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Paper Citation Record · LEDGER

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation

As of 14 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2506.04956.

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

pith.paper-citation-record.v1
2506.04956 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:36:27.255445Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:19:50.621480Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T04:19:50.995387Z

Reference resolution

36 of 36 outbound references displayed

  • verified exact1
  • verified fuzzy8
  • unresolved26
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d2007dcb-1f0c-47c6-a9f5-85640c8c89b6 · outbound

This paper cites In: Medical imaging with deep learning (2022).

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: Medical imaging with deep learning (2022)

Reference 1

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Observation 231ac131-54a1-44b9-a4d8-d25535299536 · outbound

This paper cites Soft Masked Mamba Diffusion Model for CT to MRI Conversion.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Soft Masked Mamba Diffusion Model for CT to MRI Conversion

Reference 2

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local_arxiv, observed 2026-08-07T10:36:27.906146Z

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Observation 6a01f387-c901-499f-955a-d9d6210e6372 · outbound

This paper cites In: Proceedings of the IEEE/CVF International conference on com- puter vision.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: Proceedings of the IEEE/CVF International conference on com- puter vision

Reference 3

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Observation 84702ea3-a463-4236-88ec-a66b1d05dd01 · outbound

This paper cites Make-A-Video: Text-to-Video Generation without Text-Video Data.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 4

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Observation 1a1e503b-26c6-43dc-94cb-60358803c625 · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 5

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Observation b0c0a9cd-6c9c-4937-97de-111d6927d458 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 6

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Observation 922b54a9-d27a-46ad-9900-8f4807847535 · outbound

This paper cites In: International Conference on Medical Image Computing and Computer-Assisted Intervention.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: International Conference on Medical Image Computing and Computer-Assisted Intervention

Reference 7

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Observation 0048ddc4-7e66-4d03-afc2-c81ab2468a00 · outbound

This paper cites IEEE Transactions on Visualization and Computer Graphics (2024).

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation IEEE Transactions on Visualization and Computer Graphics (2024)

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation a9d67d3d-ddc1-4813-96a8-41f079085492 · outbound

This paper cites Latte: Latent Diffusion Transformer for Video Generation.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Latte: Latent Diffusion Transformer for Video Generation

Reference 9

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Observation b50e6d53-4392-46de-9e49-fa8c8d44875c · outbound

This paper cites EndoMamba: An Efficient Foundation Model for Endoscopic Videos via Hierarchical Pre-training.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation EndoMamba: An Efficient Foundation Model for Endoscopic Videos via Hierarchical Pre-training

Reference 10

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Observation ca646ca0-3543-45d9-b28e-9158b8e29eac · outbound

This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 11

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Observation 4490631e-230b-4427-aae5-4c62f8609089 · outbound

This paper cites an unresolved cited work.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Unresolved cited work

Reference 12

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Observation 944dd719-6555-42c0-b52d-b20d0a581ee6 · outbound

This paper cites Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generation.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generation

Reference 13

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Observation b196f34d-df83-4752-b384-10cc033debd4 · outbound

This paper cites In: Proceedings of the IEEE/CVF international conference on computer vision.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: Proceedings of the IEEE/CVF international conference on computer vision

Reference 14

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Observation d22d5156-ac05-4c13-bea9-e625a49bd6c7 · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation RWKV: Reinventing RNNs for the Transformer Era

Reference 15

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Observation 149c0852-2597-48b8-bab9-ad2485051fbf · outbound

This paper cites Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence

Reference 16

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Observation 08676b96-f7fe-4d4a-9cc5-f332545801fc · outbound

This paper cites Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures

Reference 17

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Observation 7bb4cabc-a85d-4041-871d-dfddcf6f4f36 · outbound

This paper cites Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV

Reference 18

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Observation 4170128b-a325-4877-912f-9805e5eddf80 · outbound

This paper cites In: Proceedings of the IEEE/CVF winter conference on appli- cations of computer vision.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: Proceedings of the IEEE/CVF winter conference on appli- cations of computer vision

Reference 19

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Observation 350df687-ca1a-4162-863a-1e3f5e1b359a · outbound

This paper cites Advances in neural information processing systems34, 21696–21707 (2021).

