Paper Citation Record · LEDGER

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation

As of 17 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 2 inbound Pith citation observations for arXiv:2505.07691.

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

pith.paper-citation-record.v1
2505.07691 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

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measured 59 of 59 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:39:12.054368Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T23:39:12.359086Z

Reference resolution

57 of 57 outbound references displayed

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External citation measurements

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Outbound references

Observation 9f35dae7-899d-4a9e-85c0-ad3ee4da2b32 · outbound

This paper cites 2017 Robotic Instrument Segmentation Challenge.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation 2017 Robotic Instrument Segmentation Challenge

Reference 1

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Observation 1c41266f-e831-4839-bda2-46e9ea35b95f · outbound

This paper cites Bidirectional copy-paste for semi-supervised medical image segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Bidirectional copy-paste for semi-supervised medical image segmentation

Reference 2

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Observation ec6bae67-b619-4a73-9097-276549bb4673 · outbound

This paper cites Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging, 37 (11):2514–2525, 2018.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging, 37 (11):2514–2525, 2018

Reference 3

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Observation 41e9b777-721b-4cd3-9665-9c022a18cf39 · outbound

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

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 4

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Observation 48c82d76-0743-442d-a1a1-254b2c48fc57 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 5

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Observation 1a3b226e-3d2c-441c-b425-f44ec6a26d62 · outbound

This paper cites Semi-supervised domain adaptation based on dual-level domain mixing for semantic segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Semi-supervised domain adaptation based on dual-level domain mixing for semantic segmentation

Reference 6

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Observation 8db57ccd-909b-416a-ba6c-38400507dac6 · outbound

This paper cites Semi-supervised semantic segmentation with cross pseudo supervision.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation with cross pseudo supervision

Reference 7

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Observation b0ddc359-ef92-4f61-84ad-015296d92213 · outbound

This paper cites Mask-based Data Augmentation for Semi-supervised Semantic Segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Mask-based Data Augmentation for Semi-supervised Semantic Segmentation

Reference 8

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Observation 930e82d8-fe7b-43fd-8935-24a04aa97a77 · outbound

This paper cites Interactive network perturbation between teacher and stu- dents for semi-supervised semantic segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Interactive network perturbation between teacher and stu- dents for semi-supervised semantic segmentation

Reference 9

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Observation 75f7e136-2ffb-4daf-9bcd-474afc58260a · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Imagenet: A large-scale hierarchical image database

Reference 10

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This paper cites Dmt: Dynamic mutual training for semi-supervised learning.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Dmt: Dynamic mutual training for semi-supervised learning

Reference 11

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Observation 167b6841-5a9a-463a-8a87-ca74653197e1 · outbound

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

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation needs strong, varied perturbations

Reference 12

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Observation 5fcb0143-910d-473a-acb1-f5af6a67ae33 · outbound

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

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Dsp: Dual soft-paste for unsupervised domain adaptive semantic segmentation

Reference 13

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Observation d2b31848-8f52-47a0-9b2e-eb56839bfa1c · outbound

This paper cites Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection

Reference 14

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Observation 96e51767-40e0-40b9-809f-3bf89a2266f9 · outbound

This paper cites Simple copy-paste is a strong data augmentation method for instance segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Simple copy-paste is a strong data augmentation method for instance segmentation

Reference 15

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Observation beb24c70-c4eb-4cda-ac1a-37ce58c2c3e0 · outbound

This paper cites Deep residual learning for image recognition.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Deep residual learning for image recognition

Reference 16

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Observation a915d978-c854-4dd4-b857-476bd9fbc8b9 · outbound

This paper cites Mask r-cnn.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Mask r-cnn

Reference 17

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Observation 98563ed7-4eef-4032-9482-db487d2ea35d · outbound

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

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation via adaptive equalization learning

Reference 18

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Observation 7ae16dc4-2076-49a8-ab76-9e441b631e4d · outbound

This paper cites Universal semi-supervised semantic segmenta- tion.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Universal semi-supervised semantic segmenta- tion

Reference 19

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Observation f182508d-9ee1-42f1-922a-89741f654b1c · outbound

This paper cites Guided collaborative training for pixel-wise semi-supervised learning.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Guided collaborative training for pixel-wise semi-supervised learning

Reference 20

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Observation 5c335d79-e5f3-444e-a974-e401fad7d2bb · outbound

This paper cites Semi-supervised semantic seg- mentation with directional context-aware consistency.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Semi-supervised semantic seg- mentation with directional context-aware consistency

Reference 21

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Observation dd936eab-38a9-4e56-ba5b-185a4707cb0a · outbound

This paper cites Temporal Ensembling for Semi-Supervised Learning.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Temporal Ensembling for Semi-Supervised Learning

Reference 22

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Observation e9b0a53e-cb27-46fd-a16c-494a93abffdd · outbound

This paper cites Pseudo-label: The simple and effi- cient semi-supervised learning method for deep neural net- works.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Pseudo-label: The simple and effi- cient semi-supervised learning method for deep neural net- works

Reference 23

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Observation 059cf204-b98a-49e4-9708-24845e4fa849 · outbound

This paper cites Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization

Reference 24

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Observation 756f4c5a-104f-450f-97a4-d0e44ec9db05 · outbound

This paper cites Bidirectional learning for domain adaptation of semantic segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Bidirectional learning for domain adaptation of semantic segmentation

Reference 25

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Observation a3ceed25-3bb5-4aa6-a443-b789c4fae148 · outbound

This paper cites Microsoft coco: Common objects in context.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Microsoft coco: Common objects in context

Reference 26

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Observation 562eccb0-8b35-4181-8de2-34e1bde9ebd1 · outbound

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Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Ms- net: Multi-site network for improving prostate segmentation with heterogeneous mri data

