Paper Citation Record · LEDGER

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers

As of 21 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2412.01941.

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

pith.paper-citation-record.v1
2412.01941 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:08:04.243400Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 01e98e9f-9b9c-4e8a-a91e-797239bb017e · outbound

This paper cites Towards build- ing more robust models with frequency bias.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Towards build- ing more robust models with frequency bias

Reference 1

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Observation 46f41064-eeab-4df7-990a-4fd3e3d1cd5f · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 2

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Observation 1d7b87d1-8165-4ca3-84d9-d685d69652d4 · outbound

This paper cites Twins: Re- visiting the design of spatial attention in vision transformers.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Twins: Re- visiting the design of spatial attention in vision transformers

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.848740Z

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Observation 1ae3941b-59a6-4823-ba42-e23bd9d50733 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers The cityscapes dataset for semantic urban scene understanding

Reference 4

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Observation edf53add-0075-4ad0-ab0c-115fa2cb7f91 · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Improved Regularization of Convolutional Neural Networks with Cutout

Reference 5

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Observation 1e76566e-5a61-4a98-b8b0-f5412a8a403a · outbound

This paper cites NoisyMix: Boosting Model Robustness to Common Corruptions.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers NoisyMix: Boosting Model Robustness to Common Corruptions

Reference 6

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Observation d71c3b06-f1b3-4465-bf8c-0557e8e8dbb2 · outbound

This paper cites Pixmix: Dreamlike pictures comprehensively improve safety measures.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Pixmix: Dreamlike pictures comprehensively improve safety measures

Reference 7

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Observation 353ab824-cef6-4f14-b384-816473b969d7 · outbound

This paper cites A simple feature augmentation for domain general- ization.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers A simple feature augmentation for domain general- ization

Reference 8

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Observation 906d1288-437c-478e-bb21-44565219850b · outbound

This paper cites Deep Manifold Traversal: Changing Labels with Convolutional Features.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Deep Manifold Traversal: Changing Labels with Convolutional Features

Reference 9

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Observation 17f6ac47-e1cc-4652-99e8-ef779273e898 · outbound

This paper cites The many faces of robust- ness: A critical analysis of out-of-distribution generalization.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers The many faces of robust- ness: A critical analysis of out-of-distribution generalization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.791449Z

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Observation f26ff672-45c6-4186-9143-9d7b7a1345da · outbound

This paper cites Benchmarking neural network robustness to common corruptions and perturbations.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Benchmarking neural network robustness to common corruptions and perturbations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.776928Z

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Observation 0abc210e-be57-4457-823d-926f7509d82d · outbound

This paper cites Augmix: A simple data processing method to improve robustness and uncertainty.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Augmix: A simple data processing method to improve robustness and uncertainty

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.762849Z

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Observation f6b87df1-6a9b-49e8-9d1b-bf444e7560d5 · outbound

This paper cites Pixmix: Dreamlike pictures comprehensively improve safety measures.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Pixmix: Dreamlike pictures comprehensively improve safety measures

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.748859Z

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Observation 7d5d4105-6de2-45a9-a02b-ef2d0f7f87ba · outbound

This paper cites Rethinking spatial dimensions of vision transformers.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Rethinking spatial dimensions of vision transformers

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.733464Z

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Observation 36db1b46-aaf1-4c5c-a0fe-a5e2b5765b8f · outbound

This paper cites Benchmarking the robustness of semantic segmentation models.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Benchmarking the robustness of semantic segmentation models

Reference 15

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Observation 88549abb-6cb2-41f2-84f7-0a0eac55e4be · outbound

This paper cites A simple feature augmentation for domain generalization.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers A simple feature augmentation for domain generalization

Reference 16

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Source-reported events for the cited work

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Observation d9b51739-fe44-4642-8f5d-cf0ba3f7f73f · outbound

This paper cites Noisy feature mixup.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Noisy feature mixup

Reference 17

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Source-reported events for the cited work

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Observation 474853b9-bc7d-41c7-b5ff-5549cb755e53 · outbound

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

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Swin transformer: Hierarchical vision transformer using shifted windows

Reference 18

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Observation 2ddbdcde-3544-41d0-aa8f-f2d5b6a4eb12 · outbound

This paper cites A convnet for the 2020s.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers A convnet for the 2020s

Reference 19

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Observation 24421126-bd1b-41a8-b232-85c6d8804dfc · outbound

This paper cites Fully convolutional networks for semantic segmentation.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Fully convolutional networks for semantic segmentation

Reference 20

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Observation f1a14135-6c7e-404b-b0ca-0edbcb15710d · outbound

This paper cites Towards robust vision transformer.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Towards robust vision transformer

Reference 21

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Observation 8913e596-a3cb-4c43-946e-4a4958b98295 · outbound

This paper cites Benchmarking robustness in object detection: Autonomous driving when winter is com- ing.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Benchmarking robustness in object detection: Autonomous driving when winter is com- ing

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.622100Z

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Observation b6d139e2-9d08-4d92-a252-4fe7da760495 · outbound

