Paper Citation Record · LEDGER

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion

As of 20 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2505.14719.

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

pith.paper-citation-record.v1
2505.14719 v3

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:23:09.336104Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T00:50:47.085579Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T00:51:14.493325Z

Reference resolution

41 of 41 outbound references displayed

verified exact0
verified fuzzy27
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 055a1a60-7c59-4a4e-90ef-f74e1c5747dc · outbound

This paper cites A low power, fully event- based gesture recognition system.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion A low power, fully event- based gesture recognition system

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.838136Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f91eadcf-db24-4bcc-abe8-1fe8d24ddeec · outbound

This paper cites Is space-time attention all you need for video understanding? InICML, volume 2, page 4,.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Is space-time attention all you need for video understanding? InICML, volume 2, page 4,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:23:09.192859Z

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Observation 82b31e7f-0f3f-4671-ad32-ec650e83cb13 · outbound

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

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T20:23:09.213387Z

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Observation 2a06fc71-c453-4ede-9ed4-03907a59d312 · outbound

This paper cites Multiscale vision transformers.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Multiscale vision transformers

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.774166Z

Source-reported events for the cited work

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Observation 6e42b34a-d4f6-469e-98c4-11a415b82091 · outbound

This paper cites Levit: a vision trans- former in convnet’s clothing for faster inference.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Levit: a vision trans- former in convnet’s clothing for faster inference

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.761000Z

Source-reported events for the cited work

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Observation eb467d04-bda1-422e-9f23-9133eea834d1 · outbound

This paper cites Multi-scale self- attention for text classification.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Multi-scale self- attention for text classification

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.748948Z

Source-reported events for the cited work

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Observation 47be100a-24ed-4acc-8d7c-9a50c4337170 · outbound

This paper cites Pct: Point cloud transformer.Computational Visual Me- dia, 7:187–199,.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Pct: Point cloud transformer.Computational Visual Me- dia, 7:187–199,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.736387Z

Source-reported events for the cited work

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Observation a653f433-0b03-404e-a187-ac83872a7c6d · outbound

This paper cites Masked au- toencoders are scalable vision learners.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Masked au- toencoders are scalable vision learners

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T20:23:09.231770Z

Source-reported events for the cited work

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Observation 24a82edf-4152-4fbb-883f-242e0d29371f · outbound

This paper cites Cifar10-dvs: an event-stream dataset for object classification.Frontiers in neuroscience, 11:309,.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Cifar10-dvs: an event-stream dataset for object classification.Frontiers in neuroscience, 11:309,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.659181Z

Source-reported events for the cited work

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Observation 5eb0a6fd-c583-4a95-8ba9-13553a41f727 · outbound

This paper cites Rethinking vision transformers for mo- bilenet size and speed.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Rethinking vision transformers for mo- bilenet size and speed

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.646250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e06f31f0-d303-4590-8bac-9be4499409d0 · outbound

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

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Swin transformer: Hierarchical vision transformer using shifted windows

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:23:09.258689Z

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Observation 760b0a38-0b72-495a-8a29-0732577518a6 · outbound

This paper cites Ecoformer: Energy-saving atten- tion with linear complexity.Advances in Neural Informa- tion Processing Systems, 35:10295–10308,.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Ecoformer: Energy-saving atten- tion with linear complexity.Advances in Neural Informa- tion Processing Systems, 35:10295–10308,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.625832Z

Source-reported events for the cited work

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Observation 317dd609-931e-4718-9b71-c064430a2a86 · outbound

This paper cites Networks of spiking neu- rons: the third generation of neural network models.Neu- ral networks, 10(9):1659–1671,.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Networks of spiking neu- rons: the third generation of neural network models.Neu- ral networks, 10(9):1659–1671,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T20:23:09.266700Z

Source-reported events for the cited work

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Observation ff0a2e9c-ce3a-404f-9965-67e96b685a11 · outbound

This paper cites Image super-resolution with non-local sparse attention.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Image super-resolution with non-local sparse attention

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.604396Z

Source-reported events for the cited work

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Observation 717e5e5f-f7ce-4837-afd1-a153e6138b25 · outbound

This paper cites Transformers for image recognition at scale.On- line: https://ai.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Transformers for image recognition at scale.On- line: https://ai

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.591633Z

Source-reported events for the cited work

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Observation 9d1af8ec-b18a-454a-ad19-da57e4d1c125 · outbound

This paper cites X-linear attention networks for image captioning.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion X-linear attention networks for image captioning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.576445Z

Source-reported events for the cited work

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Observation a16d5591-2f9b-4db3-95cc-40a8f436e322 · outbound

This paper cites SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T20:23:09.286245Z

Source-reported events for the cited work

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Observation 22efe85b-d1ad-4f78-8444-12bad81a2582 · outbound

This paper cites Towards spike-based machine intelligence with neuromorphic computing.Nature, 575(7784):607–617,.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Towards spike-based machine intelligence with neuromorphic computing.Nature, 575(7784):607–617,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.550838Z

Source-reported events for the cited work

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Observation 2a5be4e0-bbc9-4cd3-ab49-9eda05a98eb8 · outbound

This paper cites Spikingresformer: Bridging resnet and vision trans- former in spiking neural networks.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Spikingresformer: Bridging resnet and vision trans- former in spiking neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.537634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e7068879-3fe9-414d-8d2f-fd4a98ca8fe8 · outbound

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

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Attention is all you need.Advances in neural information processing systems, 30,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T20:23:09.305395Z

Source-reported events for the cited work

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Observation 5ed70b70-dd3e-457f-a313-832a90aacfbd · outbound

