Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:55:27.478208Z
Paper Citation Record · LEDGER
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation
As of 10 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2506.11142.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
49 of 49 outbound references displayed
- verified exact1
- verified fuzzy44
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 60d46d35-7ea6-4d6c-92c0-001cdd503f3a · outbound
This paper cites ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.215739Z
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.
Observation a7d77658-734d-4961-85ad-37106f5eece5 · outbound
This paper cites Perturbed and strict mean teachers for semi-supervised semantic segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Perturbed and strict mean teachers for semi-supervised semantic segmentation,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.202104Z
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.
Observation fe5f654a-3d77-4e10-b1d2-b8230cbad5c7 · outbound
This paper cites Revisiting Weak-to-Strong Consistency in Semi-Supervised Se- mantic Segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Revisiting Weak-to-Strong Consistency in Semi-Supervised Se- mantic Segmentation,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.187889Z
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.
Observation be24d83f-1793-4fc7-a8bd-ba6d459099f1 · outbound
This paper cites Reco: Retrieve and co-segment for zero-shot transfer,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Reco: Retrieve and co-segment for zero-shot transfer,
Reference 4
- Resolution
- verified fuzzy
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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.
Observation b587d2fc-caa8-4049-b42c-a3134400916a · outbound
This paper cites CW-BASS: Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic Segmentation.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation CW-BASS: Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic Segmentation
Reference 5
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d20cd3da-eed1-4fdb-a65f-dd15cfaac6db · outbound
This paper cites Semisupervised semantic segmentation with cross pseudo super- vision,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semisupervised semantic segmentation with cross pseudo super- vision,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.161477Z
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.
Observation 31991598-70d9-42f4-b10a-981eceda7a6c · outbound
This paper cites Fixmatch: Simplifyingsemi-supervisedlearningwithconsistency and confidence,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Fixmatch: Simplifyingsemi-supervisedlearningwithconsistency and confidence,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.147834Z
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.
Observation fdf5f02b-d1c9-4438-a128-68f9590064c4 · outbound
This paper cites PseudoSeg: Designing Pseudo Labels for Semantic Segmentation.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:55:27.287722Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4ff92a35-d709-44bf-ad0e-863a6d20f95e · outbound
This paper cites Semi-supervised semantic segmentation with directional context- aware consistency,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation with directional context- aware consistency,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.135064Z
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.
Observation 3d5c628a-3645-410b-b683-49f9388f6c8d · outbound
This paper cites Semi-supervised semantic segmentation with error lo- calization network,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation with error lo- calization network,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.121763Z
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.
Observation 64dca4f0-c729-4bcd-98cc-f38f8b64462e · outbound
This paper cites Semi-supervisedsemanticsegmentationusingunreliablepseudo- labels,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-supervisedsemanticsegmentationusingunreliablepseudo- labels,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.107988Z
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.
Observation d422db30-1561-4817-ba0b-731c1c8cc9bf · outbound
This paper cites Pseudo-Label: The Simple and Efficient Semi-Supervised Learn- ing Method for Deep Neural Networks,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Pseudo-Label: The Simple and Efficient Semi-Supervised Learn- ing Method for Deep Neural Networks,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.095116Z
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.
Observation 21356734-fcd4-4d8a-a1e5-ffefd2128752 · outbound
This paper cites Temporal Ensembling for Semi-Supervised Learning.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Temporal Ensembling for Semi-Supervised Learning
Reference 13
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7aee4427-9abb-4481-be9a-5dd5cb74d7b6 · outbound
This paper cites Deep residual learning for image recognition,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Deep residual learning for image recognition,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.081251Z
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.
Observation 8576eadd-5c53-4888-97af-a8898ddc9aeb · outbound
This paper cites Imagenet: A large-scale hierarchical image database,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Imagenet: A large-scale hierarchical image database,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.067056Z
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.
Observation 267e2e6e-6415-4e6e-9a28-23d411997293 · outbound
This paper cites RepUNet: A fast image semantic segmentation model based on convolutional reparameterization of ship satellite images,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation RepUNet: A fast image semantic segmentation model based on convolutional reparameterization of ship satellite images,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.053428Z
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.
Observation 8971b05a-03cb-4238-94c3-7e62a983f7dc · outbound
This paper cites Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.039988Z
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.
