Revisiting the Integration of Convolution and Attention for Vision Backbone | Paper Citation Record

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This paper cites Flamingo: a visual language model for few-shot learning.

Revisiting the Integration of Convolution and Attention for Vision Backbone Flamingo: a visual language model for few-shot learning

Reference 1

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Revisiting the Integration of Convolution and Attention for Vision Backbone High-performance large-scale image recognition without normalization

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Revisiting the Integration of Convolution and Attention for Vision Backbone Regionvit: Regional-to-local attention for vision transformers

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Revisiting the Integration of Convolution and Attention for Vision Backbone MMDetection: Open MMLab Detection Toolbox and Benchmark

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Revisiting the Integration of Convolution and Attention for Vision Backbone Mixformer: Mixing features across windows and dimensions

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Revisiting the Integration of Convolution and Attention for Vision Backbone Generating Long Sequences with Sparse Transformers

Reference 6

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Revisiting the Integration of Convolution and Attention for Vision Backbone Rethinking Attention with Performers

Reference 7

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Revisiting the Integration of Convolution and Attention for Vision Backbone Twins: Revisiting the design of spatial attention in vision transformers

Reference 8

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Revisiting the Integration of Convolution and Attention for Vision Backbone Conditional positional encodings for vision transformers

Reference 9

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Revisiting the Integration of Convolution and Attention for Vision Backbone Openmmlab semantic segmentation toolbox and benchmark

Reference 10

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This paper cites Randaugment: Practical automated data augmentation with a reduced search space.

Revisiting the Integration of Convolution and Attention for Vision Backbone Randaugment: Practical automated data augmentation with a reduced search space

Reference 11

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

Revisiting the Integration of Convolution and Attention for Vision Backbone Coatnet: Marrying convolution and attention for all data sizes

Reference 12

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Revisiting the Integration of Convolution and Attention for Vision Backbone Imagenet: A large-scale hierarchical image database

Reference 13

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This paper cites Cswin transformer: A general vision transformer backbone with cross-shaped windows.

Revisiting the Integration of Convolution and Attention for Vision Backbone Cswin transformer: A general vision transformer backbone with cross-shaped windows

Reference 14

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

Revisiting the Integration of Convolution and Attention for Vision Backbone An image is worth 16x16 words: Transformers for image recognition at scale

Reference 15

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This paper cites Study on density peaks clustering based on k-nearest neighbors and principal component analysis.

Revisiting the Integration of Convolution and Attention for Vision Backbone Study on density peaks clustering based on k-nearest neighbors and principal component analysis

Reference 16

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This paper cites Paca-vit: learning patch-to-cluster attention in vision transformers.

Revisiting the Integration of Convolution and Attention for Vision Backbone Paca-vit: learning patch-to-cluster attention in vision transformers

Reference 17

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This paper cites Cmt: Convolutional neural networks meet vision transformers.

Revisiting the Integration of Convolution and Attention for Vision Backbone Cmt: Convolutional neural networks meet vision transformers

Reference 18

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This paper cites Flatten transformer: Vision transformer using focused linear attention.

Revisiting the Integration of Convolution and Attention for Vision Backbone Flatten transformer: Vision transformer using focused linear attention

Reference 19

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This paper cites Neighborhood attention transformer.

Revisiting the Integration of Convolution and Attention for Vision Backbone Neighborhood attention transformer

Reference 20

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Revisiting the Integration of Convolution and Attention for Vision Backbone Mask r-cnn

Reference 21

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Revisiting the Integration of Convolution and Attention for Vision Backbone Masked autoencoders are scalable vision learners

Reference 22

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Revisiting the Integration of Convolution and Attention for Vision Backbone Squeeze-and-excitation networks

Reference 23

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Revisiting the Integration of Convolution and Attention for Vision Backbone Deep networks with stochastic depth

Reference 24

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This paper cites How Much Position Information Do Convolutional Neural Networks Encode?.

