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Paper Citation Record · LEDGER

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation

As of 15 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2509.04669.

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

pith.paper-citation-record.v1
2509.04669 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:58:39.562084Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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 exact2
  • verified fuzzy29
  • unresolved23
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1ee1ba64-e4a6-4466-9dac-41466871343a · outbound

This paper cites Layer Normalization.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Layer Normalization

Reference 1

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Observation ae4f6a31-1ae4-43c9-9c24-2adc8e095960 · outbound

This paper cites Mobile- former: Bridging mobilenet and transformer.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Mobile- former: Bridging mobilenet and transformer

Reference 2

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 76c4b244-1078-472b-a522-23b7aea4cc59 · outbound

This paper cites PTQ4VM: Post-Training Quantization for Visual Mamba.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation PTQ4VM: Post-Training Quantization for Visual Mamba

Reference 3

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local_arxiv, observed 2026-08-05T05:58:40.113725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 1122c7cc-99f5-472d-81a6-660fa885fa21 · outbound

This paper cites Randaugment: Practical automated data augmen- tation with a reduced search space.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Randaugment: Practical automated data augmen- tation with a reduced search space

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:34.853319Z digest=sha256:e27d96ee1b44f46da30b9077616f369e799a42ffa7304754733f4242e6f67175

Observation 15d565b8-94a3-4d3c-952b-c6a1d31c9aca · outbound

This paper cites Coatnet: Marrying convolution and attention for all data sizes.Advances in neural information processing systems, 34:3965–3977, 2021.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Coatnet: Marrying convolution and attention for all data sizes.Advances in neural information processing systems, 34:3965–3977, 2021

Reference 5

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 4617beef-9611-4c1c-82a2-34ecf34fc26a · outbound

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

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Imagenet: A large-scale hierarchical image database

Reference 6

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raw_fallback, observed 2026-08-05T05:58:45.752283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:35.039319Z digest=sha256:9adae571b3f893e4437e697c5c5e27f1aa08110e213790e66235d3cc267ae822

Observation f8477be4-2b23-4b5a-9dd1-7fca5800c8cc · outbound

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

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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source=pdf_text observed=2026-08-05T05:58:35.117604Z digest=sha256:89fb355fbf749cc971e24f698151e9ac761fbd5b8c84e286fead11ec38117750

Observation 44168f73-83a5-4f78-8313-984784ef2f27 · outbound

This paper cites Sigmoid- weighted linear units for neural network function approxima- tion in reinforcement learning.Neural networks, 107:3–11,.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Sigmoid- weighted linear units for neural network function approxima- tion in reinforcement learning.Neural networks, 107:3–11,

Reference 8

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source=pdf_text observed=2026-08-05T05:58:35.238441Z digest=sha256:0928460686e4c335e440e7609288a5c4dcc744872f417c80acc1601a5cfee859

Observation 65e70e26-09fb-46d2-b757-ca06bbbd20b2 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 9

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source=pdf_text observed=2026-08-05T05:58:35.316363Z digest=sha256:76eba06ce6d759677abd2016fe31af1267609d46373fb7ca28cb99891f762db1

Observation 9725620b-995b-48be-869f-38d94ba74a99 · outbound

This paper cites QMamba: On First Exploration of Vision Mamba for Image Quality Assessment.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation QMamba: On First Exploration of Vision Mamba for Image Quality Assessment

Reference 10

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source=pdf_text observed=2026-08-05T05:58:35.480046Z digest=sha256:3d7c052b5cdeccca889b5e5e4939facc41a6923356285f48786dc64bcbc04cee

Observation d136d334-c730-4244-8e58-e34f87b8bbd8 · outbound

This paper cites Demystify Mamba in Vision: A Linear Attention Perspective.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Demystify Mamba in Vision: A Linear Attention Perspective

Reference 11

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source=pdf_text observed=2026-08-05T05:58:35.566295Z digest=sha256:d6f16b0f614cdbcd992a285c3ace639ce7f0f4adb4f1372f67b92c99fc66c9b5

