Paper Citation Record · LEDGER

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery

As of 14 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2411.09101.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.09101 v2

Coverage vector

measured 38 of 38 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation 671b2a20-c65f-49d7-8992-b5fa16466907 · outbound

This paper cites SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,

Reference 1

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Observation 2700adac-7108-475b-851f-0ced3677b8eb · outbound

This paper cites Per-Pixel Classification is Not All You Need for Semantic Segmentation,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Per-Pixel Classification is Not All You Need for Semantic Segmentation,

Reference 2

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

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery ImageNet: A large-scale hierarchical image database,

Reference 3

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Observation 880c0604-57e8-4b0e-8ea9-da47f6c8d9aa · outbound

This paper cites ResUNet- a: A deep learning framework for semantic segmentation of remotely sensed data,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery ResUNet- a: A deep learning framework for semantic segmentation of remotely sensed data,

Reference 4

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Observation 5e047c9e-bcd4-4afa-96f2-b3b4eb671f31 · outbound

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

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,

Reference 5

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Observation 8a3f59d8-dea0-4b45-8037-f424ca87d94a · outbound

This paper cites Is Attention Better Than Matrix Decomposition?,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Is Attention Better Than Matrix Decomposition?,

Reference 6

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Observation c2b07af7-2ff4-49a7-884f-9abef142d929 · outbound

This paper cites SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference 7

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Observation 19b543f8-59ae-43cd-b208-9827fa6e722c · outbound

This paper cites Visual Attention Network.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Visual Attention Network

Reference 8

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Observation e8c890f1-a288-46b3-b9ca-07209d1b1df3 · outbound

This paper cites AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation,

Reference 9

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Observation 75504dbf-5341-48ce-9d84-9efc27e23b35 · outbound

This paper cites Deep residual learning for image recognition,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Deep residual learning for image recognition,

Reference 10

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Observation 4f03c24b-8fdb-4031-8ba3-1b9e1d106348 · outbound

This paper cites The possibilities and pitfalls of doing a secondary analysis of a qualitative data set,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery The possibilities and pitfalls of doing a secondary analysis of a qualitative data set,

Reference 11

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Observation 09f7a812-f9a5-4aae-b449-19135485bfde · outbound

This paper cites Weakly-supervised learning based automatic augmentation of aerial insulator images,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Weakly-supervised learning based automatic augmentation of aerial insulator images,

Reference 12

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Observation b1db340d-2137-4059-b13a-e783c1b93cb8 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Adam: A Method for Stochastic Optimization

Reference 13

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Observation 16a09ce9-8666-4ad1-94b3-f4a1ab09fb8b · outbound

This paper cites Focal Loss for Dense Object Detection.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Focal Loss for Dense Object Detection

Reference 14

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Observation 18dbdcb4-6dd8-48da-b6b5-056759e6277a · outbound

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

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 15

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Observation dd50a342-60be-4e16-bb7e-5a670a563f69 · outbound

This paper cites Cross-entropy loss functions: Theoretical analysis and applications,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Cross-entropy loss functions: Theoretical analysis and applications,

Reference 16

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Observation 57fb7f74-d41f-442e-bd42-6e0f5e5de4ea · outbound

This paper cites On the difficulty of training recurrent neural networks,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery On the difficulty of training recurrent neural networks,

Reference 17

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Observation 47c7f685-532c-4e35-b267-1a591fa63a80 · outbound

This paper cites Regmi, Unsupervised Image Segmentation in Satellite Imagery Using Deep Learning.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Regmi, Unsupervised Image Segmentation in Satellite Imagery Using Deep Learning

Reference 18

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Observation 0a39be60-8949-4504-a07f-07927f9821ba · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery U-Net: Convolutional Networks for Biomedical Image Segmentation,

Reference 19

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Observation 99c43734-1fdb-4027-9335-f119fc029935 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 20

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Observation 969b8fb8-4971-4046-ba69-b2c911839032 · outbound

