Paper Citation Record · LEDGER

Unsupervised Class Generation to Expand Semantic Segmentation Datasets

As of 11 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2501.02264.

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

pith.paper-citation-record.v1
2501.02264 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:18:25.578206Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

34 of 34 outbound references displayed

verified exact1
verified fuzzy25
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5a8143ce-917f-44a1-ad3c-a348b7ea6ce3 · outbound

This paper cites Visual domain adaptation through lo- cality information.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Visual domain adaptation through lo- cality information

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:26.240886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation df502f5e-a2eb-4146-b8a6-b58fb0fd298f · outbound

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

Unsupervised Class Generation to Expand Semantic Segmentation Datasets The cityscapes dataset for semantic urban scene understanding

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:26.219989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation de8439a9-431b-4656-afd2-7f30f8b2da42 · outbound

This paper cites A method of assigning numerical and percentage values to the degree of roundness of sand grains.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets A method of assigning numerical and percentage values to the degree of roundness of sand grains

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:26.195285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 997f6897-729f-4019-b9f0-522af931d2b1 · outbound

This paper cites CARLA: An open urban driving simulator.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets CARLA: An open urban driving simulator

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:26.166937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1f887995-4c7e-48e5-8261-d2ce311d09c8 · outbound

This paper cites an unresolved cited work.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:18:26.142108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 07bc9d92-16c0-4a8a-a9f4-8065ce8a088f · outbound

This paper cites Ot- clda: Optimal transport and contrastive learning for domain adaptive semantic segmentation.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Ot- clda: Optimal transport and contrastive learning for domain adaptive semantic segmentation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:26.123291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c3b4bf6d-9613-467c-a09c-96ed12d15ad3 · outbound

This paper cites Dsp: Dual soft-paste for unsupervised domain adaptive semantic segmentation.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Dsp: Dual soft-paste for unsupervised domain adaptive semantic segmentation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:26.102644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 042d3169-b483-4432-be0b-5acdf9eaff14 · outbound

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

Unsupervised Class Generation to Expand Semantic Segmentation Datasets DAFormer: Improving network architectures and training strategies for domain-adaptive semantic segmentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:26.086300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fb094b86-94bd-416e-bb99-6e9c6a5dde48 · outbound

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

Unsupervised Class Generation to Expand Semantic Segmentation Datasets HRDA: Context-aware high-resolution domain-adaptive semantic segmentation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:26.068109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ed775792-b7c4-4fd2-9e78-a7685fadf12f · outbound

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

Unsupervised Class Generation to Expand Semantic Segmentation Datasets MIC: Masked image consistency for context- enhanced domain adaptation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:26.049589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ca44e116-d113-4540-ac91-b16287f9eac5 · outbound

This paper cites Segment any- thing.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Segment any- thing

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T22:18:25.462214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6082a9ef-675d-4e72-b12b-0bb9d097bb2c · outbound

This paper cites Efficient inference in fully connected crfs with gaussian edge potentials.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Efficient inference in fully connected crfs with gaussian edge potentials

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:26.018551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 209e991d-a50f-480a-b151-0049fdbd108c · outbound

This paper cites Open-vocabulary atten- tion maps with token optimization for semantic segmentation in diffusion models.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Open-vocabulary atten- tion maps with token optimization for semantic segmentation in diffusion models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:26.000649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 53aa5cd6-22f2-4c83-b55b-c65f12a0774e · outbound

This paper cites Nguyen, Trinh V.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Nguyen, Trinh V

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.981008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1f569d5a-04cf-420f-91fc-e92089574458 · outbound

This paper cites The third criterion: Compactness as a procedural safeguard against partisan ger- rymandering.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets The third criterion: Compactness as a procedural safeguard against partisan ger- rymandering

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.962202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 888b70bf-f2e9-4804-81be-588940b52f3d · outbound

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

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.944858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cc703e2c-5476-4b66-be60-dc7a22ad17f5 · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets High-Resolution Image Synthesis with Latent Diffusion Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T22:18:25.494784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0dba32cb-c268-4136-8cd3-e46c35899e08 · outbound

This paper cites Lgsvl simulator: A high fidelity simulator for autonomous driving.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Lgsvl simulator: A high fidelity simulator for autonomous driving

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.926973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9e2c445d-8ef1-4225-970e-971b88418908 · outbound

This paper cites an unresolved cited work.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:18:25.910315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1f50e05b-545a-4032-8542-b22129e10965 · outbound

This paper cites sch ¨afer, Nico M.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets sch ¨afer, Nico M

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.893731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cce4a785-6c44-4437-9701-e9c047349b38 · outbound

This paper cites Airsim: High-fidelity visual and physical simulation for autonomous vehicles.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Airsim: High-fidelity visual and physical simulation for autonomous vehicles

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T22:18:25.513971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abdba083-d7c2-4ae3-bdb6-74b60776cc41 · outbound

This paper cites What the DAAM: Interpreting Stable Diffusion Using Cross Attention.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets What the DAAM: Interpreting Stable Diffusion Using Cross Attention

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T22:18:25.518801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78699da9-a7b7-410c-aeb0-1da01c8dc953 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T22:18:25.524014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01615a8a-a9ef-418b-8983-37d88b9ecc26 · outbound

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

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Dacs: Domain adaptation via cross- domain mixed sampling

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.863956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e6ce63df-6403-4422-85a7-68aceb36dbfe · outbound

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

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Learning to adapt structured output space for semantic seg- mentation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.844495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 817dbc5c-ba65-487e-b748-62ed80cfc147 · outbound

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

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Advent: Adversarial entropy mini- mization for domain adaptation in semantic segmentation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.827179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5aff90be-fdf8-4f70-861f-995804996eba · outbound

This paper cites Pseudo-label assisted optimization of multi-branch net- work for cross-domain person re-identification.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Pseudo-label assisted optimization of multi-branch net- work for cross-domain person re-identification

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.810132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2f000bc1-2d52-431d-885a-92c259f73eca · outbound

This paper cites Datasetdm: Synthesizing data with perception annota- tions using diffusion models.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Datasetdm: Synthesizing data with perception annota- tions using diffusion models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.791956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e2648856-e581-4e6f-82d4-88945587d9cc · outbound

This paper cites Self-supervised adversarial learn- ing for domain adaptation of pavement distress classifica- tion.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Self-supervised adversarial learn- ing for domain adaptation of pavement distress classifica- tion

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.773226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c36cce6f-c14f-4a12-9f2e-b2789ac8d953 · outbound

This paper cites Transfer learning from synthetic to real lidar point cloud for semantic segmentation.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Transfer learning from synthetic to real lidar point cloud for semantic segmentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.755659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 21280b4d-79f5-4b3d-bb65-975808edbc08 · outbound

This paper cites an unresolved cited work.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:18:25.735246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3e5148f1-184d-4f33-ae09-20920abf9181 · outbound

This paper cites Weighted and class-specific maxi- mum mean discrepancy for unsupervised domain adaptation.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Weighted and class-specific maxi- mum mean discrepancy for unsupervised domain adaptation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.716869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 08125176-c482-4055-947e-94641d67cba2 · outbound

This paper cites DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:18:25.626408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e17cfef0-9b18-4833-b6d2-f42b16ec6f3b · outbound

This paper cites Parauda: Invariant feature learning with auxil- iary synthetic samples for unsupervised domain adaptation.

Unsupervised Class Generation to Expand Semantic Segmentation Datasets Parauda: Invariant feature learning with auxil- iary synthetic samples for unsupervised domain adaptation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:18:25.698238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.