Reference 27

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Observation aba4d80a-5e42-4c01-ae89-f48d7061c516 · outbound

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

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Perturbed and strict mean teachers for semi-supervised semantic segmenta- tion

Reference 28

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Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Fully convolutional networks for semantic segmentation

Reference 29

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This paper cites Switching temporary teachers for semi-supervised semantic segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Switching temporary teachers for semi-supervised semantic segmentation

Reference 30

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Observation 62e1643a-55d8-43e6-b46d-765a0f06543f · outbound

This paper cites Classmix: Segmentation-based data aug- mentation for semi-supervised learning.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Classmix: Segmentation-based data aug- mentation for semi-supervised learning

Reference 31

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This paper cites Semi- supervised semantic segmentation with cross-consistency training.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Semi- supervised semantic segmentation with cross-consistency training

Reference 32

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This paper cites Perone, Pedro Ballester, Rodrigo C.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Perone, Pedro Ballester, Rodrigo C

Reference 33

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Observation 9728ca7c-ec6c-43fd-81e4-155731711f33 · outbound

This paper cites Regularization with stochastic transformations and perturba- tions for deep semi-supervised learning.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Regularization with stochastic transformations and perturba- tions for deep semi-supervised learning

Reference 34

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

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 35

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This paper cites Corrmatch: Label propagation via correlation matching for semi-supervised semantic segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Corrmatch: Label propagation via correlation matching for semi-supervised semantic segmentation

Reference 36

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Observation 54884074-61bf-4eb7-b806-89797e83d29d · outbound

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

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 37

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

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Dacs: Domain adaptation via cross- domain mixed sampling

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.957291Z

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This paper cites Unsupervised semantic seg- mentation by contrasting object mask proposals.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Unsupervised semantic seg- mentation by contrasting object mask proposals

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.945460Z

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This paper cites Su- dre, Mark S.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Su- dre, Mark S

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.933066Z

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This paper cites Exploring cross-image pixel contrast for semantic segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Exploring cross-image pixel contrast for semantic segmentation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.921131Z

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Observation 1b6864fa-d7a5-4a51-8367-8a5517503f47 · outbound

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

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation using unreliable pseudo-labels

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.909113Z

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Observation f13f9296-e899-499f-98f9-7fd5f3919434 · outbound

This paper cites Mcf: Mutual correction framework for semi- supervised medical image segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Mcf: Mutual correction framework for semi- supervised medical image segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.896085Z

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Observation 533b726d-ce0a-4f29-b0f2-357547ca2de4 · outbound

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

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Crest: A class-rebalancing self-training frame- work for imbalanced semi-supervised learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.882947Z

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Observation 0aa93196-6881-4a11-9f07-2ca9700d6e58 · outbound

This paper cites Unsupervised data augmentation for consistency training.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Unsupervised data augmentation for consistency training

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.869302Z

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Observation ea49d3f4-8a09-4fce-9142-daed870deaa4 · outbound

This paper cites A global benchmark of algo- rithms for segmenting the left atrium from late gadolinium- enhanced cardiac magnetic resonance imaging.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation A global benchmark of algo- rithms for segmenting the left atrium from late gadolinium- enhanced cardiac magnetic resonance imaging

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.854060Z

Source-reported events for the cited work

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Observation 544a965e-82b1-4e21-a65a-4daca73d69b6 · outbound

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

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation St++: Make self-training work better for semi-supervised se- mantic segmentation

Reference 47

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Observation 725541c2-8f06-4f00-8730-0f638853be5d · outbound

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

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Revisiting weak-to-strong consistency in semi-supervised semantic segmentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.826808Z

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Observation 2b7fbca6-d57a-4fb2-99a5-a84c534a2265 · outbound

This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.813385Z

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Observation 9455f52a-03c9-402e-804c-7bc2e902f6f3 · outbound

This paper cites Reciprocal learn- ing for semi-supervised segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Reciprocal learn- ing for semi-supervised segmentation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.800003Z

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Observation 7533f2df-d2ba-403e-810c-12eb63f0889c · outbound

This paper cites Pyramid scene parsing network.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Pyramid scene parsing network

Reference 51

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Observation 62b2705f-3b4c-4de2-a60b-f48303aedb85 · outbound

This paper cites Alternate diverse teaching for semi-supervised medical image segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Alternate diverse teaching for semi-supervised medical image segmentation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.774536Z

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Observation ead415d8-cf59-4a3e-a3a7-495919467eb4 · outbound

This paper cites Pixel contrastive-consistent semi-supervised semantic segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Pixel contrastive-consistent semi-supervised semantic segmentation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.760447Z

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Observation 34124229-f89a-42f4-9d0c-d4aa63c89f67 · outbound

This paper cites C3-semiseg: Contrastive semi-supervised segmentation via cross-set learning and dynamic class- balancing.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation C3-semiseg: Contrastive semi-supervised segmentation via cross-set learning and dynamic class- balancing

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.747529Z

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Observation 10072bfc-199b-42a6-8209-3fe26fa9a0ec · outbound

This paper cites Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:13:23.734801Z

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Observation a87dbe5b-1eaf-400e-b01b-98d70240a0c3 · outbound

This paper cites an unresolved cited work.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:13:23.720318Z

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Observation 3d0bb866-c2bb-4715-8592-926707cc15c2 · outbound

This paper cites PseudoSeg: Designing Pseudo Labels for Semantic Segmentation.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation PseudoSeg: Designing Pseudo Labels for Semantic Segmentation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T22:13:23.596374Z

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Pith citing papers

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

PixCon: Clean-Positive Contrastive Learning for Foundation-Model Semi-Supervised Segmentation cites this paper.

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

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Observation 807d9ce7-4098-4743-aec2-b7e8763d49cb · 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 Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:39:12.362999Z

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