RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone | Paper Citation Record

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This paper cites Scal- ing graph convolutions for mobile vision.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Scal- ing graph convolutions for mobile vision

Reference 1

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This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 2

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone End-to- end object detection with transformers

Reference 3

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This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 4

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Mobile- former: Bridging mobilenet and transformer

Reference 5

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Observation d3c38b0f-0ceb-46ae-b701-1986349229c0 · outbound

This paper cites Conditional Positional Encodings for Vision Transformers.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Conditional Positional Encodings for Vision Transformers

Reference 6

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This paper cites Randaugment: Practical automated data augmenta- tion with a reduced search space.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Randaugment: Practical automated data augmenta- tion with a reduced search space

Reference 7

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This paper cites Deformable convolutional networks.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Deformable convolutional networks

Reference 8

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Observation 1adce494-db87-4d00-aba5-12832d14a845 · outbound

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Imagenet: A large-scale hierarchical image database

Reference 9

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Observation 7aeecad1-ac6d-4e72-bdf6-f3dfbbe68858 · outbound

This paper cites Diffusion models beat gans on image synthesis.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Diffusion models beat gans on image synthesis

Reference 10

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Observation d342fad8-5a90-43c9-a226-624b835c4f6e · outbound

This paper cites Scaling up your kernels to 31x31: Revisiting large kernel design in cnns.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Scaling up your kernels to 31x31: Revisiting large kernel design in cnns

Reference 11

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Observation c7330ced-f030-4875-8513-925ba370f3d9 · outbound

This paper cites Repvgg: Making vgg-style convnets great again.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Repvgg: Making vgg-style convnets great again

Reference 12

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Observation 31c0b2b9-d2af-413f-b4ff-0890d7385ee2 · outbound

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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Observation 8b81a51b-1fdb-460f-871e-e9e38bb411db · outbound

This paper cites Structure and content-guided video synthesis with diffusion models.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Structure and content-guided video synthesis with diffusion models

Reference 14

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Observation f69b7e9c-5546-413f-b7b6-529c6c1ddf9b · outbound

This paper cites Generative adversarial nets.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Generative adversarial nets

Reference 15

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Observation c48d4fd7-9f2f-43bb-b7a0-17cca776d3dc · outbound

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Vision GNN: An Image is Worth Graph of Nodes

Reference 16

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This paper cites Vision hgnn: An image is more than a graph of nodes.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Vision hgnn: An image is more than a graph of nodes

Reference 17

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Observation 249a9cc4-81e5-4a24-8f6f-442d5ffc05cf · outbound

This paper cites Mask r-cnn.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Mask r-cnn

Reference 18

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Observation 076c5023-2da1-443b-8dad-d4f3c3ac8ccc · outbound

This paper cites Deep residual learning for image recognition.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Deep residual learning for image recognition

Reference 19

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Observation e90c0451-e25d-41c4-9dd5-0461e45a5d40 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Gaussian Error Linear Units (GELUs)

Reference 20

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Observation 517a7230-65a6-4cd6-b79f-5687aa4622b3 · outbound

This paper cites Denoising diffu- sion probabilistic models.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Denoising diffu- sion probabilistic models

Reference 21

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Observation f1abce3f-1dd8-40f2-a571-4349cb71e78b · outbound

This paper cites Augment your batch: Improving generalization through instance repetition.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Augment your batch: Improving generalization through instance repetition

Reference 22

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Observation b755d8a9-9ab9-4e3f-b84a-43aa8893e0ce · outbound

This paper cites A real-time algorithm for signal analysis with the help of the wavelet transform.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone A real-time algorithm for signal analysis with the help of the wavelet transform

Reference 23

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Observation cb713ff4-962f-4e2b-8871-6a291d21a3ca · outbound

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 24

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This paper cites Densely connected convolutional networks.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Densely connected convolutional networks

Reference 25

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Observation 4de091ea-566c-42a5-892a-576c4469abb2 · outbound

This paper cites An Introduction to Image Synthesis with Generative Adversarial Nets.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone An Introduction to Image Synthesis with Generative Adversarial Nets

Reference 26

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This paper cites SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Reference 27

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Observation e8684d9c-b6b8-4b60-860e-7cff4ff0a4f6 · outbound

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Batch normalization: Accelerating deep network training by reducing internal co- variate shift

Reference 28

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Observation 125edee6-05b3-4ba9-b6b4-bed0eb5a2af5 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Adam: A Method for Stochastic Optimization

Reference 29

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Observation 422fe248-b246-446f-bccc-4e9e2422cfb2 · outbound

This paper cites Panoptic feature pyramid networks.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Panoptic feature pyramid networks

Reference 30

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Observation 3d3a9a3a-6b2c-4f75-b7e9-4cc1f45fcfc9 · outbound

This paper cites Gradient-based learning applied to document recog- nition.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Gradient-based learning applied to document recog- nition

Reference 31

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This paper cites Deepgcns: Can gcns go as deep as cnns? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9267–9276, 2019.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Deepgcns: Can gcns go as deep as cnns? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9267–9276, 2019

Reference 32

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This paper cites Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

Reference 33

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Rethinking Vision Transformers for MobileNet Size and Speed

