Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:30:18.105965Z
Paper Citation Record · LEDGER
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction
As of 22 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2507.15803.
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 2026-08-22T06:32:14.747728+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
46 of 46 outbound references displayed
- verified exact0
- verified fuzzy41
- unresolved5
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 841dde45-b525-4dcf-a799-b8c60819e944 · outbound
This paper cites Uncertainty Sets for Image Classifiers using Conformal Prediction.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Uncertainty Sets for Image Classifiers using Conformal Prediction
Reference 1
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a1156021-0c6a-4557-9b1e-6bbe0c29e62a · outbound
This paper cites A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Reference 2
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 60d87d25-a993-44b5-88b8-fa9967eaa174 · outbound
This paper cites Prediction-powered infer- ence.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Prediction-powered infer- ence
Reference 3
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bf6709d9-1a06-4b3c-bd94-ccc4993f66e6 · outbound
This paper cites Kandinsky conformal prediction: Efficient calibration of image segmentation algorithms.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Kandinsky conformal prediction: Efficient calibration of image segmentation algorithms
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 2026-08-22T06:32:14.747728+00:00.
Observation 205b7e2c-7801-4b3a-a272-390610087bcd · outbound
This paper cites Rsprompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Rsprompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model
Reference 5
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 22332aed-2f7d-491b-9285-e763aff53d1c · outbound
This paper cites Semi-supervised semantic segmentation with cross pseudo supervision.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Semi-supervised semantic segmentation with cross pseudo supervision
Reference 6
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5815457e-b3af-47b5-a680-61c351cc9b44 · outbound
This paper cites The cityscapes dataset for semantic urban scene understanding.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction The cityscapes dataset for semantic urban scene understanding
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.915488Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 34128947-ba78-418f-9b59-88a233a4e142 · outbound
This paper cites The pascal visual object classes challenge: A retrospective.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction The pascal visual object classes challenge: A retrospective
Reference 8
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 97a47309-dcf2-4e82-8e11-fbaf1d33b44a · outbound
This paper cites Finlayson.
Reference 9
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 348e5d24-fb0c-4539-8c82-0babbf82bd7c · outbound
This paper cites Semantic contours from inverse detectors.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Semantic contours from inverse detectors
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.870315Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d58cf3b6-15db-4b8a-ba6f-6dbe5b6c5eca · outbound
This paper cites Weakly- supervised concealed object segmentation with sam-based pseudo labeling and multi-scale feature grouping.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Weakly- supervised concealed object segmentation with sam-based pseudo labeling and multi-scale feature grouping
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.854466Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 990d3965-6aef-46d0-80d3-c161f6e3b565 · outbound
This paper cites Semi-supervised semantic segmentation via adaptive equalization learning.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Semi-supervised semantic segmentation via adaptive equalization learning
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.836502Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 60faba79-2077-45eb-a95c-a474c2e50ab0 · outbound
This paper cites Semicvt: Semi-supervised convolutional vi- sion transformer for semantic segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Semicvt: Semi-supervised convolutional vi- sion transformer for semantic segmentation
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.819731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation cabc0c7f-3085-4612-b8f9-6d8ea4146896 · outbound
This paper cites Semi-supervised se- mantic segmentation via gentle teaching assistant.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Semi-supervised se- mantic segmentation via gentle teaching assistant
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.802352Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8fbbc9c6-a1ad-410e-aacc-d32348683b83 · outbound
This paper cites Segment any- thing.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Segment any- thing
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.784332Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 30f5babe-e566-45e5-ba77-3b65fcb5bf79 · outbound
This paper cites From sam to cams: Exploring segment anything model for weakly supervised semantic segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction From sam to cams: Exploring segment anything model for weakly supervised semantic segmentation
Reference 16
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation e8143896-5230-4a06-b59d-ea220b8c3cd6 · outbound
This paper cites Distribution-free prediction bands for non-parametric regression.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Distribution-free prediction bands for non-parametric regression
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.754040Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 554827dd-fd5f-4bab-8c7c-936f666c014e · outbound
This paper cites Logic- induced diagnostic reasoning for semi-supervised semantic segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Logic- induced diagnostic reasoning for semi-supervised semantic segmentation
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.737176Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation db8f3c5a-8717-4828-96e6-b9ea34fff947 · outbound
This paper cites The pitfalls and promise of confor- mal inference under adversarial attacks.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction The pitfalls and promise of confor- mal inference under adversarial attacks
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.720876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2e1d8ed3-f05c-4378-9d19-b0dde4d387db · outbound
This paper cites Enhanced soft label for semi-supervised semantic seg- mentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Enhanced soft label for semi-supervised semantic seg- mentation
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.703234Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 14efebd3-8167-47b1-93f6-ca0da4d372fd · outbound
This paper cites Rankmatch: Exploring the better consistency regularization for semi-supervised semantic segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Rankmatch: Exploring the better consistency regularization for semi-supervised semantic segmentation
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.687111Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8735e604-5598-4911-b269-7b5adedeb2cb · outbound
This paper cites Cross prompting con- sistency with segment anything model for semi-supervised medical image segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Cross prompting con- sistency with segment anything model for semi-supervised medical image segmentation
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.669473Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 764cc7fc-f8e1-4815-9ab7-9cd4c019a6df · outbound
This paper cites Inductive confidence machines for re- gression.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Inductive confidence machines for re- gression
