_fallback, observed 2026-08-12T20:44:25.600506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation bf1b2f89-7be9-4c78-935d-5bc9aa34d2a6 · outbound

This paper cites Alvarez, and Ping Luo.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Alvarez, and Ping Luo

Reference 51

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unresolved
no resolver link, observed 2026-08-12T20:44:25.468727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:44:25.468727Z digest=sha256:21a4f8c632196da5d6dfabf5f69d5a2da5fd4c8414e640fd141d98e2cb39a339

Observation 9a9c5e20-e47a-4bdb-8ce5-e7a878ef949a · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 52

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raw_fallback, observed 2026-08-12T20:44:25.578708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation c787f9c4-9aff-434a-9eb9-4fbd542d710a · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 53

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unresolved
raw_fallback, observed 2026-08-12T20:44:25.565578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T20:44:25.477122Z digest=sha256:1f2ecc0c8a4714209095191fe3f87c215c07ef23b70d60f9372b49cdcb299092

Observation 23a0b415-a994-4b99-8482-ccfe209a0a08 · outbound

This paper cites an unresolved cited work.

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers Unresolved cited work

Reference 54

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unresolved
raw_fallback, observed 2026-08-12T20:44:25.552606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Pith citing papers

No inbound Pith citation observations are available.