Observation 70888f7e-560d-4128-bcb0-6aaff6ea2916 · outbound

This paper cites Vision GNN: An Image is Worth Graph of Nodes.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Vision GNN: An Image is Worth Graph of Nodes

Reference 12

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source=pdf_text observed=2026-08-05T05:58:35.664094Z digest=sha256:5cb522b049ae64ea02294e9af441065a9b563e2ea070e6d964aae05a8b784ee3

Observation b453b477-7b23-486b-9b93-a74f861f585d · outbound

This paper cites MambaVision: A Hybrid Mamba-Transformer Vision Backbone.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation MambaVision: A Hybrid Mamba-Transformer Vision Backbone

Reference 13

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source=pdf_text observed=2026-08-05T05:58:35.764365Z digest=sha256:1c7b7c7fac61c1c2f4969cb51c412f7660842a2d74df496ae5f025af09e4e702

Observation 0654394c-ec1e-4e87-82c0-c1afa0c574c1 · outbound

This paper cites Deep residual learning for image recognition.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Deep residual learning for image recognition

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:35.860038Z digest=sha256:54e1a38070f02a9163716049bc532a43b3e8924e63830b26a77b4b43d5ad84a2

Observation e3f9b8ce-6014-4408-94e1-bb78b6f26d8d · outbound

This paper cites Gaussian Error Linear Units (GELUs).

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Gaussian Error Linear Units (GELUs)

Reference 15

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source=pdf_text observed=2026-08-05T05:58:35.973378Z digest=sha256:5dc3ccb5584c09c1fdf941478c7dc3744aa315c52c81c0d8ded9552e8dca607a

Observation e02ed127-91f3-4eb9-bbb3-96ba1df54a7a · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 16

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source=pdf_text observed=2026-08-05T05:58:36.061756Z digest=sha256:fdb206029c0dab80169a4ef2b2a6da9d05bc70e54c0cd4cdd0ad7fad14663607

Observation e991f93b-79ea-4b4a-982c-62fa528071d7 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal co- variate shift.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Batch normalization: Accelerating deep network training by reducing internal co- variate shift

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:36.184124Z digest=sha256:4a96e6c843372347da958038e09d835e445c72b325dac9efc91b6fc451cadfc5

Observation 47d10050-ac09-478a-8a88-5762c33db0e4 · outbound

This paper cites Panoptic feature pyramid networks.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Panoptic feature pyramid networks

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:36.295349Z digest=sha256:abd7dbd885797e618be096aa3f0cd19b59c344e6c8354d6c7fbf8d50eaea2624

Observation 751592e1-6aac-4b3d-bd7f-ee307d6c455b · outbound

This paper cites Imagenet classification with deep convolutional neural net- works.Advances in neural information processing systems, 25, 2012.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Imagenet classification with deep convolutional neural net- works.Advances in neural information processing systems, 25, 2012

Reference 19

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:36.389134Z digest=sha256:4c9ce008b1ecc467d21249b3a401f3bc29051632fecfaeab87adc8f703a3bef0

Observation b9166290-5035-4e33-b157-72ace97c8f48 · outbound

This paper cites Gradient-based learning applied to document recog- nition.Proceedings of the IEEE, 86(11):2278–2324, 1998.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Gradient-based learning applied to document recog- nition.Proceedings of the IEEE, 86(11):2278–2324, 1998

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:36.478762Z digest=sha256:c5160a274bfaaeb20413062645cdff5d93e6e2eb5d343ed02f85c3e0585c1613

Observation 3ae3f516-10da-4203-929a-83766dbc747b · outbound

This paper cites Videomamba: State space model for efficient video understanding.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Videomamba: State space model for efficient video understanding

Reference 21

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raw_fallback, observed 2026-08-05T05:58:44.206115Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:36.606296Z digest=sha256:78d4bd47c13d546f6e67117ab9b74de70428fabef066112338aa1557c001543a