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.651211Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 3171c0f4-e7ed-4c07-81e4-41e94904e7ad · outbound
This paper cites Learning transferable visual models from natural language supervi- sion.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Learning transferable visual models from natural language supervi- sion
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:30:16.139366Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c18dd74f-4c64-423c-a0dd-6f692b54787b · outbound
This paper cites Glamm: Pixel grounding large multimodal model.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Glamm: Pixel grounding large multimodal model
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.623263Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2bc1b393-abc5-4374-ab07-dd21ee5729a8 · outbound
This paper cites A tutorial on conformal prediction.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction A tutorial on conformal prediction
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.605741Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 3ee79d8f-a7ec-4650-b34c-6b64e110c578 · outbound
This paper cites Conformal prediction for class-wise coverage via augmented label rank calibration.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Conformal prediction for class-wise coverage via augmented label rank calibration
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.588321Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 01027873-bd3d-42d5-8369-daba7cd691fe · outbound
This paper cites Direct prediction set min- imization via bilevel conformal classifier training.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Direct prediction set min- imization via bilevel conformal classifier training
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.569667Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2af602fc-50bf-4fc0-99a2-e1276c02fff2 · outbound
This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:30:16.777698Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b8313ffc-d8b2-4124-a832-efdcc32a8e7a · outbound
This paper cites Corrmatch: Label propagation via correlation matching for semi-supervised semantic segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Corrmatch: Label propagation via correlation matching for semi-supervised semantic segmentation
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.540191Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation cc05f13d-3baf-407e-a455-5842e5a46b47 · outbound
This paper cites Daw: exploring the better weighting function for semi-supervised semantic segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Daw: exploring the better weighting function for semi-supervised semantic segmentation
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.521832Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d757f3c9-d3a3-4c32-974b-867b4a3c5d6e · outbound
This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.505162Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 3012b013-e0c7-4536-a8bf-4f9beb36c99e · outbound
This paper cites Algorithmic learning in a random world.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Algorithmic learning in a random world
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.489660Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c9a494d7-7ac3-478c-bb3c-beff0c6d2f12 · outbound
This paper cites Allspark: Reborn labeled features from unlabeled in trans- former for semi-supervised semantic segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Allspark: Reborn labeled features from unlabeled in trans- former for semi-supervised semantic segmentation
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.474732Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation e3497897-47a9-4638-948f-5411fb20a675 · outbound
This paper cites Hunting sparsity: Density-guided contrastive learning for semi-supervised semantic segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Hunting sparsity: Density-guided contrastive learning for semi-supervised semantic segmentation
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.459663Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 53d9c538-a856-4441-8cf4-12072cf2368a · outbound
This paper cites Towards the uncharted: Density-descending feature perturbation for semi-supervised semantic segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Towards the uncharted: Density-descending feature perturbation for semi-supervised semantic segmentation
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.444177Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7012e7d2-38fd-4681-ba85-e2ae7fc804b3 · outbound
This paper cites Semi- supervised semantic segmentation using unreliable pseudo- labels.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Semi- supervised semantic segmentation using unreliable pseudo- labels
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.428003Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7b1830ce-7610-45d9-81ca-b62fb6b79ff1 · outbound
This paper cites Conflict-based cross-view consistency for semi-supervised semantic segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Conflict-based cross-view consistency for semi-supervised semantic segmentation
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.411779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 1d6fffe7-7638-4012-abda-89b515dc2c66 · outbound
This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Segformer: Simple and efficient design for semantic segmentation with transformers
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.394681Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 1bdde8dc-7d91-4c78-8aef-8b0c71c231aa · outbound
This paper cites Semi-supervised semantic segmentation with prototype-based consistency regularization.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Semi-supervised semantic segmentation with prototype-based consistency regularization
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.379609Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7bde2db6-9d92-4088-9ff0-9a416d144026 · outbound
This paper cites Revisiting weak-to-strong consistency in semi-supervised semantic segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Revisiting weak-to-strong consistency in semi-supervised semantic segmentation
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.363078Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7ebacdc4-febf-46c2-82df-bd44cb35d2f2 · outbound
This paper cites SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:30:17.872479Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e60d6eb3-ac0f-4ff2-ad37-60a59a3b42a9 · outbound
This paper cites Augmentation matters: A simple-yet- effective approach to semi-supervised semantic segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Augmentation matters: A simple-yet- effective approach to semi-supervised semantic segmentation
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.345762Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 24839e8b-767b-4730-b460-6a9568444232 · outbound
This paper cites Scene parsing through ade20k dataset.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Scene parsing through ade20k dataset
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.326693Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 94c0f797-21ae-49fe-8234-61495b6e0ca0 · outbound
This paper cites Segment everything everywhere all at once.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Segment everything everywhere all at once
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.308095Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation f00f4174-a59f-49f9-be03-8aad44a66f55 · outbound
This paper cites Pseudoseg: Design- ing pseudo labels for semantic segmentation.
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction Pseudoseg: Design- ing pseudo labels for semantic segmentation
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:30:18.279278Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Pith citing papers
No inbound Pith citation observations are available.