This paper cites On in- teraction between augmentations and corruptions in natural corruption robustness.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers On in- teraction between augmentations and corruptions in natural corruption robustness

Reference 23

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Observation ab6be951-d87f-4aec-a1c8-d3cc32f714b0 · outbound

This paper cites Prime: A few primitives can boost robustness to common corruptions.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Prime: A few primitives can boost robustness to common corruptions

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.594074Z

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Observation b2a07d7d-4f0d-4ab8-91a6-71d48bf1cad5 · outbound

This paper cites A simple way to make neural networks robust against diverse image corruptions.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers A simple way to make neural networks robust against diverse image corruptions

Reference 25

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Observation 0a28b2e0-a756-4d53-ba02-24d51acd5613 · outbound

This paper cites A survey on image data augmentation for deep learning.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers A survey on image data augmentation for deep learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.565045Z

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Observation 1d120f20-9f67-4940-bbdf-9bff7deb2756 · outbound

This paper cites Deep feature interpolation for image content changes.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Deep feature interpolation for image content changes

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.550641Z

Source-reported events for the cited work

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Observation 0f80ecf0-eedd-4ab7-8eab-890ae1028421 · outbound

This paper cites Augmax: Adversar- ial composition of random augmentations for robust training.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Augmax: Adversar- ial composition of random augmentations for robust training

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.536393Z

Source-reported events for the cited work

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Observation 5dae46fa-3285-4be3-925f-16b9b4fdfd9c · outbound

This paper cites Internimage: Exploring large-scale vision foundation models with deformable convolutions.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Internimage: Exploring large-scale vision foundation models with deformable convolutions

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.521819Z

Source-reported events for the cited work

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Observation e6ee66f9-aadd-472c-8afb-6a1ddb1bf4ba · outbound

This paper cites Pyra- mid vision transformer: A versatile backbone for dense pre- diction without convolutions.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Pyra- mid vision transformer: A versatile backbone for dense pre- diction without convolutions

Reference 30

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8624202a-e608-4925-9b87-28e51864682b · outbound

This paper cites Wider or deeper: Revisiting the resnet model for visual recog- nition.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Wider or deeper: Revisiting the resnet model for visual recog- nition

Reference 31

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Observation cf0e30e1-d661-496f-b529-4363839812c7 · outbound

This paper cites Segformer: Simple and effi- cient design for semantic segmentation with transformers.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Segformer: Simple and effi- cient design for semantic segmentation with transformers

Reference 32

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Observation 2cd3997d-c9bf-47f6-87b8-92291a2e2f55 · outbound

This paper cites Multi-scale context aggrega- tion by dilated convolutions.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Multi-scale context aggrega- tion by dilated convolutions

Reference 33

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Observation eabb8a4b-8adb-439a-b98d-6ae545419887 · outbound

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

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 34

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Observation 56ea6ca5-5697-40f8-b9c3-8aac39071585 · outbound

This paper cites mixup: Beyond empirical risk minimization.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers mixup: Beyond empirical risk minimization

Reference 35

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Observation afca0b18-03e9-4b45-bdd6-ca133d5683af · outbound

This paper cites Fully at- tentional networks with self-emerging token labeling.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Fully at- tentional networks with self-emerging token labeling

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.426894Z

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Observation fd5bfc58-2e72-4101-98e0-a326c6052e75 · outbound

This paper cites Icnet for real-time semantic segmentation on high-resolution images.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Icnet for real-time semantic segmentation on high-resolution images

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.411815Z

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Observation 4c9cb4e3-c33f-4f27-9509-332770f4c931 · outbound

This paper cites Pyramid scene parsing network.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Pyramid scene parsing network

Reference 38

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Observation dab713d6-6617-4bd2-85ed-3e1e2c830457 · outbound

This paper cites Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.381081Z

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Observation bf9350f0-1b4e-4526-9baf-8976fb209dda · outbound

This paper cites Improving the robustness of deep neural networks via stability training.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Improving the robustness of deep neural networks via stability training

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.367195Z

Source-reported events for the cited work

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Observation e6644863-2b5f-4ef6-a6d0-4f5e191cc6d3 · outbound

This paper cites Scene parsing through ade20k dataset.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Scene parsing through ade20k dataset

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.353009Z

Source-reported events for the cited work

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Observation 9964ab4d-5ea0-4dd5-9152-cef9ad2cd3f6 · outbound

This paper cites Un- derstanding the robustness in vision transformers.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Un- derstanding the robustness in vision transformers

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.338623Z

Source-reported events for the cited work

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Observation 9367ed03-34a3-4b6f-90c6-639dc95afdd2 · outbound

This paper cites Choice of ϵ It is only important to select an ϵ value that incites the model to learn new representations i.e, which brings de- creases the baseline model performance.

Global Average Feature Augmentation for Robust Semantic Segmentation with Transformers Choice of ϵ It is only important to select an ϵ value that incites the model to learn new representations i.e, which brings de- creases the baseline model performance

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:08:04.322908Z

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Pith citing papers

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