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Paper Citation Record · LEDGER

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation

As of 9 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2507.17957.

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

pith.paper-citation-record.v1
2507.17957 v2

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:44:28.404327Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

30 of 30 outbound references displayed

  • verified exact1
  • verified fuzzy27
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 54271a0b-6ec4-4e2e-8bd9-c115ed7abf3f · outbound

This paper cites Learning to adapt structured output space for semantic segmentation,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Learning to adapt structured output space for semantic segmentation,

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.644719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.336053Z digest=sha256:e82495fc6b43521dfb0a4ee698884223e1aaaebdd56a6ac5a7e95533ce1b2a3e

Observation a50c16ca-94d1-42c6-8cd9-f21c7707b054 · outbound

This paper cites Dlow: Domain flow and applications,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Dlow: Domain flow and applications,

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.637519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 167063fc-4a02-4252-8c4b-ce848887f264 · outbound

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

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Daformer: Improving network architectures and training strategies for domain-adaptive semantic seg- mentation,

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.630218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.341821Z digest=sha256:08a58804e87840e2ef3d68cf8dc33f3b6e16b0b05ebd2729ec42c3ba308b9a62

Observation 51da81e8-820b-4b9e-9d02-896164cc3209 · outbound

This paper cites Ida: Informed domain adaptive semantic segmentation,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Ida: Informed domain adaptive semantic segmentation,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.623046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.344311Z digest=sha256:c0028c0626af96b6cb7c437c6e7b0ced1bb4a9bd70f254ec6703ee9b4e47fc17

Observation 50ac4552-5c5e-458c-ada6-72b6f6cbbcf7 · outbound

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

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Hrda: Context-aware high- resolution domain-adaptive semantic segmentation,

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.616187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.346846Z digest=sha256:7ebaf4968d3e13e75599bf1602d8739aaedfc62d1458c96517446fb37f1328d3

Observation bef15e9c-4258-4313-be94-3215fe6f3bcd · outbound

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

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Playing for data: Ground truth from computer games,

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.608954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.349304Z digest=sha256:5845afc6cd4106a7c57a6a119c49012bfb081af7dda3b8b6b177e0b57e2ebd26

Observation ccc7fc35-4c72-4234-8ec4-54ec52f84570 · outbound

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

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation The cityscapes dataset for semantic urban scene understanding,

Reference 7

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raw_fallback, observed 2026-08-06T14:44:28.601555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.351844Z digest=sha256:5feb0effa0798bafe678a504898e71f981a49a89e25c1856af3112d325416bfd

Observation 4507ba6a-858a-41f6-bddc-7c3cd69d30e9 · outbound

This paper cites The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,

Reference 8

Resolution
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raw_fallback, observed 2026-08-06T14:44:28.594446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.354112Z digest=sha256:6eb745b75bb21b037ac20ed0727f9809d754cb1786379c8b1e2bfb6db8d19f7e

Observation 23db50e8-6734-41f1-afa5-68254ee12fc4 · outbound

This paper cites A rugd dataset for autonomous navigation and visual perception in unstructured outdoor environments,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation A rugd dataset for autonomous navigation and visual perception in unstructured outdoor environments,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.587002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.356309Z digest=sha256:2d101d9f8196154a6b892c3cc3e6ac6efc99bdeb7257d35338cf0ec7a508577b

Observation ff5f70e8-f35d-4624-a746-ac48d11cbb6e · outbound

This paper cites Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,

Reference 10

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raw_fallback, observed 2026-08-06T14:44:28.579579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.358648Z digest=sha256:c4e24d3ef5a0a22976b0613f5cd0f83cca87a23da5d5c79e0cd534df9e798c73

Observation 94b1e150-74fd-4c11-80c0-749d256e3804 · outbound

This paper cites Sliced wasserstein discrepancy for unsupervised domain adaptation,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Sliced wasserstein discrepancy for unsupervised domain adaptation,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.571957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.360866Z digest=sha256:73d85420d3a23b318fbc6386f4c2f4c6fc21c7b5de8847de9ca4935d4f3d7dc0

Observation 2f94e9c8-bb45-4f98-885d-0fda6cd08d9d · outbound

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

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Mic: Masked image consistency for context-enhanced domain adaptation,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.564236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.363103Z digest=sha256:aabf5be6374512508a37c8d5ae24bbe491458bcbf6dc1a559efce8ab3720c455

Observation 21633f88-dc87-4ff3-aad1-a5b1c9166402 · outbound

This paper cites Pseudolabel guided pixels contrast for domain adaptive semantic segmentation,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Pseudolabel guided pixels contrast for domain adaptive semantic segmentation,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.556310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.365359Z digest=sha256:0258c96c14b62a30fc7856938a668092b08a86bcc01130012fca57687604b8cb

Observation 0ae238e4-62cc-482f-a40b-41c1a6e35660 · outbound

This paper cites Unsupervised domain adaptation for semantic segmentation with pseudo label self-refinement,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Unsupervised domain adaptation for semantic segmentation with pseudo label self-refinement,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.548223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.367630Z digest=sha256:bdc74109003130f0c66163b953e6ceb86e18c300f4858219f1d78fb77803bf63

Observation 74000e8f-9722-47ae-a26d-d20b9cd10dea · outbound

This paper cites Feature pyramid networks for object detection,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Feature pyramid networks for object detection,

Reference 15

Resolution
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raw_fallback, observed 2026-08-06T14:44:28.540454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.369768Z digest=sha256:34727129fb1103153f340a9a951ec24f415168204b9d8926e51699e06b9df7b4

Observation a7b32381-bac3-4300-a35f-26a5230c803c · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,

Reference 16

Resolution
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raw_fallback, observed 2026-08-06T14:44:28.533060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.371786Z digest=sha256:41f90dc559d7d51e88f5dfafabc22f8614734ab872cce990f1d5cb35faa2d835