This paper cites Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,

Reference 21

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Observation 9a5ca145-bcee-4a0a-80ca-d56d3f345534 · outbound

This paper cites RingMo: A Remote Sensing Foundation Model With Masked Image Modeling,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery RingMo: A Remote Sensing Foundation Model With Masked Image Modeling,

Reference 22

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Observation 5b0ea9d5-d300-4f73-9501-eb22982d59f1 · outbound

This paper cites Attention is all you need,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Attention is all you need,

Reference 23

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Observation 7ec611f1-91ec-4d05-b3ee-6df2f0130313 · outbound

This paper cites Advancing plain vision transformer toward remote sensing foundation model,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Advancing plain vision transformer toward remote sensing foundation model,

Reference 24

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Observation 23ecce86-770a-47ff-a3bf-5de16e372453 · outbound

This paper cites Samrs: Scaling-up remote sensing segmentation dataset with segment anything model,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Samrs: Scaling-up remote sensing segmentation dataset with segment anything model,

Reference 25

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Observation 5d3441a6-7ecb-485e-b0f2-803042188300 · outbound

This paper cites Transformers: State-of-the-Art Natural Language Pro- cessing,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Transformers: State-of-the-Art Natural Language Pro- cessing,

Reference 26

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Observation 925cff10-8209-4b13-8f99-75d3d77a6733 · outbound

This paper cites W-Net: Convolutional neural network for segmenting remote sensing images by dual path semantics,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery W-Net: Convolutional neural network for segmenting remote sensing images by dual path semantics,

Reference 27

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Observation 734fabe5-9102-4a9c-84b9-0949f53afa64 · outbound

This paper cites AANet: an attention- based alignment semantic segmentation network for high spatial resolu- tion remote sensing images,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery AANet: an attention- based alignment semantic segmentation network for high spatial resolu- tion remote sensing images,

Reference 28

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This paper cites DOTA: A Large-Scale Dataset for Object Detection in Aerial Images,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery DOTA: A Large-Scale Dataset for Object Detection in Aerial Images,

Reference 29

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Observation 3f0b730e-17f1-4989-a17e-3919ea25ecdf · outbound

This paper cites Unified perceptual parsing for scene understanding,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Unified perceptual parsing for scene understanding,

Reference 30

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Observation 9d408078-aa08-4034-a058-4f7f762f5b97 · outbound

This paper cites TreeUNet: Adaptive Tree convolutional neural networks for subdecimeter aerial image segmentation,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery TreeUNet: Adaptive Tree convolutional neural networks for subdecimeter aerial image segmentation,

Reference 31

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Observation 25bf471d-3f77-4a5f-b2b6-63c1867d418e · outbound

This paper cites iSAID: A Large-scale Dataset for Instance Seg- mentation in Aerial Images,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery iSAID: A Large-scale Dataset for Instance Seg- mentation in Aerial Images,

Reference 32

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This paper cites Context Encoding for Semantic Segmentation,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Context Encoding for Semantic Segmentation,

Reference 33

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This paper cites Vitaev2: Vision transformer advanced by exploring inductive bias for image recognition and be- yond,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Vitaev2: Vision transformer advanced by exploring inductive bias for image recognition and be- yond,

Reference 34

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Observation 27d46e93-7633-4aea-9cd2-60426f983a51 · outbound

This paper cites Iou loss for 2d/3d object detection,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Iou loss for 2d/3d object detection,

Reference 35

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Observation 94f32d10-c98d-4ad5-b2c5-b5c89d4331ef · outbound

This paper cites MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation,

Reference 36

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Observation 96658b5e-9e5b-4a7a-8c3d-89299bb94f00 · outbound

This paper cites Learning Deconvolution Network for Semantic Segmentation,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Learning Deconvolution Network for Semantic Segmentation,

Reference 37

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Observation 6dbabec3-d289-482a-a09f-39f8cb51f909 · outbound

This paper cites Using GAN Methods for Aerial Images Segmentation,.

Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery Using GAN Methods for Aerial Images Segmentation,

Reference 38

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Pith citing papers

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