Unified Local and Global Attention Interaction Modeling for Vision Transformers | Paper Citation Record

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Reference 1

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Unresolved cited work

Reference 2

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This paper cites End-to-End Object Detection with Transformers.

Unified Local and Global Attention Interaction Modeling for Vision Transformers End-to-End Object Detection with Transformers

Reference 3

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This paper cites SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More.

Unified Local and Global Attention Interaction Modeling for Vision Transformers SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 4

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This paper cites SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More.

Unified Local and Global Attention Interaction Modeling for Vision Transformers SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 5

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Unresolved cited work

Reference 6

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

Unified Local and Global Attention Interaction Modeling for Vision Transformers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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This paper cites Concealed Object Detection.

Unified Local and Global Attention Interaction Modeling for Vision Transformers Concealed Object Detection

Reference 8

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Reference 9

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Fast R-CNN

Reference 10

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This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation.

Unified Local and Global Attention Interaction Modeling for Vision Transformers Rich feature hierarchies for accurate object detection and semantic segmentation

Reference 11

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This paper cites Goldberger, Luis A.

Unified Local and Global Attention Interaction Modeling for Vision Transformers Goldberger, Luis A

Reference 12

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This paper cites CMT: Convolutional Neural Networks Meet Vision Transformers.

Unified Local and Global Attention Interaction Modeling for Vision Transformers CMT: Convolutional Neural Networks Meet Vision Transformers

Reference 13

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Unresolved cited work

Reference 14

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This paper cites Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.

Unified Local and Global Attention Interaction Modeling for Vision Transformers Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Reference 15

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Deep Residual Learning for Image Recognition

Reference 16

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Unresolved cited work

Reference 17

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This paper cites Berg, Wan- Yen Lo, Piotr DollÃąr, and Ross Girshick.

Unified Local and Global Attention Interaction Modeling for Vision Transformers Berg, Wan- Yen Lo, Piotr DollÃąr, and Ross Girshick

Reference 18

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This paper cites Similarity of Neural Network Representations Revisited.

Unified Local and Global Attention Interaction Modeling for Vision Transformers Similarity of Neural Network Representations Revisited

Reference 19

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This paper cites CornerNet: Detecting Objects as Paired Keypoints.

Unified Local and Global Attention Interaction Modeling for Vision Transformers CornerNet: Detecting Objects as Paired Keypoints

Reference 20

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Reference 21

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Feature Pyramid Networks for Object Detection

Reference 22

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Girshick, Kaiming He , and Piotr Dollár

Reference 23

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Unified Local and Global Attention Interaction Modeling for Vision Transformers SSD: Single Shot MultiBox Detector

Reference 25

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This paper cites Swin Transformer V2: Scaling Up Capacity and Resolution.

Unified Local and Global Attention Interaction Modeling for Vision Transformers Swin Transformer V2: Scaling Up Capacity and Resolution

Reference 26

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This paper cites RTMDet: An Empirical Study of Designing Real-Time Object Detectors.

Unified Local and Global Attention Interaction Modeling for Vision Transformers RTMDet: An Empirical Study of Designing Real-Time Object Detectors

Reference 28

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Unresolved cited work

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Reference 33

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This paper cites In Computer Vision âĂŞ ECCV 2022 Workshops: Tel A viv, Israel, October 23âĂŞ27, 202 2, Proceedings, Part VII (Tel Aviv, Israel).

Unified Local and Global Attention Interaction Modeling for Vision Transformers In Computer Vision âĂŞ ECCV 2022 Workshops: Tel A viv, Israel, October 23âĂŞ27, 202 2, Proceedings, Part VII (Tel Aviv, Israel)

Reference 34

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Unified Local and Global Attention Interaction Modeling for Vision Transformers You Only Look Once: Unified, Real-Time Object Detection

Reference 36

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 37

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Wu, Safwan S

Reference 38

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Designing Network Design Spaces

Reference 39

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Unified Local and Global Attention Interaction Modeling for Vision Transformers EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 40

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Attention Is All You Need

Reference 41

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

Reference 42

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Reference 43

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 44

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Unified Local and Global Attention Interaction Modeling for Vision Transformers CvT: Introducing Convolutions to Vision Transformers

Reference 46

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Vision Transformer with Deformable Attention

Reference 47

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Unified Local and Global Attention Interaction Modeling for Vision Transformers DAT++: Spatially Dynamic Vision Transformer with Deformable Attention

Reference 48

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Reference 49

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Reference 51

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Detecting Twenty-thousand Classes using Image-level Supervision

Reference 52

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 53

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Reference 55

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Refiner: Refining Self-attention for Vision Transformers

Reference 57

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Unified Local and Global Attention Interaction Modeling for Vision Transformers Focal Loss for Dense Object Detection

Reference 2017

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Unified Local and Global Attention Interaction Modeling for Vision Transformers In European Conference on Computer Vision

Reference 2021

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This paper cites In 2023 IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (CVPR).

Unified Local and Global Attention Interaction Modeling for Vision Transformers In 2023 IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (CVPR)

Reference 2023

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This paper cites SurANet: Surrounding-Aware Network for Concealed Object Detection via Highly-Efficient Interactive Contrastive Learning Strategy.

Unified Local and Global Attention Interaction Modeling for Vision Transformers SurANet: Surrounding-Aware Network for Concealed Object Detection via Highly-Efficient Interactive Contrastive Learning Strategy

Reference 2024

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