Multi-Token Enhancing for Vision Representation Learning | Paper Citation Record

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This paper cites Asano, Christian Rupprecht, and Andrea Vedaldi.

Multi-Token Enhancing for Vision Representation Learning Asano, Christian Rupprecht, and Andrea Vedaldi

Reference 1

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Observation fb9c483a-0a2e-4aa0-95d1-237f9ae77cf2 · outbound

This paper cites BEit: BERT pre-training of image transformers.

Multi-Token Enhancing for Vision Representation Learning BEit: BERT pre-training of image transformers

Reference 2

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Observation 81363a80-e5b7-4c4c-93aa-0416cae652cb · outbound

This paper cites Cascade r-cnn: Delving into high quality object detection.

Multi-Token Enhancing for Vision Representation Learning Cascade r-cnn: Delving into high quality object detection

Reference 3

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Observation 17b47991-8b28-4e0b-9141-b7f273e49974 · outbound

This paper cites Unsupervised learn- ing of visual features by contrasting cluster assignments.

Multi-Token Enhancing for Vision Representation Learning Unsupervised learn- ing of visual features by contrasting cluster assignments

Reference 4

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Observation 0eb08d6c-8a62-4bb8-87af-39508d7990a7 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Multi-Token Enhancing for Vision Representation Learning Emerg- ing properties in self-supervised vision transformers

Reference 5

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Observation e5b8be40-8107-4031-972d-4d6a660ce06f · outbound

This paper cites Mixed autoencoder for self-supervised visual representation learning.

Multi-Token Enhancing for Vision Representation Learning Mixed autoencoder for self-supervised visual representation learning

Reference 6

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Observation bae4412f-d772-4e0f-80d8-54654691c16e · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Multi-Token Enhancing for Vision Representation Learning A simple framework for contrastive learning of visual representations

Reference 7

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Observation 4987cec4-5022-4483-86b2-723da00bcfe6 · outbound

This paper cites Exploring simple siamese rep- resentation learning.

Multi-Token Enhancing for Vision Representation Learning Exploring simple siamese rep- resentation learning

Reference 8

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Observation 7b450849-3c64-41c3-b76d-bf7cd4ad6571 · outbound

This paper cites An empiri- cal study of training self-supervised vision transformers.

Multi-Token Enhancing for Vision Representation Learning An empiri- cal study of training self-supervised vision transformers

Reference 9

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Observation 1c7a8955-0b96-42f2-b548-25581cf233fe · outbound

This paper cites Convit: Improving vision transformers with soft convolutional inductive biases.

Multi-Token Enhancing for Vision Representation Learning Convit: Improving vision transformers with soft convolutional inductive biases

Reference 10

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Observation d5345f7c-f55e-466c-8da6-f9b683ef5111 · outbound

This paper cites Ensemble methods in machine learn- ing.

Multi-Token Enhancing for Vision Representation Learning Ensemble methods in machine learn- ing

Reference 11

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Observation 7c14abbd-a1ad-4f6c-9577-b8b7051979d7 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Multi-Token Enhancing for Vision Representation Learning An image is worth 16x16 words: Transformers for image recognition at scale

Reference 12

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Observation 59e7332b-e982-491d-9d09-8a3b37c71d86 · outbound

This paper cites Whitening for self-supervised representation learning.

Multi-Token Enhancing for Vision Representation Learning Whitening for self-supervised representation learning

Reference 13

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Observation f324fea3-b88c-4cbd-95d9-b320c9086d2e · outbound

This paper cites Seed: Self-supervised dis- tillation for visual representation.

Multi-Token Enhancing for Vision Representation Learning Seed: Self-supervised dis- tillation for visual representation

Reference 14

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This paper cites Evolved part masking for self-supervised learning.

Multi-Token Enhancing for Vision Representation Learning Evolved part masking for self-supervised learning

Reference 15

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Observation 1df1351e-b688-472e-8008-58ad63218e85 · outbound

This paper cites Ganaie, Minghui Hu, A.K.

Multi-Token Enhancing for Vision Representation Learning Ganaie, Minghui Hu, A.K

Reference 16

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Observation cc5b256c-db5d-4777-a2d4-2980d658d30e · outbound

This paper cites Large-scale un- supervised semantic segmentation.

Multi-Token Enhancing for Vision Representation Learning Large-scale un- supervised semantic segmentation

Reference 17

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Observation 87d73ed9-4a22-4f77-a574-d3764fe50431 · outbound

This paper cites Richemond, Elena Buchatskaya, Carl Doersch, Bernardo ´Avila Pires, Zhaohan Guo, Moham- mad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, R´emi Munos, and Michal Valko.

Multi-Token Enhancing for Vision Representation Learning Richemond, Elena Buchatskaya, Carl Doersch, Bernardo ´Avila Pires, Zhaohan Guo, Moham- mad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, R´emi Munos, and Michal Valko

Reference 18

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Observation 0007e657-5c59-4277-9ac2-129af3be0f57 · outbound

This paper cites Visual Attention Network.