Reference 34

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This paper cites EfficientFormer: Vision Transformers at MobileNet Speed.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone EfficientFormer: Vision Transformers at MobileNet Speed

Reference 35

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This paper cites Microsoft coco: Common objects in context.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Microsoft coco: Common objects in context

Reference 36

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This paper cites Generative adversarial networks for image and video synthesis: Algorithms and applications.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Generative adversarial networks for image and video synthesis: Algorithms and applications

Reference 37

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Swin transformer: Hierarchical vision transformer using shifted windows

Reference 38

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This paper cites A convnet for the 2020s.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone A convnet for the 2020s

Reference 39

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This paper cites Decoupled Weight Decay Regularization.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Decoupled Weight Decay Regularization

Reference 40

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Shufflenet v2: Practical guidelines for efficient cnn architec- ture design

Reference 41

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This paper cites Towards robust vision transformer.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Towards robust vision transformer

Reference 42

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 43

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Separable Self-attention for Mobile Vision Transformers

Reference 44

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This paper cites Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation

Reference 45

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Observation 59aa6c9e-0b9d-423a-8e5f-baea781720e3 · outbound

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Mo- bilevig: Graph-based sparse attention for mobile vision ap- plications

Reference 46

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Observation 08c25109-4795-47c1-9ee5-1ffab67d9c9d · outbound

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Greedyvig: Dynamic axial graph construction for efficient vision gnns

Reference 47

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This paper cites Three decades of low power: From watts to wisdom.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Three decades of low power: From watts to wisdom

Reference 48

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This paper cites Edgevits: Competing light-weight cnns on mo- bile devices with vision transformers.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Edgevits: Competing light-weight cnns on mo- bile devices with vision transformers

Reference 49

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This paper cites Pytorch: An imperative style, high- performance deep learning library.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Pytorch: An imperative style, high- performance deep learning library

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This paper cites Designing network design spaces.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Designing network design spaces

Reference 51

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Observation 9005c671-8392-487a-b944-39a375a28e5e · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 52

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Observation 6eeb8de7-5536-4071-9cc0-278a2d0df1e7 · outbound

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 53

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This paper cites Efficientnetv2: Smaller models and faster training.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Efficientnetv2: Smaller models and faster training

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:55.935960Z

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Observation 24fcf53d-cb88-4f58-b027-0ba3d381b53e · outbound

This paper cites Mlp-mixer: An all-mlp architecture for vision.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Mlp-mixer: An all-mlp architecture for vision

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This paper cites Resmlp: Feedforward networks for image classification with data-efficient training.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Resmlp: Feedforward networks for image classification with data-efficient training

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:55.915615Z

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This paper cites MobileOne: An Improved One millisecond Mobile Backbone.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone MobileOne: An Improved One millisecond Mobile Backbone

Reference 57

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Fastvit: A fast hybrid vision transformer using structural reparameterization

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:55.905258Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dab2d9c9-bcfd-486c-a4c5-8a9147fc80f3 · outbound

This paper cites Attention is all you need.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Attention is all you need

Reference 59

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This paper cites RepViT: Revisiting Mobile CNN From ViT Perspective.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone RepViT: Revisiting Mobile CNN From ViT Perspective

Reference 60

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Observation 06911349-cb74-4daa-b8d5-3720143e3073 · outbound

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Pyramid vision transformer: A versatile backbone for dense prediction without convolutions

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:55.889461Z

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Observation b31b36f7-d81f-4d91-bb12-e4309ecf7e78 · outbound

This paper cites Can CNNs Be More Robust Than Transformers?.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Can CNNs Be More Robust Than Transformers?

Reference 62

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This paper cites PyTorch Image Models.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone PyTorch Image Models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:55.880780Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d2946ea3-90f2-471a-8d4f-d12119c0f52d · outbound

This paper cites Multi-Scale Context Aggregation by Dilated Convolutions.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Multi-Scale Context Aggregation by Dilated Convolutions

Reference 64

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Observation dcc94d24-3491-44d9-8727-c9a7790b049d · outbound

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

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Metaformer is actually what you need for vision

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:55.871683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6cc4c62d-db55-41e8-9dce-ec6b152b50d3 · outbound

This paper cites Cutmix: Regular- ization strategy to train strong classifiers with localizable fea- tures.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Cutmix: Regular- ization strategy to train strong classifiers with localizable fea- tures

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:55.860889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6bf3bbae-5d81-44d5-90fd-0c69534bd56d · outbound

This paper cites Dauphin, and David Lopez-Paz.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Dauphin, and David Lopez-Paz

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:55.850721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f2fef51e-d294-4d06-8c7d-2e0072367a3e · outbound

This paper cites Shufflenet: An extremely efficient convolutional neural net- work for mobile devices.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Shufflenet: An extremely efficient convolutional neural net- work for mobile devices

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:55.840802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ef7554bb-2f8e-468a-9b5f-0e67fd175116 · outbound

This paper cites Random erasing data augmentation.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Random erasing data augmentation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:55.830660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c0d53dd3-c0a7-47f1-9414-8d07e09e916d · outbound

This paper cites Scene parsing through ade20k dataset.

RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone Scene parsing through ade20k dataset

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:28:55.820164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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