Observation f3483c8f-8267-46e6-93ff-0f99e5820c1f · outbound

This paper cites Rethinking Vision Transformers for MobileNet Size and Speed.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Rethinking Vision Transformers for MobileNet Size and Speed

Reference 22

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local_arxiv, observed 2026-08-05T05:58:39.830062Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:36.730352Z digest=sha256:17d9bd966c8a8f483a5cefdbb78bc9d00545d090b4c066efcb8145f0edfcacbd

Observation 368cf7c8-8827-4a50-be14-c05d87904c03 · outbound

This paper cites EfficientFormer: Vision Transformers at MobileNet Speed.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation EfficientFormer: Vision Transformers at MobileNet Speed

Reference 23

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source=pdf_text observed=2026-08-05T05:58:36.856982Z digest=sha256:5b6c077f4d41bf931255b11307a982d44a08b5e6de3a99f4b25f88e45e9af9cf

Observation e8f9e3f9-8ca7-4705-b6fe-9f80e6a2b6cd · outbound

This paper cites Vision Mamba: A Comprehensive Survey and Taxonomy.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Vision Mamba: A Comprehensive Survey and Taxonomy

Reference 24

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source=pdf_text observed=2026-08-05T05:58:36.974497Z digest=sha256:51c5b2ef68fe0be8942689dc02a4480e1033d6b6e9b0e341e00a5d34cbb0fa92

Observation 936224c9-be73-481f-a6d1-ef993772d01f · outbound

This paper cites Vmamba: Visual state space model.Advances in neural information processing systems, 37:103031–103063, 2024.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Vmamba: Visual state space model.Advances in neural information processing systems, 37:103031–103063, 2024

Reference 25

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:37.071587Z digest=sha256:e3df37f3bac5d76118724168e8348484c7d8e47d689cc9ced15cc89c16168d54

Observation 20306d8c-19f8-4b77-bf23-59046d1a6584 · outbound

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

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 26

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source=pdf_text observed=2026-08-05T05:58:37.181206Z digest=sha256:2f7547cc5b93e33cddca9e53bb012eafd33197a25f3c125612217be6d816eb60

Observation 348259d3-ce1b-4458-86d5-5273198273f1 · outbound

This paper cites A convnet for the 2020s.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation A convnet for the 2020s

Reference 27

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source=pdf_text observed=2026-08-05T05:58:37.303681Z digest=sha256:2f656cf4c13e75daef3a6d9a6bb2dd3a49e8d03ca1369f83bb0034f043d1aeeb

Observation 1108aeb2-5462-4fcd-9e39-21d83dfca2c3 · outbound

This paper cites Decoupled Weight Decay Regularization.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Decoupled Weight Decay Regularization

Reference 28

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source=pdf_text observed=2026-08-05T05:58:37.413506Z digest=sha256:cafe20e80fa5721c24f8e2e823bdc1e363c8fd585a06baf1184dc79bf6fdfd60

Observation 19d5b4a8-c25f-4541-a607-e49b0be41cd8 · outbound

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

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 29

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source=pdf_text observed=2026-08-05T05:58:37.547248Z digest=sha256:6a8ce5eed25f0ef7f989959928314a82d7774cc6af1b182fef69ee42ab5750e5

Observation 79c8756f-1f73-4f5d-b01f-17d17222a623 · outbound

This paper cites Separable Self-attention for Mobile Vision Transformers.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Separable Self-attention for Mobile Vision Transformers

Reference 30

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Observation 48c5ed50-5bde-4cbc-b5a0-6922891ba141 · outbound

This paper cites Mo- bilevig: Graph-based sparse attention for mobile vision ap- plications.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Mo- bilevig: Graph-based sparse attention for mobile vision ap- plications

Reference 31

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raw_fallback, observed 2026-08-05T05:58:43.603905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:37.774218Z digest=sha256:be17d012ffe3dc8e3ed95306fced9b85af778c369e852eea98eabe62b8c144ff