Revisiting the Integration of Convolution and Attention for Vision Backbone How Much Position Information Do Convolutional Neural Networks Encode?

Reference 25

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This paper cites All tokens matter: Token labeling for training better vision transformers.

Revisiting the Integration of Convolution and Attention for Vision Backbone All tokens matter: Token labeling for training better vision transformers

Reference 26

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Revisiting the Integration of Convolution and Attention for Vision Backbone Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 27

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Revisiting the Integration of Convolution and Attention for Vision Backbone Panoptic feature pyramid networks

Reference 28

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Revisiting the Integration of Convolution and Attention for Vision Backbone Uniformer: Unifying convolution and self-attention for visual recognition

Reference 29

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Revisiting the Integration of Convolution and Attention for Vision Backbone Clusterfomer: clustering as a universal visual learner

Reference 30

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Revisiting the Integration of Convolution and Attention for Vision Backbone Microsoft coco: Common objects in context

Reference 31

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Revisiting the Integration of Convolution and Attention for Vision Backbone Feature pyramid networks for object detection

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Revisiting the Integration of Convolution and Attention for Vision Backbone Focal loss for dense object detection

Reference 33

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Revisiting the Integration of Convolution and Attention for Vision Backbone Scale-aware modulation meet transformer

Reference 34

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Revisiting the Integration of Convolution and Attention for Vision Backbone Swin transformer: Hierarchical vision transformer using shifted windows

Reference 35

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Revisiting the Integration of Convolution and Attention for Vision Backbone A convnet for the 2020s

Reference 36

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Revisiting the Integration of Convolution and Attention for Vision Backbone Stochastic gradient descent with warm restarts

Reference 37

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Revisiting the Integration of Convolution and Attention for Vision Backbone Decoupled weight decay regularization

Reference 38

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Revisiting the Integration of Convolution and Attention for Vision Backbone Understanding the effective receptive field in deep convolutional neural networks

Reference 39

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Revisiting the Integration of Convolution and Attention for Vision Backbone On the integration of self-attention and convolution

Reference 40

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Revisiting the Integration of Convolution and Attention for Vision Backbone How do vision transformers work? In International Conference on Learning Representations

Reference 41

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Revisiting the Integration of Convolution and Attention for Vision Backbone From Sparse to Soft Mixtures of Experts

Reference 42

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Revisiting the Integration of Convolution and Attention for Vision Backbone Sg-former: Self-guided transformer with evolving token reallocation

Reference 43

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Revisiting the Integration of Convolution and Attention for Vision Backbone GLU Variants Improve Transformer

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Revisiting the Integration of Convolution and Attention for Vision Backbone Rethinking the inception architecture for computer vision

Reference 45

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Revisiting the Integration of Convolution and Attention for Vision Backbone Training data-efficient image transformers & distillation through attention

Reference 46

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Revisiting the Integration of Convolution and Attention for Vision Backbone Maxvit: Multi-axis vision transformer

Reference 47

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Revisiting the Integration of Convolution and Attention for Vision Backbone Attention is all you need

Reference 48

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Revisiting the Integration of Convolution and Attention for Vision Backbone Fast transformers with clustered attention

Reference 49

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Revisiting the Integration of Convolution and Attention for Vision Backbone Linformer: Self-Attention with Linear Complexity

Reference 50

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Revisiting the Integration of Convolution and Attention for Vision Backbone Pyramid vision transformer: A versatile backbone for dense prediction without convolutions

Reference 51

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Revisiting the Integration of Convolution and Attention for Vision Backbone Internimage: Exploring large-scale vision foundation models with deformable convolutions

Reference 52

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Revisiting the Integration of Convolution and Attention for Vision Backbone Crossformer: A versatile vision transformer hinging on cross-scale attention

Reference 53

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Revisiting the Integration of Convolution and Attention for Vision Backbone Pytorch image models

Reference 54

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Revisiting the Integration of Convolution and Attention for Vision Backbone Cvt: Introducing convolutions to vision transformers

Reference 55

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Revisiting the Integration of Convolution and Attention for Vision Backbone Vision transformer with deformable attention

Reference 56

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This paper cites Unified perceptual parsing for scene understanding.