Multi-Token Enhancing for Vision Representation Learning Visual Attention Network

Reference 19

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Observation 202ee373-4dee-40f8-b622-5c150b8e821b · outbound

This paper cites Hansen and P.

Multi-Token Enhancing for Vision Representation Learning Hansen and P

Reference 20

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Observation ec2b9bb3-731d-42c2-8a6a-304c77232e7a · outbound

This paper cites Training independent subnetworks for robust prediction.

Multi-Token Enhancing for Vision Representation Learning Training independent subnetworks for robust prediction

Reference 21

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Observation 6f0a9f0a-c6a9-420a-b565-579b2f1db22f · outbound

This paper cites Deep residual learning for image recognition.

Multi-Token Enhancing for Vision Representation Learning Deep residual learning for image recognition

Reference 22

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Observation 7307fe2b-58c4-4e12-be64-f897cce54176 · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Multi-Token Enhancing for Vision Representation Learning Momentum contrast for unsupervised visual rep- resentation learning

Reference 23

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Observation fec79ee1-7fa5-4f2b-9c1b-320cee2d22b1 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Multi-Token Enhancing for Vision Representation Learning Masked autoencoders are scalable vision learners

Reference 24

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Observation ae5b7a43-a9ee-4420-8f01-4c65f282ca8a · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Multi-Token Enhancing for Vision Representation Learning Distilling the Knowledge in a Neural Network

Reference 25

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Observation f91d5ffd-e3db-4dee-ac1b-1705c7950631 · outbound

This paper cites Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition.

Multi-Token Enhancing for Vision Representation Learning Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition

Reference 26

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This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

Multi-Token Enhancing for Vision Representation Learning Averaging Weights Leads to Wider Optima and Better Generalization

Reference 27

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Observation 008a7f77-e186-44e7-8e9b-595500a28f5c · outbound

This paper cites Similarity of neural network representa- tions revisited.

Multi-Token Enhancing for Vision Representation Learning Similarity of neural network representa- tions revisited

Reference 28

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Observation 6b864517-9da5-4755-9925-88bcbaf7e41c · outbound

This paper cites Fractalnet: Ultra-deep neural networks without residuals.

Multi-Token Enhancing for Vision Representation Learning Fractalnet: Ultra-deep neural networks without residuals

Reference 29

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Observation cf0c168f-5f8b-47c9-b486-193d522a2456 · outbound

This paper cites Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks.

Multi-Token Enhancing for Vision Representation Learning Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks

Reference 30

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Observation 49c894ea-0fc9-4cbd-be3b-b64fc9434d9c · outbound

This paper cites Sere: Exploring feature self-relation for self-supervised trans- former.

Multi-Token Enhancing for Vision Representation Learning Sere: Exploring feature self-relation for self-supervised trans- former

Reference 31

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This paper cites Enhancing representa- tions through heterogeneous self-supervised learning.

Multi-Token Enhancing for Vision Representation Learning Enhancing representa- tions through heterogeneous self-supervised learning

Reference 32

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This paper cites Microsoft coco: Common objects in context.

Multi-Token Enhancing for Vision Representation Learning Microsoft coco: Common objects in context

Reference 33

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This paper cites Swin trans- former: Hierarchical vision transformer using shifted win- dows.

Multi-Token Enhancing for Vision Representation Learning Swin trans- former: Hierarchical vision transformer using shifted win- dows

Reference 34

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This paper cites A convnet for the 2020s.

Multi-Token Enhancing for Vision Representation Learning A convnet for the 2020s

Reference 35

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This paper cites Decoupled weight decay regularization.

Multi-Token Enhancing for Vision Representation Learning Decoupled weight decay regularization

Reference 36

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This paper cites Representation uncertainty in self-supervised learning as variational inference.

Multi-Token Enhancing for Vision Representation Learning Representation uncertainty in self-supervised learning as variational inference

Reference 37

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This paper cites Simreg: Regression as a sim- ple yet effective tool for self-supervised knowledge distilla- tion.

Multi-Token Enhancing for Vision Representation Learning Simreg: Regression as a sim- ple yet effective tool for self-supervised knowledge distilla- tion

Reference 38

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This paper cites Representation Learning with Contrastive Predictive Coding.

Multi-Token Enhancing for Vision Representation Learning Representation Learning with Contrastive Predictive Coding

Reference 39

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This paper cites Imagenet large scale visual recognition challenge.

Multi-Token Enhancing for Vision Representation Learning Imagenet large scale visual recognition challenge

Reference 40

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This paper cites Learning common rationale to improve self-supervised rep- resentation for fine-grained visual recognition problems.

Multi-Token Enhancing for Vision Representation Learning Learning common rationale to improve self-supervised rep- resentation for fine-grained visual recognition problems

Reference 41

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This paper cites an unresolved cited work.

