MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers | Paper Citation Record

Observation c7de7d48-ba92-4b66-8577-9f33223c599b · outbound

This paper cites Layer Normalization.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Layer Normalization

Reference 1

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Observation 7475193f-5158-467a-ace5-713f4e0b2708 · outbound

This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 2

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Observation 10321d41-0599-4d38-80f6-c09ac586fcb8 · outbound

This paper cites CycleMLP: A MLP-like Architecture for Dense Prediction.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers CycleMLP: A MLP-like Architecture for Dense Prediction

Reference 3

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Observation 39a28443-e4a1-4509-9530-e1271565b2c6 · outbound

This paper cites Twins: Revisiting the design of spatial attention in vision transformers, 2021.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Twins: Revisiting the design of spatial attention in vision transformers, 2021

Reference 4

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Observation 38fee022-f3ce-46fc-9af3-cb39d6b99425 · outbound

This paper cites Mmsegmentation: Open- mmlab semantic segmentation toolbox and benchmark,.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Mmsegmentation: Open- mmlab semantic segmentation toolbox and benchmark,

Reference 5

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Observation d348127d-e7fc-4b8a-84af-5b9fa2414222 · outbound

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

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Randaugment: Practical automated data augmen- tation with a reduced search space

Reference 6

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Observation aaa21eac-edf0-464d-87df-3800442065ce · outbound

This paper cites Coatnet: Marrying convolution and attention for all data sizes.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Coatnet: Marrying convolution and attention for all data sizes

Reference 7

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Observation 2c4525cc-c989-4802-9513-0e2f03475a57 · outbound

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

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Imagenet: A large-scale hierarchical image database

Reference 8

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Observation 9841c4be-b33b-4317-beb2-93eade38f88f · outbound

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

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

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Observation f0d87a41-ce01-46e2-bcab-4827e813cb0d · outbound

This paper cites Multiscale Vision Transformers.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Multiscale Vision Transformers

Reference 10

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Observation bd35d36f-b548-49eb-af81-00f068998dad · outbound

This paper cites Segnext: Rethinking convolutional attention design for semantic segmentation,.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Segnext: Rethinking convolutional attention design for semantic segmentation,

Reference 11

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Observation e9170c0f-c16c-4480-8fb4-a70225783446 · outbound

This paper cites Visual Attention Network.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Visual Attention Network

Reference 12

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Observation b4b86f3a-ccc3-42c6-9343-2aaeb8f003ab · outbound

This paper cites Deep residual learning for image recognition.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Deep residual learning for image recognition

Reference 13

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Observation 1b8d0802-2d77-4adb-93bf-6fad9df567e1 · outbound

This paper cites Mask r-cnn, 2018.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Mask r-cnn, 2018

Reference 14

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Observation 9a869c6b-0f24-41a6-b7d7-23d3e7b6be31 · outbound

This paper cites Deep networks with stochastic depth.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Deep networks with stochastic depth

Reference 15

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Observation bed20438-0074-4926-b6e5-a5a779e30582 · outbound

This paper cites Arbitrary style transfer in real-time with adaptive instance normalization, 2017.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Arbitrary style transfer in real-time with adaptive instance normalization, 2017

Reference 16

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Observation 29986528-00a0-4f41-93d0-31d13389fea1 · outbound

This paper cites Batch renormalization: Towards reducing minibatch dependence in batch-normalized models, 2017.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Batch renormalization: Towards reducing minibatch dependence in batch-normalized models, 2017

Reference 17

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Observation e595f8bc-5db7-46cf-a31e-7b9e1280c0fe · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 18

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Observation 1fa7156f-bf36-4b4d-8030-57372fbb9e40 · outbound

This paper cites Relational self-attention: What’s missing in attention for video understanding.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Relational self-attention: What’s missing in attention for video understanding

Reference 19

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Observation f2835c07-1940-4809-b32c-329e8f100596 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Adam: A Method for Stochastic Optimization

Reference 20

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Observation 5722a313-f18f-455e-935c-0cd05c2e3472 · outbound

This paper cites Panoptic feature pyramid networks.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Panoptic feature pyramid networks

Reference 21

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Observation a9de85a0-e180-435f-98e7-8c8daa20cef3 · outbound

This paper cites Mvitv2: Improved multiscale vision transformers for classification and detection.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Mvitv2: Improved multiscale vision transformers for classification and detection

Reference 22

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Observation d7166ac2-e40f-425b-b744-885ac63c940e · outbound

