VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation | Paper Citation Record

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:58:44.206115Z

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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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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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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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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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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

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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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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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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

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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

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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

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:58:43.304071Z

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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

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:58:43.103711Z

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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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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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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

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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

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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

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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

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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

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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

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

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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

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

Source-reported events for the cited work

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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

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

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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

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

Source-reported events for the cited work

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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

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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 2026-08-13T06:32:02.005865+00:00.

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 2026-08-13T06:32:02.005865+00:00.

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

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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 2026-08-13T06:32:02.005865+00:00.

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

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

Source-reported events for the cited work

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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

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

Source-reported events for the cited work

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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 2026-08-13T06:32:02.005865+00:00.

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.

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 2026-08-13T06:32:02.005865+00:00.

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