Multi-Token Enhancing for Vision Representation Learning Unresolved cited work

Reference 42

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Observation 1397c6fb-8ffa-482b-9707-6664ac7e44ae · outbound

This paper cites Multi- mode online knowledge distillation for self-supervised visual representation learning.

Multi-Token Enhancing for Vision Representation Learning Multi- mode online knowledge distillation for self-supervised visual representation learning

Reference 43

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Observation 39d4be1e-013c-4f9a-ac70-79b84717ee06 · outbound

This paper cites Semantics-consistent feature search for self-supervised visual representation learning.

Multi-Token Enhancing for Vision Representation Learning Semantics-consistent feature search for self-supervised visual representation learning

Reference 44

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Observation 01ba14a2-f08d-40cd-be01-5faafd0dab63 · outbound

This paper cites Dropout: A simple way to prevent neural networks from overfitting.

Multi-Token Enhancing for Vision Representation Learning Dropout: A simple way to prevent neural networks from overfitting

Reference 45

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This paper cites Siamese image modeling for self-supervised vision represen- tation learning.

Multi-Token Enhancing for Vision Representation Learning Siamese image modeling for self-supervised vision represen- tation learning

Reference 46

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This paper cites Un- derstanding self-supervised learning dynamics without con- trastive pairs.

Multi-Token Enhancing for Vision Representation Learning Un- derstanding self-supervised learning dynamics without con- trastive pairs

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:59:36.921604Z

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This paper cites The inaturalist species classification and de- tection dataset.

Multi-Token Enhancing for Vision Representation Learning The inaturalist species classification and de- tection dataset

Reference 48

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Observation fd3cc78a-db4f-4275-9230-f92fded43f8e · outbound

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

Multi-Token Enhancing for Vision Representation Learning Pyramid vision transformer: A versatile backbone for dense prediction without convolutions

Reference 49

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This paper cites Dense contrastive learning for self-supervised visual pre-training.

Multi-Token Enhancing for Vision Representation Learning Dense contrastive learning for self-supervised visual pre-training

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:59:36.888226Z

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Observation 0b6c0f23-d613-4c04-a131-7ab4fed5a8bf · outbound

This paper cites Masked Feature Prediction for Self-Supervised Visual Pre-Training.

Multi-Token Enhancing for Vision Representation Learning Masked Feature Prediction for Self-Supervised Visual Pre-Training

Reference 51

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Observation 8f63cfc6-8ea1-4a29-8c9c-fbd761f90e77 · outbound

This paper cites Batchensemble: an alternative approach to efficient ensemble and lifelong learning.

Multi-Token Enhancing for Vision Representation Learning Batchensemble: an alternative approach to efficient ensemble and lifelong learning

Reference 52

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Observation fd8cabfb-f044-4e51-ae64-d8cf6949b0e4 · outbound

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

Multi-Token Enhancing for Vision Representation Learning Con- vnext v2: Co-designing and scaling convnets with masked autoencoders

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:59:36.865107Z

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This paper cites Cvt: Introducing con- volutions to vision transformers.

Multi-Token Enhancing for Vision Representation Learning Cvt: Introducing con- volutions to vision transformers

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:59:36.851238Z

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Observation d1933b00-fa25-46b6-9444-27b306a7c594 · outbound

This paper cites P2T: Pyramid pooling transformer for scene understanding.

Multi-Token Enhancing for Vision Representation Learning P2T: Pyramid pooling transformer for scene understanding

Reference 55

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Multi-Token Enhancing for Vision Representation Learning Unified perceptual parsing for scene understand- ing

Reference 56

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Multi-Token Enhancing for Vision Representation Learning Detco: Unsu- pervised contrastive learning for object detection

Reference 57

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Multi-Token Enhancing for Vision Representation Learning Self-Supervised Learning with Swin Transformers

Reference 58

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Multi-Token Enhancing for Vision Representation Learning Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:59:36.801568Z

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Multi-Token Enhancing for Vision Representation Learning Simmim: A simple framework for masked image modeling

Reference 60

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Multi-Token Enhancing for Vision Representation Learning Bag of instances aggregation boosts self-supervised distillation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:59:36.782035Z

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Multi-Token Enhancing for Vision Representation Learning Joint unsuper- vised learning of deep representations and image clusters

Reference 62

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Multi-Token Enhancing for Vision Representation Learning Decoupled contrastive learning

Reference 63

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Multi-Token Enhancing for Vision Representation Learning Online deep clustering for unsupervised representation learning

Reference 64

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Multi-Token Enhancing for Vision Representation Learning Scene parsing through ade20k dataset

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:59:36.732631Z

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Multi-Token Enhancing for Vision Representation Learning ibot: Image bert pre-training with online tokenizer

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:59:36.719476Z

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This paper cites Mugs: A Multi-Granular Self-Supervised Learning Framework.

Multi-Token Enhancing for Vision Representation Learning Mugs: A Multi-Granular Self-Supervised Learning Framework

Reference 67

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Multi-Token Enhancing for Vision Representation Learning Multi-label self- supervised learning with scene images

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:59:36.707319Z

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