This paper cites AS-MLP: An Axial Shifted MLP Architecture for Vision.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers AS-MLP: An Axial Shifted MLP Architecture for Vision

Reference 23

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Observation 3ca758f5-1059-4aae-9d56-02f41a15dc4a · outbound

This paper cites Microsoft coco: Common objects in context.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Microsoft coco: Common objects in context

Reference 24

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Observation 331bfe55-e673-4641-87c1-7b13291d4340 · outbound

This paper cites Focal loss for dense object detection, 2018.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Focal loss for dense object detection, 2018

Reference 25

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Observation 50af3cc3-dbaa-4b87-ae15-d1ae37dfcb64 · outbound

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

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Swin transformer: Hierarchical vision transformer using shifted windows

Reference 26

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Observation 5732a23a-d106-486c-afc5-4c97c07cb58a · outbound

This paper cites A convnet for the 2020s.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers A convnet for the 2020s

Reference 27

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Observation 11d4a1a3-db15-4655-913e-64721d343196 · outbound

This paper cites Decoupled Weight Decay Regularization.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Decoupled Weight Decay Regularization

Reference 28

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Observation 3637f4e0-8dfe-4153-86c9-994b75885459 · outbound

This paper cites How do vision transformers work?, 2022.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers How do vision transformers work?, 2022

Reference 29

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Observation 66a09598-7ecb-4a39-b708-bd37f83e4407 · outbound

This paper cites Semantic image synthesis with spatially-adaptive nor- malization.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Semantic image synthesis with spatially-adaptive nor- malization

Reference 30

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Observation 1d79b78b-fc4a-46b3-94eb-64f62fc40bc7 · outbound

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

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Pytorch: An im- perative style, high-performance deep learning library

Reference 31

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Observation f007f04f-8fa1-4923-a4a2-595b4b2d6a9c · outbound

This paper cites Acceleration of stochastic approximation by averaging.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Acceleration of stochastic approximation by averaging

Reference 32

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Observation 7ec5e319-f1ad-4b0e-8730-94dc35119152 · outbound

This paper cites What makes for good tokenizers in vision transformer? IEEE Transactions on Pattern Analysis and Machine Intelligence,.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers What makes for good tokenizers in vision transformer? IEEE Transactions on Pattern Analysis and Machine Intelligence,

Reference 33

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Observation 85eb279b-be1e-4cd5-971b-1d3f423a896c · outbound

This paper cites Do vision trans- formers see like convolutional neural networks? Advances in Neural Information Processing Systems, 34:12116–12128,.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Do vision trans- formers see like convolutional neural networks? Advances in Neural Information Processing Systems, 34:12116–12128,

Reference 34

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Observation a066e8f0-2382-4d92-9860-4fdfb750d206 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks, 2019.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Mobilenetv2: Inverted residuals and linear bottlenecks, 2019

Reference 35

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Observation 43ce6aa9-f109-4aee-8aa8-3c833791980f · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 36

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Observation 00b7bd86-6602-4cb8-b4ae-862074991779 · outbound

This paper cites Powernorm: Rethinking batch normaliza- tion in transformers.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Powernorm: Rethinking batch normaliza- tion in transformers

Reference 37

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Observation 5b13d27e-365c-4453-a5ce-616fe2a4970d · outbound

This paper cites NormFormer: Improved Transformer Pretraining with Extra Normalization.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers NormFormer: Improved Transformer Pretraining with Extra Normalization

Reference 38

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Observation b5ae794c-f344-4b07-9303-94610bc313a6 · outbound

This paper cites Evalnorm: Esti- mating batch normalization statistics for evaluation, 2019.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Evalnorm: Esti- mating batch normalization statistics for evaluation, 2019

Reference 39

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Observation 2dd01271-4d17-4d0b-a9c3-50a8174ddd71 · outbound

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MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Rethinking the inception archi- tecture for computer vision

Reference 40

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Observation af4e1c54-1194-4a2e-a0ac-16d053fd602c · outbound

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MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Mlp-mixer: An all-mlp architecture for vision

Reference 41

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Observation 64fd3689-1a74-44e6-9569-c61fbf8cb174 · outbound

This paper cites Resmlp: Feedforward networks for image clas- sification with data-efficient training, 2021.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Resmlp: Feedforward networks for image clas- sification with data-efficient training, 2021

Reference 42

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Observation a2ea657f-b050-44e1-909f-ceca805a7b5d · outbound