Revisiting the Integration of Convolution and Attention for Vision Backbone Unified perceptual parsing for scene understanding

Reference 57

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This paper cites ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers.

Revisiting the Integration of Convolution and Attention for Vision Backbone ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers

Reference 58

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Revisiting the Integration of Convolution and Attention for Vision Backbone Moat: Alternating mobile convolution and attention brings strong vision models

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Revisiting the Integration of Convolution and Attention for Vision Backbone Scalablevit: Rethinking the context-oriented generalization of vision transformer

Reference 60

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Revisiting the Integration of Convolution and Attention for Vision Backbone Wave-vit: Unifying wavelet and transformers for visual representation learning

Reference 61

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Revisiting the Integration of Convolution and Attention for Vision Backbone Dual vision transformer

Reference 62

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Revisiting the Integration of Convolution and Attention for Vision Backbone mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

Reference 63

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Revisiting the Integration of Convolution and Attention for Vision Backbone Metaformer is actually what you need for vision

Reference 64

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Revisiting the Integration of Convolution and Attention for Vision Backbone Cutmix: Regularization strategy to train strong classifiers with localizable features

Reference 65

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Revisiting the Integration of Convolution and Attention for Vision Backbone Not all tokens are equal: Human-centric visual analysis via token clustering transformer

Reference 66

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Revisiting the Integration of Convolution and Attention for Vision Backbone Dauphin, and David Lopez-Paz

Reference 67

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Revisiting the Integration of Convolution and Attention for Vision Backbone Dense distinct query for end-to-end object detection

Reference 68

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Revisiting the Integration of Convolution and Attention for Vision Backbone Semantic understanding of scenes through the ade20k dataset

Reference 69

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Revisiting the Integration of Convolution and Attention for Vision Backbone Biformer: Vision transformer with bi-level routing attention

Reference 70

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Revisiting the Integration of Convolution and Attention for Vision Backbone The contributions are summarized at the end of the Introduction (Sec

Reference 71

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Revisiting the Integration of Convolution and Attention for Vision Backbone Limitations

Reference 72

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Revisiting the Integration of Convolution and Attention for Vision Backbone Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 73

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Revisiting the Integration of Convolution and Attention for Vision Backbone Unresolved cited work

Reference 74

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Revisiting the Integration of Convolution and Attention for Vision Backbone We will release the code upon the acceptance of this paper

Reference 75

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Revisiting the Integration of Convolution and Attention for Vision Backbone Unresolved cited work

Reference 76

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Revisiting the Integration of Convolution and Attention for Vision Backbone The experimental results are not sensitive to random initialization

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:23.208439Z

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Revisiting the Integration of Convolution and Attention for Vision Backbone Unresolved cited work

Reference 78

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Revisiting the Integration of Convolution and Attention for Vision Backbone We only use existing and publicly available datasets for evaluations

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:23.177914Z

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Revisiting the Integration of Convolution and Attention for Vision Backbone It is too broad to discuss the societal impacts of such a general topic

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:23.162268Z

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Revisiting the Integration of Convolution and Attention for Vision Backbone It poses no such risks

Reference 81

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Revisiting the Integration of Convolution and Attention for Vision Backbone These works are properly cited

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:23.129743Z

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Revisiting the Integration of Convolution and Attention for Vision Backbone Guidelines: • The answer NA means that the paper does not release new assets

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:23.114445Z

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Revisiting the Integration of Convolution and Attention for Vision Backbone Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:23.098801Z

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Revisiting the Integration of Convolution and Attention for Vision Backbone Guidelines: 23 • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:15:23.082865Z

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