Observation 2ef09ccb-7d4c-4bc6-82c5-f58431b6e600 · outbound

This paper cites Greedyvig: Dynamic axial graph construction for efficient vision gnns.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Greedyvig: Dynamic axial graph construction for efficient vision gnns

Reference 32

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raw_fallback, observed 2026-08-05T05:58:43.304071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:37.887405Z digest=sha256:10cf15ee37c499e1e9d110c61f600222ed7bebb132b019c0ba718238ba9c851a

Observation d5d6544a-da93-4228-9a24-bef4fd93f228 · outbound

This paper cites Rapidnet: Multi-level dilated convolution based mobile backbone.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Rapidnet: Multi-level dilated convolution based mobile backbone

Reference 33

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raw_fallback, observed 2026-08-05T05:58:43.103711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:38.027571Z digest=sha256:b71f02295a19f8e2ac84cd617f0874ddee3e59eb9e666181b04b7149894cbcae

Observation f3e2d82f-a55e-42f9-9a62-adb617efdce8 · outbound

This paper cites Rectified linear units im- prove restricted boltzmann machines.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Rectified linear units im- prove restricted boltzmann machines

Reference 34

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raw_fallback, observed 2026-08-05T05:58:42.849443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:38.165862Z digest=sha256:086d41dba4b0d36f5a400747375a8421a6f477a6f1b32a24b6eb5da1d9b1026e

Observation 32ddb340-de77-4452-9394-2f6d399e3841 · outbound

This paper cites ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning

Reference 35

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no resolver link, observed 2026-08-05T05:58:38.203912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:58:38.203912Z digest=sha256:a60d1847c2f3bf76bea4f749ee02efa4ea90602c537dcb99870cbff9e29ec5ed

Observation 702b9af9-06ac-4268-b871-c0275d2327e4 · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library.Advances in neural in- formation processing systems, 32, 2019.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Pytorch: An imperative style, high- performance deep learning library.Advances in neural in- formation processing systems, 32, 2019

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:42.697527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:38.332819Z digest=sha256:f092931437fcb1104e8cc44eedcf155693a5ed6f8146c858c2f23177229113f9

Observation d486ca89-4361-4dd9-9e4a-00358841a1b1 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:42.507376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:38.448490Z digest=sha256:37fe4a960cfaaa09c696c95b5e330ced5282b60354555cc5ddcf9450c210be02

Observation a3739f46-e632-4c9c-b9a1-3f70e487841a · outbound

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

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-05T05:58:38.563887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:58:38.563887Z digest=sha256:fa9efbc04c526ed1bfe6e9bd0be4c66b75d0e182edecce52909018fda7c7aad0

Observation 433e61f8-cd0a-43aa-a62f-a48213a6a185 · outbound

This paper cites Wignet: Windowed vi- sion graph neural network.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Wignet: Windowed vi- sion graph neural network

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:42.326216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:38.673012Z digest=sha256:64c803172549c6c365b83004b23871d7a82628a35f8722120d8948aa56dcba89

Observation 15c407fa-fa22-41c1-914d-7959899ce7f5 · outbound

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

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-05T05:58:38.767608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:58:38.767608Z digest=sha256:e7c1960ef4ded67bea890fd6dc200a86bfd2d360be1b3f9d7d47852c54007ed0

Observation 7433c584-105b-4bc9-81da-a77f29a8600f · outbound

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

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Training data-efficient image transformers & distillation through at- tention

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:42.154466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:38.795749Z digest=sha256:bc013d35c937908c9fa8b725a3a2cfe7d54bdf177902fc99f0340a173a31ad3a

Observation 04c9c0a7-63c6-4ed2-bc4e-72c1ad5bd3b1 · outbound

This paper cites Fastvit: A fast hybrid vision transformer using structural reparameterization.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Fastvit: A fast hybrid vision transformer using structural reparameterization

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:41.984843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:38.849132Z digest=sha256:12e9c31451a7c6ba7059113dced395dcab6efacb5a4ee8e1b91b308f7acfedbf

