Paper Citation Record · LEDGER

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation

As of 21 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2505.11075.

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

pith.paper-citation-record.v1
2505.11075 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:05:31.700491Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T16:50:57.376622Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T16:53:00.103570Z

Reference resolution

46 of 46 outbound references displayed

verified exact0
verified fuzzy33
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 513ad6ea-2d35-4ffa-9dc4-6d97f590762b · outbound

This paper cites Learn- ing with pseudo-ensembles.Advances in neural information processing systems, 27, 2014.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Learn- ing with pseudo-ensembles.Advances in neural information processing systems, 27, 2014

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.596424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 59169714-44aa-4703-acf7-274cc7035ff5 · outbound

This paper cites Deep watershed transform for instance segmentation.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Deep watershed transform for instance segmentation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.579204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4d5b75a0-45ce-4188-b52a-14bb2e2e408b · outbound

This paper cites Guided distillation for semi-supervised instance segmentation.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Guided distillation for semi-supervised instance segmentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.562083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cee5afe2-9804-46ee-bbe5-6617b8f506a1 · outbound

This paper cites ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:31.467396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e93251a-ff08-4135-a5fd-4b0fc09cf6e6 · outbound

This paper cites Mixmatch: A holistic approach to semi-supervised learning.Advances in neural information processing systems, 32, 2019.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Mixmatch: A holistic approach to semi-supervised learning.Advances in neural information processing systems, 32, 2019

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:31.473216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97f65fb0-8e3d-495e-a75b-3cdea582dc75 · outbound

This paper cites Yolact: Real-time instance segmentation.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Yolact: Real-time instance segmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:31.478018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 218ad49c-120c-4bc0-81a5-28835bc79d94 · outbound

This paper cites Interactive graph cuts for optimal boundary & region segmentation of objects in nd images.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Interactive graph cuts for optimal boundary & region segmentation of objects in nd images

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.526965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ee0bf766-fcdf-4ca2-b12a-b1adbca71358 · outbound

This paper cites Cascade r-cnn: High quality object detection and instance segmentation.IEEE transactions on pattern analysis and machine intelligence, 43(5):1483–1498, 2019.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Cascade r-cnn: High quality object detection and instance segmentation.IEEE transactions on pattern analysis and machine intelligence, 43(5):1483–1498, 2019

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:31.489744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb764fbd-7356-4147-90e5-c9dd422e97eb · outbound

This paper cites End-to- end object detection with transformers.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation End-to- end object detection with transformers

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:31.494346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb520169-56e7-4cb2-a58a-5040d8b24ad3 · outbound

This paper cites Per- pixel classification is not all you need for semantic segmen- tation.Advances in neural information processing systems, 34:17864–17875, 2021.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Per- pixel classification is not all you need for semantic segmen- tation.Advances in neural information processing systems, 34:17864–17875, 2021

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:31.499057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d250654-78ed-4fbe-ab82-afbd11dfe777 · outbound

This paper cites Schwing, Alexan- der Kirillov, and Rohit Girdhar.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Schwing, Alexan- der Kirillov, and Rohit Girdhar

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.476212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0b392531-3d30-488c-91b5-d60cb0d2fafd · outbound

This paper cites Mean shift: A robust ap- proach toward feature space analysis.IEEE Transactions on pattern analysis and machine intelligence, 24(5):603–619,.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Mean shift: A robust ap- proach toward feature space analysis.IEEE Transactions on pattern analysis and machine intelligence, 24(5):603–619,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.459766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1a4f5d29-3f9a-46e6-ba37-ad6f043969a4 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation The cityscapes dataset for semantic urban scene understanding

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.443362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ca60acc4-3f30-4523-9894-7d67de1bed38 · outbound

This paper cites Semantic Instance Segmentation with a Discriminative Loss Function.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Semantic Instance Segmentation with a Discriminative Loss Function

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:31.520314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1cba8ac-6260-4c8c-b006-b8d92314d075 · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Improved Regularization of Convolutional Neural Networks with Cutout

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:31.526206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f385570f-ec7d-4b9e-a8f6-71e0fc3189d2 · outbound

This paper cites Polite teacher: Semi-supervised instance segmentation with mutual learning and pseudo-label thresholding.IEEE Access, 2024.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Polite teacher: Semi-supervised instance segmentation with mutual learning and pseudo-label thresholding.IEEE Access, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.426814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f41e38df-31dd-4281-a931-77c5fb4a41b5 · outbound

This paper cites Consistency-based semi- supervised active learning: Towards minimizing labeling cost.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Consistency-based semi- supervised active learning: Towards minimizing labeling cost

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.409278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7eed3297-7176-4325-89eb-da8bf68d0fcd · outbound

This paper cites Ssap: Single-shot in- stance segmentation with affinity pyramid.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Ssap: Single-shot in- stance segmentation with affinity pyramid

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.391717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5e53b9fb-0e92-4f45-8d29-df90b63b4066 · outbound

This paper cites Fast r-cnn.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Fast r-cnn

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:31.546923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16419e23-e09d-46ac-bd74-e4b98c35f9de · outbound

This paper cites LVIS: A Dataset for Large V ocabulary Instance Segmentation.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation LVIS: A Dataset for Large V ocabulary Instance Segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.358645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f4aaab77-5778-4c05-8632-360b35904a94 · outbound

