Paper Citation Record · LEDGER

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation

As of 18 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2607.03068.

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

pith.paper-citation-record.v1
2607.03068 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T05:07:47.077399Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:39:12.146803Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T23:39:12.175553Z

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy0
unresolved37
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 825c4c32-a768-4f5e-a4f3-7e66de252d98 · outbound

This paper cites an unresolved cited work.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2467164-5de9-4a40-8e70-37b7ff434242 · outbound

This paper cites Learning imbalanced datasets with label- distribution-aware margin loss.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Learning imbalanced datasets with label- distribution-aware margin loss

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9e83dff-4e07-454f-b5a8-151387479306 · outbound

This paper cites Softmatch: Addressing the quantity-quality tradeoff in semi- supervised learning.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Softmatch: Addressing the quantity-quality tradeoff in semi- supervised learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03f49a6f-7bb6-40e8-a82c-c9e0449be47d · outbound

This paper cites Semi-supervised semantic segmentation needs strong, varied perturbations.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Semi-supervised semantic segmentation needs strong, varied perturbations

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c38e035-9230-403f-8de8-37190fade14a · outbound

This paper cites Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2caf7f9a-7cae-4038-95bf-435b6b33310e · outbound

This paper cites Deep residual learning for image recognition.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Deep residual learning for image recognition

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a510cf7-f173-427e-8bfc-d5de6fca2313 · outbound

This paper cites Re-distributing biased pseudo labels for semi-supervised semantic segmentation: A baseline investigation.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Re-distributing biased pseudo labels for semi-supervised semantic segmentation: A baseline investigation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 094af108-caf7-47a6-8c63-d9d4db84a995 · outbound

This paper cites Beyond pixels: Semi-supervised semantic segmenta- tion with a multi-scale patch-based multi-label classifier.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Beyond pixels: Semi-supervised semantic segmenta- tion with a multi-scale patch-based multi-label classifier

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e2f580d-5fc2-465f-ab6d-033d7fcd1d23 · outbound

This paper cites SemiVL: Semi-supervised semantic segmenta- tion with vision-language guidance.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation SemiVL: Semi-supervised semantic segmenta- tion with vision-language guidance

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05b80ae6-6fdc-4232-9fa1-b1f94d6b8f6b · outbound

This paper cites Semi-supervised semantic segmentation via adaptive equalization learning.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Semi-supervised semantic segmentation via adaptive equalization learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d178719e-c374-49dc-9cd8-35d287d77a95 · outbound

This paper cites Semi-supervised semantic segmentation via gentle teaching assistant.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Semi-supervised semantic segmentation via gentle teaching assistant

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 689a2535-30ce-4417-ac57-1518613daf59 · outbound

This paper cites CAFS: Class Adaptive Framework for Semi-Supervised Semantic Segmentation.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation CAFS: Class Adaptive Framework for Semi-Supervised Semantic Segmentation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52769978-5d15-46ac-a5ce-b3180e2a1cee · outbound

This paper cites Supervised contrastive learning.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Supervised contrastive learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a7f656e-da7f-4ab7-89ce-cc59ab9952b1 · outbound

This paper cites Ro- bust pseudo-label learning for semantic segmentation: An encoding perspective.arXiv preprint arXiv:2512.06870,.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Ro- bust pseudo-label learning for semantic segmentation: An encoding perspective.arXiv preprint arXiv:2512.06870,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e687feb5-ed1c-4627-b1e8-ae46306ccc68 · outbound

This paper cites an unresolved cited work.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cd77941-ed7e-4bdc-b2aa-34771f6487ea · outbound

This paper cites Perturbed and strict mean teachers for semi-supervised semantic segmenta- tion.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Perturbed and strict mean teachers for semi-supervised semantic segmenta- tion

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 949a8526-c6f3-409b-9cd2-e1e1242c98a6 · outbound

This paper cites Improving semi-supervised semantic segmentation with sliced-wasserstein feature alignment and uniformity.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Improving semi-supervised semantic segmentation with sliced-wasserstein feature alignment and uniformity

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98d87ba7-d5d4-4b27-98f9-92ed3001d418 · outbound

This paper cites RankMatch: Exploring the better consistency regularization for semi-supervised semantic segmentation.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation RankMatch: Exploring the better consistency regularization for semi-supervised semantic segmentation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78acc956-46c2-47e7-8686-eb466e75108a · outbound

This paper cites V o, Marc Szafraniec, et al.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation V o, Marc Szafraniec, et al

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ed9255e-6501-4b34-baf6-b411ec1aea7c · outbound

