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Paper Citation Record · LEDGER

Prompting Diffusion Representations for Cross-Domain Semantic Segmentation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2307.02138.

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

pith.paper-citation-record.v1
2307.02138 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:14:41.207368Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T23:47:15.685184Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6ef3a029-8e56-492e-bf64-743b83a9bca0 · inbound

Segment Any-Quality Images with Generative Latent Space Enhancement cites this paper.

Segment Any-Quality Images with Generative Latent Space Enhancement Prompting Diffusion Representations for Cross-Domain Semantic Segmentation

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-22T23:47:15.688542Z

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.

source=pdf_text observed=2026-05-22T23:45:38.970279Z digest=sha256:86a07be69fe23c71aa181769fc375459b673db20fd508e2c3317b03ee3543632

Observation 6a666828-d394-4af0-824b-718b6f47c4aa · inbound

Knowledge-Aligned Counterfactual-Enhancement Diffusion Perception for Unsupervised Cross-Domain Visual Emotion Recognition cites this paper.

Knowledge-Aligned Counterfactual-Enhancement Diffusion Perception for Unsupervised Cross-Domain Visual Emotion Recognition Prompting Diffusion Representations for Cross-Domain Semantic Segmentation

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T14:14:41.207368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:14:41.207368Z digest=sha256:8184b983b1281b1ecea11c6d987c4edab21663dc5e04564be74ef03a82756e9e

Observation 3c822dac-8f4c-47e9-a65b-9a5872fb6bc3 · inbound

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation cites this paper.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Prompting Diffusion Representations for Cross-Domain Semantic Segmentation

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T12:56:59.804675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:56:59.804675Z digest=sha256:6f6d2c2b0ce550c1fd2564e453c2ac753a757d88ab0fed0161632aa7c96b706b

Observation 367731b2-d081-4f21-a056-6500f079cd96 · inbound

IELDG: Suppressing Domain-Specific Noise with Inverse Evolution Layers for Domain Generalized Semantic Segmentation cites this paper.

IELDG: Suppressing Domain-Specific Noise with Inverse Evolution Layers for Domain Generalized Semantic Segmentation Prompting Diffusion Representations for Cross-Domain Semantic Segmentation

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-05T15:44:40.047948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:44:40.047948Z digest=sha256:80a815f00ad540f6a8047dd077396d75a5c1a27433612b672e8b17991010d005

Observation c3367e9d-9660-4ea8-9914-ac542b7e1a56 · inbound

Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement cites this paper.

Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement Prompting Diffusion Representations for Cross-Domain Semantic Segmentation

Reference 64

Resolution
metadata mismatch
arxiv_id, observed 2026-05-16T18:13:13.226817Z

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.

source=pdf_text observed=2026-05-16T18:11:47.141366Z digest=sha256:1aba319924ea1162f56656652cb2da5b29621e7d05642e51ec6a5e5cf96b9803