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

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Training data-efficient image transformers & distillation through at- tention

Reference 43

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Observation e3b1bac5-b829-415c-8c37-3cb2e6eb3a82 · outbound

This paper cites Maxvit: Multi-axis vision transformer, 2022.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Maxvit: Multi-axis vision transformer, 2022

Reference 44

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Observation a68175a4-aef5-436b-a78d-6f4fad36d37e · outbound

This paper cites In- stance normalization: The missing ingredient for fast styliza- tion, 2017.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers In- stance normalization: The missing ingredient for fast styliza- tion, 2017

Reference 45

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Observation 010ca6d3-2c3b-44b4-ae3d-80234adaec9c · outbound

This paper cites Attention is all you need.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Attention is all you need

Reference 46

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Observation 7ccb88f5-0263-4385-bd4d-493a491ee74b · outbound

This paper cites When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism

Reference 47

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Observation 9d642eae-4c45-4e06-ae32-ec959ce8413e · outbound

This paper cites Ri- former: Keep your vision backbone effective but removing token mixer.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Ri- former: Keep your vision backbone effective but removing token mixer

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:33.188014Z

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Observation 934a293f-64b3-41c7-9779-35f66fab10b0 · outbound

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

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Pyramid vision transformer: A versatile backbone for dense prediction without convolutions

Reference 49

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Observation 58281994-c54f-402d-868d-32042a40b407 · outbound

This paper cites PVT v2: Improved baselines with pyramid vision transformer.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers PVT v2: Improved baselines with pyramid vision transformer

Reference 50

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Observation 71d710a3-29b9-41b8-8a47-834aab5ee576 · outbound

This paper cites Active Token Mixer.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Active Token Mixer

Reference 51

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Observation aab8e676-b42c-4244-a709-ec76e82795de · outbound

This paper cites Con- vnext v2: Co-designing and scaling convnets with masked autoencoders.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Con- vnext v2: Co-designing and scaling convnets with masked autoencoders

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:32.998901Z

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Observation b7f03e5f-06f8-4ac8-a329-0c203d9ea072 · outbound

This paper cites Towards stabilizing batch statistics in backward propagation of batch normalization, 2020.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Towards stabilizing batch statistics in backward propagation of batch normalization, 2020

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:32.983278Z

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Observation fcb1aa8c-c73d-4f60-a7e9-adfe1931fdef · outbound

This paper cites Focal Self-attention for Local-Global Interactions in Vision Transformers.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Focal Self-attention for Local-Global Interactions in Vision Transformers

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T10:43:31.912949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60e22678-5467-4c24-bf79-d83fd66aa814 · outbound

This paper cites Focal modulation networks, 2022.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Focal modulation networks, 2022

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:32.915670Z

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 2b092c62-72a5-4398-a493-7d7e1cf01390 · outbound

This paper cites Leveraging batch normalization for vision transformers.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Leveraging batch normalization for vision transformers

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:32.836864Z

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 482c18dc-8972-4260-b143-2d85abeea79a · outbound

This paper cites S2-mlp: Spatial-shift mlp architecture for vision.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers S2-mlp: Spatial-shift mlp architecture for vision

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:32.687788Z

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 3d9b8806-7a7e-4cbd-bc12-361009d8e795 · outbound

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

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Metaformer is actually what you need for vision

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:32.618259Z

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 377dfb90-3f52-4454-99f2-561b28605c99 · outbound

This paper cites Metaformer baselines for vision, 2022.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Metaformer baselines for vision, 2022

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:32.601707Z

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 686a3d8f-9eaf-4655-bd66-a73e7f99c996 · outbound

This paper cites Inceptionnext: When inception meets convnext, 2023.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Inceptionnext: When inception meets convnext, 2023

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:32.545384Z

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 fb307237-2f1d-4ee6-822f-a8b74b031bf0 · outbound

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

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-12T10:43:31.974627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0dd0aee0-f4f8-4da9-bff4-12b173bcb47f · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers mixup: Beyond Empirical Risk Minimization

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T10:43:31.979103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c849c23-b374-4e99-a96e-0a26c994c2d6 · outbound

This paper cites Random erasing data augmentation.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Random erasing data augmentation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:32.420065Z

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 efa4ee6a-aa2f-4b95-886f-71b7b68a6f3a · outbound

This paper cites Semantic under- standing of scenes through the ade20k dataset.

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers Semantic under- standing of scenes through the ade20k dataset

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:32.346275Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers