Paper Citation Record · LEDGER

Universal Domain Adaptation for Semantic Segmentation

As of 8 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.22458.

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

pith.paper-citation-record.v1
2505.22458 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:13:43.154341Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

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

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

verified exact0
verified fuzzy38
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9c177bed-85e4-4fb1-8638-f0f2130f9969 · outbound

This paper cites On the effectiveness of image rotation for open set domain adaptation.

Universal Domain Adaptation for Semantic Segmentation On the effectiveness of image rotation for open set domain adaptation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:50.808220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation be34476f-f75b-454d-8809-915a5196e1ca · outbound

This paper cites Unified optimal transport framework for universal domain adaptation.Advances in Neural Information Processing Sys- tems, 35:29512–29524, 2022.

Universal Domain Adaptation for Semantic Segmentation Unified optimal transport framework for universal domain adaptation.Advances in Neural Information Processing Sys- tems, 35:29512–29524, 2022

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:50.665710Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cff5abe1-4fd4-476d-807f-8782688bf247 · outbound

This paper cites Synergistic image and feature adaptation: Towards cross-modality domain adaptation for medical image seg- mentation.

Universal Domain Adaptation for Semantic Segmentation Synergistic image and feature adaptation: Towards cross-modality domain adaptation for medical image seg- mentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:50.531251Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 926f3915-5c6d-4989-88e3-2ad5236fdad8 · outbound

This paper cites an unresolved cited work.

Universal Domain Adaptation for Semantic Segmentation Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:13:50.327276Z

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Observation d10ae03e-f42b-486f-90c7-16f557b99cb0 · outbound

This paper cites Do- main adaptation for semantic segmentation with maximum squares loss.

Universal Domain Adaptation for Semantic Segmentation Do- main adaptation for semantic segmentation with maximum squares loss

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:49.939052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 02ea3c69-8f74-427a-b234-2bbf80a9f3c9 · outbound

This paper cites Open-set domain adaptation for semantic segmentation.

Universal Domain Adaptation for Semantic Segmentation Open-set domain adaptation for semantic segmentation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:49.806225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 052767a5-0d5d-4adb-b562-7e74d315fb9b · outbound

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

Universal Domain Adaptation for Semantic Segmentation The cityscapes dataset for semantic urban scene understanding

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:38.622246Z

Source-reported events for the cited work

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Observation fa50a50e-455a-4b09-a431-912f6a810ed0 · outbound

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

Universal Domain Adaptation for Semantic Segmentation Imagenet: A large-scale hierarchical image database

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:38.745323Z

Source-reported events for the cited work

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Observation 491196a1-d344-43dc-9e3c-39b209523a0a · outbound

This paper cites Ssf-dan: Separated semantic feature based domain adaptation network for semantic segmentation.

Universal Domain Adaptation for Semantic Segmentation Ssf-dan: Separated semantic feature based domain adaptation network for semantic segmentation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:49.608944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 90f93546-dc2b-4d85-b056-59cf30f35a4e · outbound

This paper cites Learning to detect open classes for universal domain adapta- tion.

Universal Domain Adaptation for Semantic Segmentation Learning to detect open classes for universal domain adapta- tion

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:49.437175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 31918203-4d35-4c96-883f-8783730cf4e9 · outbound

This paper cites Deep residual learning for image recognition.

Universal Domain Adaptation for Semantic Segmentation Deep residual learning for image recognition

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:38.984908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f83d7d70-cbb8-42a4-8106-c6e011bd4386 · outbound

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

Universal Domain Adaptation for Semantic Segmentation Conditional generative adversarial network for struc- tured domain adaptation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:49.255404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 394d2b04-27e2-4fab-a863-93576f04a8de · outbound

This paper cites Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation.

Universal Domain Adaptation for Semantic Segmentation Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:49.021509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 49c52ed2-1e70-4c7f-a07b-74ff70228078 · outbound

This paper cites Hrda: Context-aware high-resolution domain-adaptive semantic segmentation.

Universal Domain Adaptation for Semantic Segmentation Hrda: Context-aware high-resolution domain-adaptive semantic segmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:48.835370Z

Source-reported events for the cited work

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Observation f3513010-a395-40c2-9bd4-57a335880162 · outbound

This paper cites Mic: Masked image consistency for context- enhanced domain adaptation.

Universal Domain Adaptation for Semantic Segmentation Mic: Masked image consistency for context- enhanced domain adaptation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:48.641109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4e75798e-bbca-4c7d-96cd-26226d08b2b6 · outbound

This paper cites Learning texture invari- ant representation for domain adaptation of semantic seg- mentation.