This paper cites Pyramid vision transformer: A ver- satile backbone for dense prediction without convolutions.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Pyramid vision transformer: A ver- satile backbone for dense prediction without convolutions

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.499317Z

Source-reported events for the cited work

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Observation 28590d8a-6cf0-4be8-ad6a-afcd6459e260 · outbound

This paper cites Generalisation of structural knowledge in the hippocampal-entorhinal system.Advances in neural information processing systems, 31,.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Generalisation of structural knowledge in the hippocampal-entorhinal system.Advances in neural information processing systems, 31,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.486265Z

Source-reported events for the cited work

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Observation 1888c2df-9c17-44b5-a80b-ba4407615277 · outbound

This paper cites Attention Spiking Neural Networks.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Attention Spiking Neural Networks

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T20:23:09.316585Z

Source-reported events for the cited work

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Observation 7175f1aa-8d59-43ef-b8d6-b103f3698797 · outbound

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

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Metaformer is actually what you need for vision

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T20:23:09.320558Z

Source-reported events for the cited work

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Observation d9dbcf24-ca8a-4e78-b181-235970edee3a · outbound

This paper cites Spiking transformers for event-based single object track- ing.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Spiking transformers for event-based single object track- ing

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.465859Z

Source-reported events for the cited work

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Observation 41fa3197-88e7-4dc5-9a04-3c05f38ea3cf · outbound

This paper cites Random erasing data aug- mentation.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Random erasing data aug- mentation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.453576Z

Source-reported events for the cited work

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Observation d66c3ca4-6f24-41db-a28d-7fc24354b1a1 · outbound

This paper cites Spikformer: When spiking neural network meets transformer.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Spikformer: When spiking neural network meets transformer

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.442312Z

Source-reported events for the cited work

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Observation df0b0859-678a-4997-986e-6cd4bf779652 · outbound

This paper cites Spikformer v2: Join the high accuracy club on imagenet with an snn ticket.CoRR, 2024.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Spikformer v2: Join the high accuracy club on imagenet with an snn ticket.CoRR, 2024

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.429115Z

Source-reported events for the cited work

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Observation 7646cdf5-e061-4259-a0b8-adde97523d99 · outbound

This paper cites Learning multiple layers of features from tiny im- ages.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Learning multiple layers of features from tiny im- ages

Reference 1984

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Observation dfc1d53a-80b4-457b-ac64-632926869353 · outbound

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

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 1997

Resolution: unresolved; no resolver link, observed 2026-08-15T20:23:09.270653Z

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Observation d313e065-4041-4693-844e-08402961867d · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-15T20:23:09.209654Z

Source-reported events for the cited work

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Observation b733114b-7a15-4f06-a77f-7521396058c0 · outbound

This paper cites Deep networks with stochastic depth.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Deep networks with stochastic depth

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.706322Z

Source-reported events for the cited work

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Observation b160c551-de3c-4832-b145-f31ad2089c3c · outbound

This paper cites Fact: Factor-tuning for lightweight adaptation on vision trans- former.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Fact: Factor-tuning for lightweight adaptation on vision trans- former

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.693739Z

Source-reported events for the cited work

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Observation 40ed87a2-7c6e-402e-83ce-3e73aa0f7587 · outbound

This paper cites Segnet: A deep convolu- tional encoder-decoder architecture for image segmenta- tion.IEEE transactions on pattern analysis and machine intelligence, 39(12):2481–2495,.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Segnet: A deep convolu- tional encoder-decoder architecture for image segmenta- tion.IEEE transactions on pattern analysis and machine intelligence, 39(12):2481–2495,

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.825552Z

Source-reported events for the cited work

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Observation 3c64f53f-79e4-4858-bac1-daf678ec69a6 · outbound

This paper cites Randaugment: Practical auto- mated data augmentation with a reduced search space.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Randaugment: Practical auto- mated data augmentation with a reduced search space

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.793471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3712a900-6904-42fb-b566-0038984193fa · outbound

This paper cites Towards artificial general intelligence with hybrid tianjic chip architecture.Nature, 572(7767):106–111,.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Towards artificial general intelligence with hybrid tianjic chip architecture.Nature, 572(7767):106–111,

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.563412Z

Source-reported events for the cited work

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Observation 031bcd45-df6b-42e2-b133-ac6d541fb24e · outbound

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

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Imagenet: A large-scale hierarchical image database

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T20:23:09.205508Z

Source-reported events for the cited work

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Observation 33591354-bc7f-4203-a1da-f695a847f20d · outbound

This paper cites Encoder-decoder with atrous separable convolution for se- mantic image segmentation.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Encoder-decoder with atrous separable convolution for se- mantic image segmentation

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.805635Z

Source-reported events for the cited work

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Observation 8600743d-36a3-4327-bff3-92b08c881f78 · outbound

This paper cites 1.1 computing’s energy problem (and what we can do about it).

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion 1.1 computing’s energy problem (and what we can do about it)

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T20:23:09.235181Z

Source-reported events for the cited work

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Observation ce156112-a99c-4fda-83a4-a70072a16501 · outbound

This paper cites The structure of im- ages.Biological cybernetics, 50(5):363–370,.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion The structure of im- ages.Biological cybernetics, 50(5):363–370,

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.681102Z

Source-reported events for the cited work

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Observation 01548185-13d6-4997-a3ae-1f1ff1bc915a · outbound

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

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Training data-efficient image transformers & distillation through attention

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:23:09.523036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation dd57ffb9-535e-4e4b-896b-b118f7a9b997 · inbound

SAFformer:Improving Spiking Transformer via Active Predictive Filtering cites this paper.

SAFformer:Improving Spiking Transformer via Active Predictive Filtering MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-12T00:51:14.495435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.