This paper cites Vi- sion transformers for dense prediction.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Vi- sion transformers for dense prediction

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3b9ffab-f164-4a66-8adf-7bb1df283709 · outbound

This paper cites PrevMatch: Revisiting and maximizing temporal knowledge in semi-supervised semantic segmenta- tion.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation PrevMatch: Revisiting and maximizing temporal knowledge in semi-supervised semantic segmenta- tion

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 735458e2-c64b-45af-b467-ddc3ec170d55 · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2baca6d7-56b5-48cb-a8cd-32ba93ed2dd2 · outbound

This paper cites DAW: Exploring the better weighting function for semi-supervised semantic segmentation.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation DAW: Exploring the better weighting function for semi-supervised semantic segmentation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a04f8ba-38a4-433a-8960-d62d13596ed5 · outbound

This paper cites CorrMatch: Label propagation via correlation matching for semi-supervised se- mantic segmentation.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation CorrMatch: Label propagation via correlation matching for semi-supervised se- mantic segmentation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84ab47b0-c0ee-47a7-b013-7ccb8b410780 · outbound

This paper cites CW-BASS: Confidence-weighted boundary-aware learning for semi-supervised semantic segmentation.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation CW-BASS: Confidence-weighted boundary-aware learning for semi-supervised semantic segmentation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 231ebb8d-d1bb-43ef-9a04-df83749ef003 · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5ce9b08-4a82-4861-a569-bea025190cb4 · outbound

This paper cites Seesaw loss for long- tailed instance segmentation.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Seesaw loss for long- tailed instance segmentation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d97651d-dc2b-425a-af82-c7c537814a45 · outbound

This paper cites Towards the uncharted: Density-descending feature perturbation for semi-supervised semantic segmentation.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Towards the uncharted: Density-descending feature perturbation for semi-supervised semantic segmentation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c9f2d45-42f1-48c7-8c1e-f9685e884159 · outbound

This paper cites Semi-supervised semantic segmentation using unreliable pseudo-labels.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Semi-supervised semantic segmentation using unreliable pseudo-labels

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6477392f-1cd7-4009-a917-c0c1fe1f3ca0 · outbound

This paper cites Freematch: Self-adaptive thresholding for semi-supervised learning.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Freematch: Self-adaptive thresholding for semi-supervised learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2499fd39-52c7-4420-9f9a-f17b6f3506fa · outbound

This paper cites CReST: A class-rebalancing self-training frame- work for imbalanced semi-supervised learning.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation CReST: A class-rebalancing self-training frame- work for imbalanced semi-supervised learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abf9368b-d646-4506-92a2-3b619cddce24 · outbound

This paper cites Allspark: Reborn labeled features from unlabeled in trans- former for semi-supervised semantic segmentation.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Allspark: Reborn labeled features from unlabeled in trans- former for semi-supervised semantic segmentation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d07f8eb-6cf8-411f-bf64-617a5f27a0f5 · outbound

This paper cites St++: Make self-training work better for semi-supervised se- mantic segmentation.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation St++: Make self-training work better for semi-supervised se- mantic segmentation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff44dd93-db26-4ce0-945d-214242b87bfa · outbound

This paper cites Revisiting weak-to-strong consistency in semi-supervised semantic segmentation.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Revisiting weak-to-strong consistency in semi-supervised semantic segmentation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f4ed6b2-70e7-481e-a9de-b03c4eda5dd2 · outbound

This paper cites UniMatch V2: Pushing the limit of semi- supervised semantic segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation UniMatch V2: Pushing the limit of semi- supervised semantic segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06417ae9-e1d9-47cd-9ab3-b7b51dd121a5 · outbound

This paper cites Flexmatch: Boosting semi-supervised learning with curricu- lum pseudo labeling.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Flexmatch: Boosting semi-supervised learning with curricu- lum pseudo labeling

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85446d5b-bc0d-44ea-b104-793ab0c1f933 · outbound

This paper cites Instance-specific and model-adaptive supervision for semi-supervised semantic segmentation.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation Instance-specific and model-adaptive supervision for semi-supervised semantic segmentation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5f04651-e4fc-448f-9d78-231787c4ab99 · outbound

This paper cites rare classes have lower confidence and should be given lower thresholds to admit more of their pseudo-labels.

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation rare classes have lower confidence and should be given lower thresholds to admit more of their pseudo-labels

Reference 38

Resolution: malformed identifier; no resolver link, observed 2026-07-12T05:07:47.077399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 3bf27dfc-a081-4a76-803f-ed8099cfd0c7 · inbound

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers cites this paper.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:39:12.181503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.