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Advances in neural information processing systems34, 21696–21707 (2021)

Reference 20

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Observation c58c6d5e-8cdc-48ba-8011-422212bf41d4 · outbound

This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 21

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Observation 94fbcc4d-e7cb-492e-8e7e-786db8c0d191 · outbound

This paper cites Latent Video Diffusion Models for High-Fidelity Long Video Generation.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Latent Video Diffusion Models for High-Fidelity Long Video Generation

Reference 22

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Observation 665d19e4-e965-4428-b1d9-4fab5ca2f35a · outbound

This paper cites In: Proceedings of the IEEE conference on computer vision and pattern recognition.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 23

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Observation 55058e80-ee24-4e04-8b99-f1f6d20469a9 · outbound

This paper cites Value Residual Learning.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Value Residual Learning

Reference 24

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Observation 01dd9f24-ee92-4631-8333-a8dc618d49d5 · outbound

This paper cites IEEE transactions on medical imaging35(9), 2051–2063 (2016).

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation IEEE transactions on medical imaging35(9), 2051–2063 (2016)

Reference 25

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Observation 2df36040-8c80-46f9-9aa8-b9ba61b16461 · outbound

This paper cites Sci- entific data7(1), 283 (2020).

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Sci- entific data7(1), 283 (2020)

Reference 26

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Observation 1fa5c24d-4dc6-44a6-927a-bf9867187fc8 · outbound

This paper cites On Aliased Resizing and Surprising Subtleties in GAN Evaluation.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation On Aliased Resizing and Surprising Subtleties in GAN Evaluation

Reference 27

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Observation 7a66b3da-6998-47ac-85a5-4e40375e5302 · outbound

This paper cites In: Proceedings of the IEEE international conference on computer vision.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: Proceedings of the IEEE international conference on computer vision

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 924dad97-db16-463e-9ff5-102f10a5967c · outbound

This paper cites Towards Accurate Generative Models of Video: A New Metric & Challenges.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Towards Accurate Generative Models of Video: A New Metric & Challenges

Reference 29

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Observation f254769f-2d01-4654-82d3-4f419d387fc7 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 30

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Observation e468b475-5acd-4564-806e-ea2564a06ff8 · outbound

This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 31

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Observation 073f41c8-b85b-4f5d-ae90-f48bc0bad5ee · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 32

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Observation 0a0adfaf-cba0-4e71-8451-ff8bdfed2e20 · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 33

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Observation 33bbb015-4cd8-489f-bcce-f4439bc0f871 · outbound

This paper cites In: 2021 IEEE International Conference on Image Pro- cessing (ICIP).

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: 2021 IEEE International Conference on Image Pro- cessing (ICIP)

Reference 34

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raw_fallback, observed 2026-08-07T10:36:28.228520Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 2c617893-1d41-4073-a689-bf88f956ffb8 · outbound

This paper cites In: International Conference on Medical Image Computing and Computer-Assisted Intervention.

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation In: International Conference on Medical Image Computing and Computer-Assisted Intervention

Reference 35

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raw_fallback, observed 2026-08-07T10:36:28.075638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 7732d5a6-680a-4e96-9e85-f781abf52350 · outbound

This paper cites Advances in neural information processing systems33, 596–608 (2020).

FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation Advances in neural information processing systems33, 596–608 (2020)

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:36:27.255445Z digest=sha256:ccb51f744b3eae5eb666a3256de7d7a869349790e90d7ec655056e5c91c7b0eb

Pith citing papers

Observation 27ed9b6c-2b8b-46ee-a3d6-268d108c01b0 · inbound

Med-URWKV{\dag}: Toward Enhanced Pretrained Pure VRWKV Models for Medical Image Segmentation cites this paper.

Med-URWKV{\dag}: Toward Enhanced Pretrained Pure VRWKV Models for Medical Image Segmentation FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation

Reference 52

Resolution
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local_arxiv, observed 2026-08-07T04:19:51.002178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T04:19:50.621480Z digest=sha256:19a2dc0ff15483e0deff9a35c214f1e615b80f02e713d99304c2d68d919acbcb