Observation dbe7b6c8-9b96-4d34-b570-3827d060de11 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:41.761150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:38.919068Z digest=sha256:bbbf7764a8607db5af4d404a6b7e2f2295b324114f53adfc8e0c1802760c0c78

Observation ce0a5746-5951-4995-9755-23b4a3e40840 · outbound

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

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Repvit: Revisiting mobile cnn from vit perspective

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:41.596385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:38.991538Z digest=sha256:2b3e33ab111cab91504fbef577418ec522dd2ec9b74d70097ab33875722270f0

Observation ca8a8dfd-cb6e-4dbf-8a85-2efe9af0f6c7 · outbound

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

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Pyramid vision transformer: A versatile backbone for dense prediction without convolutions

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:41.356578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:39.099174Z digest=sha256:f51086d6eac2f292c71717fb7a47c49fb0026908dbf80b4f60ea682846460acf

Observation 8d9df2df-93f6-4551-bb7c-fc1ca0b1ac67 · outbound

This paper cites PyTorch Image Models.https : / / github.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation PyTorch Image Models.https : / / github

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:41.172195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:39.148372Z digest=sha256:515299359608fe5fe75e9aacb0cc4ac5936341f656daef4f5edaed59b16b51fb

Observation 277c4e8a-72b0-4200-b228-da2329e3794c · outbound

This paper cites PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition

Reference 47

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no resolver link, observed 2026-08-05T05:58:39.177250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:58:39.177250Z digest=sha256:7371eaaca7afd5855cbda4707843258a66c32fca80183993658a82c5ab23417d

Observation 36052a3a-3299-4037-9ca8-988c60a224be · outbound

This paper cites MambaOut: Do We Really Need Mamba for Vision?.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation MambaOut: Do We Really Need Mamba for Vision?

Reference 48

Resolution
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no resolver link, observed 2026-08-05T05:58:39.217128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:58:39.217128Z digest=sha256:57aca26026e404833de987b064752d6ca6c66764919d2c63098817053c29ecfb

Observation bf57f865-6435-4885-ab58-5eee5e2b3aea · outbound

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

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Metaformer is actually what you need for vision

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:40.999485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:39.249945Z digest=sha256:8d97b0f19ab7cbc55cab2f044c8af5f70116ef06a60e4060d8119ed576e2a985

Observation 981e8379-2f7a-49e4-8864-d796b4879b29 · outbound

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

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:40.818367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:39.296887Z digest=sha256:9e53f2f7e5747394dcec2cb92b531015e36cb6c10418605f2c152e573d3d467c

Observation 82870f97-73e4-414b-aff7-60e65a1f824e · outbound

This paper cites Dauphin, and David Lopez-Paz.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Dauphin, and David Lopez-Paz

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:40.663010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:39.340206Z digest=sha256:38faed6c94510201bbea0945a8f4b86b2e78a63f8e469b2bca91ae8496973006

Observation 08ab17e0-1696-4aa5-8291-6ec385f1d4fa · outbound

This paper cites Random erasing data augmentation.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Random erasing data augmentation

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:40.462242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:39.435994Z digest=sha256:952b7b348116762aa592f3cf478d9f51e11361dfd9f4b244bc4195c176060c3e

Observation bbf0b4bb-6de7-4169-a3d5-cce2fa52c974 · outbound

This paper cites Scene parsing through ade20k dataset.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Scene parsing through ade20k dataset

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-05T05:58:39.483221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:58:39.483221Z digest=sha256:ec67a2787a4a1bf6f4e53bec428ec205047dad03982012a38cbf9ba41a28da2d

Observation 22a9ae4d-183f-4311-8134-03a8e2e73197 · outbound

This paper cites Vision mamba: Efficient visual representation learning with bidirectional state space model.

VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation Vision mamba: Efficient visual representation learning with bidirectional state space model

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:58:40.296591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T05:58:39.562084Z digest=sha256:d1bd4fd29c84339324c09673326920ebf12acab908af128d4900cc494a6eeadb

Pith citing papers

No inbound Pith citation observations are available.