Observation a98234d9-2ee2-4ce3-b9c8-6bbe6af6c655 · outbound
This paper cites DARS: Data Augmentation using Refined Segmentation on Computer Vision Tasks,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation DARS: Data Augmentation using Refined Segmentation on Computer Vision Tasks,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.026498Z
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.
Observation 35df6ee3-e8ed-4cd9-8a90-ab3dc9d8ba9b · outbound
This paper cites Semi-supervised semantic segmentation via adaptive equalization learning,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation via adaptive equalization learning,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:28.012409Z
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.
Observation 0b1d3c26-c44c-48b1-881b-eb687e650721 · outbound
This paper cites UCC: Uncertainty Guided Cross-Head Co-Training for Semi- Supervised Semantic Segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation UCC: Uncertainty Guided Cross-Head Co-Training for Semi- Supervised Semantic Segmentation,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.998291Z
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.
Observation 0f4dcb92-6921-4e12-8838-5e637b947f88 · outbound
This paper cites Instance-Specific and Model-Adaptive Supervision for Semi- Supervised Semantic Segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Instance-Specific and Model-Adaptive Supervision for Semi- Supervised Semantic Segmentation,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.984865Z
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.
Observation 02a89447-35c0-4cd1-8750-0ea20275f136 · outbound
This paper cites Semi-Supervised Semantic Segmentation with Cross-Consistency Training,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-Supervised Semantic Segmentation with Cross-Consistency Training,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.970467Z
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.
Observation 364798b9-5108-4847-b148-8d924f21d24e · outbound
This paper cites Multi-Granularity Distillation Scheme Towards Lightweight Semi- Supervised Semantic Segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Multi-Granularity Distillation Scheme Towards Lightweight Semi- Supervised Semantic Segmentation,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.956019Z
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.
Observation f02dc4ec-93d8-4f4b-b10c-16cef2b83c60 · outbound
This paper cites Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.941542Z
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.
Observation 37c0ef04-b5b5-4c73-a4be-b4befe8e2559 · outbound
This paper cites Deeplab: Semantic image segmentation with deep convolu- tional nets, atrous convolution, and fully connected crfs,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Deeplab: Semantic image segmentation with deep convolu- tional nets, atrous convolution, and fully connected crfs,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.926888Z
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.
Observation 8931511f-d443-4947-864f-6b84e3f6b203 · outbound
This paper cites A simple framework for contrastive learning of visual representa- tions,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation A simple framework for contrastive learning of visual representa- tions,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.912498Z
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.
Observation 0e6ee5a7-880b-49c7-8555-f13a0ffdf202 · outbound
This paper cites C3-SemiSeg: Contrastive Semi-supervised Segmentation via Cross-set Learning and Dynamic Class-Balancing,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation C3-SemiSeg: Contrastive Semi-supervised Segmentation via Cross-set Learning and Dynamic Class-Balancing,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.897730Z
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.
Observation bba984ed-206a-410e-af64-7956ff7b104d · outbound
This paper cites ST++: Makeself-trainingworkbetterforsemi-supervisedsemantic segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation ST++: Makeself-trainingworkbetterforsemi-supervisedsemantic segmentation,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.883439Z
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.
Observation df349928-b54e-495e-a0b9-95b53905c9aa · outbound
This paper cites Semi-supervisedsemanticsegmentationusingunreliablepseudo- labels,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-supervisedsemanticsegmentationusingunreliablepseudo- labels,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.868965Z
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.
Observation 5e6a34cb-fc42-449c-9aa3-32a09ccda365 · outbound
This paper cites Semi-supervised semantic segmentation with generalized task- aware consistency,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation with generalized task- aware consistency,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.854432Z
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.
Observation dd82b994-ceb7-4c07-ad11-5c7bf46be6d4 · outbound
This paper cites Semi-supervised semantic segmentation with pixel contrastive consistency,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation with pixel contrastive consistency,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.839740Z
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.
Observation 7ff5c514-0811-419b-875e-91fd195e564b · outbound
This paper cites AugSeg: Asimpledataaugmentationmethodforsemi-supervised semantic segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation AugSeg: Asimpledataaugmentationmethodforsemi-supervised semantic segmentation,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.825457Z
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.