Universal Domain Adaptation for Semantic Segmentation Learning texture invari- ant representation for domain adaptation of semantic seg- mentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:48.500080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1c60044f-78a9-4812-b1ec-089c5873ce1b · outbound

This paper cites Improv- ing semantic segmentation via decoupled body and edge su- pervision.

Universal Domain Adaptation for Semantic Segmentation Improv- ing semantic segmentation via decoupled body and edge su- pervision

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:48.301665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 643b223e-8c6c-4446-86d7-3f4a3093c5a8 · outbound

This paper cites Bidirectional learning for domain adaptation of semantic segmentation.

Universal Domain Adaptation for Semantic Segmentation Bidirectional learning for domain adaptation of semantic segmentation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:48.137516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 354adf24-5e94-4dc5-953b-4dd52e31ec53 · outbound

This paper cites Constructing self-motivated pyramid curriculums for cross- domain semantic segmentation: A non-adversarial approach.

Universal Domain Adaptation for Semantic Segmentation Constructing self-motivated pyramid curriculums for cross- domain semantic segmentation: A non-adversarial approach

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:48.047107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 499acf74-92cc-4f21-8672-067469b83e52 · outbound

This paper cites ParseNet: Looking Wider to See Better.

Universal Domain Adaptation for Semantic Segmentation ParseNet: Looking Wider to See Better

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:39.843740Z

Source-reported events for the cited work

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Observation 991ab964-9913-4bc4-a923-10d9c06c0183 · outbound

This paper cites Fully convolutional networks for semantic segmentation.

Universal Domain Adaptation for Semantic Segmentation Fully convolutional networks for semantic segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:47.928998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0e0c0bb4-bf87-4957-883a-e250aac8f4a9 · outbound

This paper cites Mlnet: Mutual learning network with neighbor- hood invariance for universal domain adaptation.

Universal Domain Adaptation for Semantic Segmentation Mlnet: Mutual learning network with neighbor- hood invariance for universal domain adaptation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:47.675620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a6c8a878-7062-4e43-a6db-eedb7bccd76b · outbound

This paper cites Pixmatch: Unsu- pervised domain adaptation via pixelwise consistency train- ing.

Universal Domain Adaptation for Semantic Segmentation Pixmatch: Unsu- pervised domain adaptation via pixelwise consistency train- ing

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:47.505333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4fa77995-543c-4a0d-b6d8-d2da6a96f270 · outbound

This paper cites The role of context for object detection and semantic segmentation in the wild.

Universal Domain Adaptation for Semantic Segmentation The role of context for object detection and semantic segmentation in the wild

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:47.323753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1ed21738-dee6-4ae9-a3d6-f06388bdcdf4 · outbound

This paper cites Unsupervised intra-domain adaptation for se- mantic segmentation through self-supervision.

Universal Domain Adaptation for Semantic Segmentation Unsupervised intra-domain adaptation for se- mantic segmentation through self-supervision

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:47.127972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3796a133-c886-48e1-9dfe-4f794c16eaf2 · outbound

This paper cites Prevalence of neural collapse during the terminal phase of deep learning training.Proceedings of the National Academy of Sciences, 117(40):24652–24663, 2020.

Universal Domain Adaptation for Semantic Segmentation Prevalence of neural collapse during the terminal phase of deep learning training.Proceedings of the National Academy of Sciences, 117(40):24652–24663, 2020

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:46.886489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 167e8f1f-e2d5-4892-8099-0377a70fcf86 · outbound

This paper cites Playing for data: Ground truth from computer games.

Universal Domain Adaptation for Semantic Segmentation Playing for data: Ground truth from computer games

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:46.691173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5a60b72d-1476-4aaf-a1d4-a72c35b07787 · outbound

This paper cites Ovanet: One-vs-all net- work for universal domain adaptation.

Universal Domain Adaptation for Semantic Segmentation Ovanet: One-vs-all net- work for universal domain adaptation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:46.485436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2333b8cf-bde4-459e-88df-8a04ae789077 · outbound

This paper cites Universal domain adaptation through self supervi- sion.Advances in neural information processing systems, 33:16282–16292, 2020.

Universal Domain Adaptation for Semantic Segmentation Universal domain adaptation through self supervi- sion.Advances in neural information processing systems, 33:16282–16292, 2020

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:46.187908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0ddf6d12-5fd4-4567-8dfc-323685a99835 · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30, 2017.

Universal Domain Adaptation for Semantic Segmentation Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30, 2017

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed28a76b-2971-4dcd-aa5e-92ae02022855 · outbound

This paper cites Dacs: Domain adaptation via cross- domain mixed sampling.