Observation cbd2d4e6-da2c-4889-a682-e40f07e07053 · outbound

This paper cites Deep high-resolution representation learning for visual recognition,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Deep high-resolution representation learning for visual recognition,

Reference 17

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raw_fallback, observed 2026-08-06T14:44:28.525362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.374117Z digest=sha256:55db5d3d6b2b8646f08b121d25b7a04c8935361f5ef0d7330d3a07a419f65911

Observation 2781f90c-2253-4d34-a938-7fdec5706012 · outbound

This paper cites Cbam: Convolutional block attention module,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Cbam: Convolutional block attention module,

Reference 18

Resolution
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raw_fallback, observed 2026-08-06T14:44:28.518016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.376410Z digest=sha256:ccb02ff5f8cdd2deaabfe09bbe09cf8bd856680e8b22cc6a54342bd2a6d061a0

Observation 47cf0b7b-c8f5-4456-ac7b-01d0ed9bf380 · outbound

This paper cites Squeeze-and-excitation networks,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Squeeze-and-excitation networks,

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T14:44:28.378722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:44:28.378722Z digest=sha256:82defde8d291d22627c1aa6d370af896a1985890ce4c6e88ca9efc7cdf00d30a

Observation 4bb1dc03-3e1d-4fe9-8e48-6d42d6b8d605 · outbound

This paper cites Gated-scnn: Gated shape cnns for semantic segmentation,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Gated-scnn: Gated shape cnns for semantic segmentation,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.505762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.381006Z digest=sha256:2952be3636466295a83becc081bbe5ab19b912188d13bad8bb7ac52d60c96e15

Observation 892efaeb-a9b2-4afa-84c0-8a54e3045470 · outbound

This paper cites Rethinking bisenet for real-time semantic segmentation,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Rethinking bisenet for real-time semantic segmentation,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.498741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.383299Z digest=sha256:d62950177b6f9178041b7e9fcf00a9e71c9b45ffa12e811e720891ae3b961c60

Observation 5560f853-c8e8-40d8-a5e6-d1c7e6a1c75a · outbound

This paper cites Uncertainty-aware pseudo- label filtering for source-free unsupervised domain adaptation,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Uncertainty-aware pseudo- label filtering for source-free unsupervised domain adaptation,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.491563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.385465Z digest=sha256:b6ab551000914a9120724620fa49a03694a63d3fad1c36727ef710ae191c7dbf

Observation 7737b283-ecb6-46aa-89b1-60f4d8298adc · outbound

This paper cites Ga-nav: Efficient terrain segmentation for robot navigation in unstructured outdoor environments,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Ga-nav: Efficient terrain segmentation for robot navigation in unstructured outdoor environments,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.484141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.387609Z digest=sha256:69086d95ce4e7267ca17a399228c86e4bd97a508279fa8c5dcccb2d2f57c97f6

Observation cfeb5d99-1b34-4a2f-a690-1ee2eb19ce8b · outbound

This paper cites TNS: Terrain Traversability Mapping and Navigation System for Autonomous Excavators.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation TNS: Terrain Traversability Mapping and Navigation System for Autonomous Excavators

Reference 24

Resolution
verified exact
local_arxiv, observed 2026-08-06T14:44:28.438722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.389849Z digest=sha256:f8f34c448613f214a15f419474a8f2a26ba9c66cb5bc5ddfee67a76fc6dff1bd

Observation cb9fcc12-25a1-4292-92ed-3921eda5a4d0 · outbound

This paper cites SALON: Self-supervised Adaptive Learning for Off-road Navigation.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation SALON: Self-supervised Adaptive Learning for Off-road Navigation

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-06T14:44:28.392601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:44:28.392601Z digest=sha256:337f5ccac700d98e62fe91fa00f5145c843acc96e29bca5fea60750bfe06fcc3

Observation 5e329aec-3d84-4b8c-883e-1d862efe1099 · outbound

This paper cites Classmix: Segmentation-based data augmentation for semi-supervised learning,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Classmix: Segmentation-based data augmentation for semi-supervised learning,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.476510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.395174Z digest=sha256:886eee4c3a9196738ae5b64ff6265694c9e7d412df4d4e4ea6a4fa25f77b11ed

Observation d7c45281-2fd5-4202-8b4c-54abc41702c5 · outbound

This paper cites Povnav: A pareto-optimal mapless visual navigator,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Povnav: A pareto-optimal mapless visual navigator,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.468996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.397303Z digest=sha256:ec5e18a54524e6748ed79f45ecd139b333d413d328144dcffda6c62156b1110b

Observation fbadec3a-f53f-40f7-a8e7-7189579b3e2b · outbound

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

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Dacs: Domain adaptation via cross-domain mixed sampling,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.461639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.399565Z digest=sha256:87720cfea8fe935f73ac52195edb8737f0502837db6b2243ae68e90c39d36c5f

Observation f38c8873-a80f-4705-96f7-5cef24ec39f4 · outbound

This paper cites Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.454189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.401854Z digest=sha256:a1f49aa72d4c5758efa3e4048cbb6ad1b3c8f9fd075006f4a611b2f847cdaf81

Observation 264b577a-e2e4-4246-a92e-0eb7ea74c86f · outbound

This paper cites Extended receptive field uda semantic segmentation based on spatial alignment and knowledge distillation,.

AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Extended receptive field uda semantic segmentation based on spatial alignment and knowledge distillation,

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-06T14:44:28.446638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T14:44:28.404327Z digest=sha256:7e485efbc845032d35fe41aac7c6d93f1bb895b7661dce88e129f50398b3c9a4

Pith citing papers

No inbound Pith citation observations are available.