Observation ab3a532b-50ed-465c-98f2-d0e7086099c1 · outbound
This paper cites Diverse co-training for semi-supervised semantic segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Diverse co-training for semi-supervised semantic segmentation,
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.810981Z
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.
Observation f3982fad-91cf-48c8-bf4b-d5102b6b35da · outbound
This paper cites Semi-supervisedsemanticsegmentationwithentropy-basedsam- ple learning,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-supervisedsemanticsegmentationwithentropy-basedsam- ple learning,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.796142Z
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.
Observation a5adb45d-2230-45e2-a2d4-d49a2218a733 · outbound
This paper cites Semi-supervised semantic segmentation with logical diagnosis,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation with logical diagnosis,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.781503Z
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.
Observation 0ce7c296-9a03-479d-8543-629887f39fae · outbound
This paper cites Semi-supervised semantic segmentation with dual adaptive weight- ing,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation with dual adaptive weight- ing,
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.766500Z
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.
Observation 4354825a-58b5-488b-b0de-8e8c5d0ecca6 · outbound
This paper cites Semi-supervisedsemanticsegmentationwithdynamicdualfeature propagation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-supervisedsemanticsegmentationwithdynamicdualfeature propagation,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.751425Z
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.
Observation be9edbbe-8ec7-4951-b6c1-4efcb620ce9d · outbound
This paper cites Semi-supervised semantic segmentation with correspondence matching,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation with correspondence matching,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.737005Z
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.
Observation ac200793-d31f-4656-aa84-578375007a0f · outbound
This paper cites UniMatch: Unified semi-supervised learning for classification and segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation UniMatch: Unified semi-supervised learning for classification and segmentation,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.722749Z
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.
Observation e7b8a8ca-229b-482c-a695-9428704dcd03 · outbound
This paper cites The Pascal Visual Object Classes Challenge: A Retrospective,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation The Pascal Visual Object Classes Challenge: A Retrospective,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.708381Z
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.
Observation 4441e875-022c-4d3b-b9fa-47a736f56c07 · outbound
This paper cites The Cityscapes Dataset for Semantic Urban Scene Understanding,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation The Cityscapes Dataset for Semantic Urban Scene Understanding,
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.693170Z
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.
Observation e0e8816e-fbb2-4111-a757-fb96f46fcb0f · outbound
This paper cites MicrosoftCOCO:CommonObjectsinContext,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation MicrosoftCOCO:CommonObjectsinContext,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.677372Z
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.
Observation 09363c4b-5774-44e7-a6d7-abe9829ccd21 · outbound
This paper cites Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.660842Z
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.
Observation 93f41c37-0481-4138-bb58-6b9fa34100f9 · outbound
This paper cites Semi-supervised semantic segmentation needs strong, varied perturbations.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation needs strong, varied perturbations
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:55:27.455028Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5bdb446-ef6b-4770-be3c-0b7b1438bfcd · outbound
This paper cites Guided Collaborative Training for Pixel-wise Semi-Supervised Learning.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Guided Collaborative Training for Pixel-wise Semi-Supervised Learning
Reference 45
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T04:55:27.523272Z
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.
Observation e00113ba-3116-4e92-90fb-21888743df12 · outbound
This paper cites Learn- ing from future: A novel self-training framework for semantic segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Learn- ing from future: A novel self-training framework for semantic segmentation,
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.645390Z
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.
Observation a42a7f66-c711-4f59-bd32-0a7dd1b846ab · outbound
This paper cites Fuzzy positive learning for semi-supervised semantic segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Fuzzy positive learning for semi-supervised semantic segmentation,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.630303Z
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.
Observation 8b08d2fc-42ce-4567-8183-693a6277b424 · outbound
This paper cites Semantic contours from inverse detectors,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Semantic contours from inverse detectors,
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.614192Z
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.
Observation 6359ff5f-e4d9-4efd-94d4-92ae90849a0a · outbound
This paper cites Conservative-progressive collaborative learning for semi-supervised semantic segmentation,.
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation Conservative-progressive collaborative learning for semi-supervised semantic segmentation,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:55:27.598541Z
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.
Pith citing papers
No inbound Pith citation observations are available.