Universal Domain Adaptation for Semantic Segmentation Dacs: Domain adaptation via cross- domain mixed sampling

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:46.000818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2a39c817-ef5d-4e15-a01a-fcf85bab366e · outbound

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

Universal Domain Adaptation for Semantic Segmentation Learning to adapt structured output space for semantic seg- mentation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:45.788189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2bb2af7d-dab1-4ba8-b204-6d4c9df02d06 · outbound

This paper cites Domain adaptation for structured output via discriminative patch representations.

Universal Domain Adaptation for Semantic Segmentation Domain adaptation for structured output via discriminative patch representations

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:45.595946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bea84e08-7ff4-4ea5-a9b7-7a48c5ddddfc · outbound

This paper cites Idd: A dataset for exploring problems of autonomous navigation in uncon- strained environments.

Universal Domain Adaptation for Semantic Segmentation Idd: A dataset for exploring problems of autonomous navigation in uncon- strained environments

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:45.382016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 63f9761c-0262-4cc7-8a27-0f342f5209cb · outbound

This paper cites Domain adaptive semantic segmentation with self-supervised depth estimation.

Universal Domain Adaptation for Semantic Segmentation Domain adaptive semantic segmentation with self-supervised depth estimation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:45.168039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c6eb006a-bca2-4952-883b-fc263c7cd74f · outbound

This paper cites Uncertainty-aware pseudo label refinery for domain adaptive semantic segmentation.

Universal Domain Adaptation for Semantic Segmentation Uncertainty-aware pseudo label refinery for domain adaptive semantic segmentation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:44.982165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 946a753d-382a-47a0-af00-00cd14121e4e · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transform- ers.Advances in Neural Information Processing Systems, 34:12077–12090, 2021.

Universal Domain Adaptation for Semantic Segmentation Segformer: Simple and efficient design for semantic segmentation with transform- ers.Advances in Neural Information Processing Systems, 34:12077–12090, 2021

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:44.772675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1c8507e3-3f4b-45cf-8cdd-9f6a9ab07683 · outbound

This paper cites Universal domain adaptation.

Universal Domain Adaptation for Semantic Segmentation Universal domain adaptation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:44.614476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0302bbe1-15ba-43e5-8065-258d71b27dc7 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

Universal Domain Adaptation for Semantic Segmentation Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:42.651381Z

Source-reported events for the cited work

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Observation 1f09ace1-835d-4c53-ba2e-77a1b0ab7172 · outbound

This paper cites Prototypical pseudo label denoising and tar- get structure learning for domain adaptive semantic segmen- tation.

Universal Domain Adaptation for Semantic Segmentation Prototypical pseudo label denoising and tar- get structure learning for domain adaptive semantic segmen- tation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:44.445251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a319738d-050b-4060-8206-73f39c0a4ca8 · outbound

This paper cites Pyramid scene parsing network.

Universal Domain Adaptation for Semantic Segmentation Pyramid scene parsing network

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:42.792805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddc91453-94db-4656-aaf9-1b2bae325cb3 · outbound

This paper cites Psanet: Point- wise spatial attention network for scene parsing.

Universal Domain Adaptation for Semantic Segmentation Psanet: Point- wise spatial attention network for scene parsing

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:44.205242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6fffe085-27a9-4de8-a4ec-9f16dec21b6d · outbound

This paper cites Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers.

Universal Domain Adaptation for Semantic Segmentation Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:42.903988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8181759-461e-41a7-8a47-ef3ebc4e3fe1 · outbound

This paper cites Rethinking semantic segmentation: A proto- type view.

Universal Domain Adaptation for Semantic Segmentation Rethinking semantic segmentation: A proto- type view

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:43.962500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e522a856-d37b-4c4b-b089-5fb5da89a453 · outbound

This paper cites Asymmetric non-local neural networks for seman- tic segmentation.

Universal Domain Adaptation for Semantic Segmentation Asymmetric non-local neural networks for seman- tic segmentation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:43.753727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0934f3c7-1e37-4648-bd26-3f0dd93462ac · outbound

This paper cites Unsupervised domain adaptation for semantic segmentation via class-balanced self-training.

Universal Domain Adaptation for Semantic Segmentation Unsupervised domain adaptation for semantic segmentation via class-balanced self-training

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:43.569334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 85b0f9d7-e47e-4e6c-b0f4-fa2de59b71f6 · outbound

This paper cites Confidence regularized self-training.

Universal Domain Adaptation for Semantic Segmentation Confidence regularized self-training

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:43.395134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.