Paper Citation Record · LEDGER

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach

As of 16 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:1908.09547.

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

pith.paper-citation-record.v1
1908.09547 v1

Coverage vector

measured 44 of 44 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

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44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation 4cb1974b-44a0-43d9-86d8-d346013f3084 · outbound

This paper cites an unresolved cited work.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Unresolved cited work

Reference 1

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Observation 5f3a33f1-f029-4ca5-b342-cfb00d3ee93d · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 2

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Observation 5c3bf2c4-cc1f-487c-b1ee-6dad1f497927 · outbound

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

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 3

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This paper cites Road: Reality ori- ented adaptation for semantic segmentation of urban scenes.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Road: Reality ori- ented adaptation for semantic segmentation of urban scenes

Reference 4

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This paper cites No more discrimi- nation: Cross city adaptation of road scene segmenters.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach No more discrimi- nation: Cross city adaptation of road scene segmenters

Reference 5

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

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach The cityscapes dataset for semantic urban scene understanding

Reference 6

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This paper cites Curriculum model adaptation with synthetic and real data for semantic foggy scene understanding.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Curriculum model adaptation with synthetic and real data for semantic foggy scene understanding

Reference 7

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This paper cites Tsang, and Dong Xu.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Tsang, and Dong Xu

Reference 8

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This paper cites Tsang, and Jiebo Luo.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Tsang, and Jiebo Luo

Reference 9

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Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Unresolved cited work

Reference 10

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Observation 700c4da8-3128-46d2-bd08-25c3c8fa3f30 · outbound

This paper cites Self- ensembling for visual domain adaptation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Self- ensembling for visual domain adaptation

Reference 11

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This paper cites Learn- ing attributes equals multi-source domain generalization.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Learn- ing attributes equals multi-source domain generalization

Reference 12

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This paper cites Domain-adversarial train- ing of neural networks.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Domain-adversarial train- ing of neural networks

Reference 13

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This paper cites Connecting the dots with landmarks: Discriminatively learning domain- invariant features for unsupervised domain adaptation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Connecting the dots with landmarks: Discriminatively learning domain- invariant features for unsupervised domain adaptation

Reference 14

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This paper cites Geodesic ﬂow kernel for unsupervised domain adaptation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Geodesic ﬂow kernel for unsupervised domain adaptation

Reference 15

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This paper cites Deep residual learning for image recognition.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Deep residual learning for image recognition

Reference 16

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This paper cites Efros, and Trevor Dar- rell.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Efros, and Trevor Dar- rell

Reference 17

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This paper cites FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

Reference 18

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This paper cites Conditional generative adversarial network for struc- tured domain adaptation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Conditional generative adversarial network for struc- tured domain adaptation

Reference 19

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This paper cites Adaptive batch normalization for practical do- main adaptation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Adaptive batch normalization for practical do- main adaptation

Reference 20

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This paper cites ReﬁneNet: Multi-path reﬁnement networks for high- resolution semantic segmentation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach ReﬁneNet: Multi-path reﬁnement networks for high- resolution semantic segmentation

Reference 21

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This paper cites Lawrence Zitnick.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Lawrence Zitnick

Reference 22

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Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Fully convolutional networks for semantic segmentation

Reference 23

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Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Unresolved cited work

Reference 24

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Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Unresolved cited work

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This paper cites Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation

Reference 26

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This paper cites Image to image translation for domain adaptation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Image to image translation for domain adaptation

Reference 27

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This paper cites Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun

Reference 28

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Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Unresolved cited work

Reference 29

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This paper cites A dirt-t approach to unsupervised domain adaptation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach A dirt-t approach to unsupervised domain adaptation

Reference 30

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This paper cites Imagenet large scale visual recognition challenge.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Imagenet large scale visual recognition challenge

Reference 31

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Observation d442ff4b-f144-4d1e-9aed-14c9367edfed · outbound

This paper cites Maximum classiﬁer discrepancy for unsuper- vised domain adaptation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Maximum classiﬁer discrepancy for unsuper- vised domain adaptation

Reference 32

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This paper cites Learning from synthetic data: Addressing domain shift for semantic segmentation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Learning from synthetic data: Addressing domain shift for semantic segmentation

Reference 33

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

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 34

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This paper cites Learning to adapt structured output space for semantic seg- mentation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Learning to adapt structured output space for semantic seg- mentation

Reference 35

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This paper cites Advent: Adversarial entropy mini- mization for domain adaptation in semantic segmentation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Advent: Adversarial entropy mini- mization for domain adaptation in semantic segmentation

Reference 36

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This paper cites Wider or Deeper: Revisiting the ResNet Model for Visual Recognition.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

Reference 37

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Observation 58bdf248-e2c9-4f44-8129-c5c1c9dd3350 · outbound

This paper cites A curriculum domain adaptation approach to the se- mantic segmentation of urban scenes.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach A curriculum domain adaptation approach to the se- mantic segmentation of urban scenes

Reference 38

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Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Curricu- lum domain adaptation for semantic segmentation of urban scenes

Reference 39

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Observation 565c4af3-38ab-4cfb-836f-35acb9240c29 · outbound

This paper cites Fully convolutional adaptation networks for semantic segmentation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Fully convolutional adaptation networks for semantic segmentation

Reference 40

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This paper cites Pyramid scene parsing network.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Pyramid scene parsing network

Reference 41

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Observation 522d82c7-73ea-4b18-a6af-eb1ebc6a6f47 · outbound

This paper cites Penalizing top performers: Conservative loss for semantic segmentation adaptation.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Penalizing top performers: Conservative loss for semantic segmentation adaptation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:11:29.293445Z

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Observation b05fbb39-70d9-47bd-a917-25824b6677d9 · outbound

This paper cites Vijaya Kumar, and Jinsong Wang.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach Vijaya Kumar, and Jinsong Wang

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:11:29.274236Z

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Observation 278d18ac-9e9f-4d59-b6af-5543f6893eca · outbound

This paper cites top + bot- tom.

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach top + bot- tom

Reference 44

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Pith citing papers

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