This paper cites Pseudo-labeling enhanced by privileged information and its application to in situ sequencing images.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Pseudo-labeling enhanced by privileged information and its application to in situ sequencing images

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.341682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 285e7524-c072-4197-a1bc-7d40ffdc7aa0 · outbound

This paper cites Mask r-cnn.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Mask r-cnn

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.323580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cf7b5faa-68e4-455d-8979-29637dbb4478 · outbound

This paper cites Pseudo-label alignment for semi-supervised instance segmentation.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Pseudo-label alignment for semi-supervised instance segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.288243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c89df795-e193-4695-9d91-509b40f9acf1 · outbound

This paper cites Mask scoring r-cnn.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Mask scoring r-cnn

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.244407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7e9662fd-f5a3-49c7-8a75-b1b262d7d2f8 · outbound

This paper cites Consistency-based semi-supervised learning for object de- tection.Advances in Neural Information Processing Systems,.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Consistency-based semi-supervised learning for object de- tection.Advances in Neural Information Processing Systems,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.188088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 295fe016-788d-4857-a8f6-63198539ebed · outbound

This paper cites Consistency-based semi-supervised learning for object de- tection.Advances in neural information processing systems, 32, 2019.

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.169709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6523f0c7-7370-40f0-a348-6a9386a6be6a · outbound

This paper cites Centermask: Real- time anchor-free instance segmentation.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Centermask: Real- time anchor-free instance segmentation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.150070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2c6b1556-6fd0-41d8-a3e9-389c240bc4fa · outbound

This paper cites Exploring plain vision transformer backbones for object de- tection.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Exploring plain vision transformer backbones for object de- tection

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:31.598239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e4d5316-7f94-4b9e-9c27-f7b1e566e2ae · outbound

This paper cites Weakly supervised open- vocabulary object detection.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Weakly supervised open- vocabulary object detection

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.120671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6a23a0b9-8dbd-4806-bc18-7ec2e1cbd538 · outbound

This paper cites Microsoft coco: Common objects in context.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Microsoft coco: Common objects in context

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.103671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5c5a108c-4a9b-46ee-8b5c-ff233b7c7678 · outbound

This paper cites Sgn: Sequential grouping networks for instance segmentation.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Sgn: Sequential grouping networks for instance segmentation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.084898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 388771cf-f7ae-47b0-bee3-d51b1f7d5bad · outbound

This paper cites Unbiased teacher for semi-supervised object detec- tion.International Conference on Learning Representations,.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Unbiased teacher for semi-supervised object detec- tion.International Conference on Learning Representations,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.066253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c2bbe1a6-065a-4361-99b2-b6e00c200762 · outbound

This paper cites Decoupled Weight Decay Regularization.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Decoupled Weight Decay Regularization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:31.626559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16dc1a48-a69e-441a-af9c-4e7e39ca34e4 · outbound

This paper cites Semi-supervised se- mantic segmentation via strong-weak dual-branch network.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Semi-supervised se- mantic segmentation via strong-weak dual-branch network

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.049495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f02b91b0-071b-427b-9d2e-d0d293cb82bf · outbound

This paper cites Active teacher for semi-supervised ob- ject detection.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Active teacher for semi-supervised ob- ject detection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.032713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 29efbf3e-0606-4918-9914-94f22a5508c3 · outbound

This paper cites Semi- supervised semantic segmentation with cross-consistency training.arXiv: Computer Vision and Pattern Recognition,.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Semi- supervised semantic segmentation with cross-consistency training.arXiv: Computer Vision and Pattern Recognition,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:32.014816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5179414a-bc47-42a5-975a-ffe1be29cee4 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Learning transferable visual models from natural language supervi- sion

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:31.997614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1f381467-348f-4d80-9c86-b85a69888aca · outbound

This paper cites Girshick, Georgia Gkioxari, and Kaiming He.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Girshick, Georgia Gkioxari, and Kaiming He

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:31.979619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cf7e0b95-c1c6-4421-9160-0a0b037e3a7b · outbound

This paper cites A Simple Semi-Supervised Learning Framework for Object Detection.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation A Simple Semi-Supervised Learning Framework for Object Detection

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:31.662084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b8d4e22-999f-4d0e-902a-95d23f6fa55b · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30, 2017.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30, 2017

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:31.962506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7a5d1129-8c72-41fc-bcb6-d41db33c6358 · outbound

This paper cites Noisy bound- aries: Lemon or lemonade for semi-supervised instance seg- mentation?2022 IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition (CVPR), pages 16805–16814,.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Noisy bound- aries: Lemon or lemonade for semi-supervised instance seg- mentation?2022 IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition (CVPR), pages 16805–16814,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:31.945090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e79adbe7-81d5-4837-8b83-df45cbf59478 · outbound

This paper cites Detectron2.https://github.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Detectron2.https://github

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:31.926562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e5656e88-6506-4e24-a21b-8b09f1423927 · outbound

This paper cites End-to- end semi-supervised object detection with soft teacher.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation End-to- end semi-supervised object detection with soft teacher

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:31.683334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e249aed-0ad9-410b-b9cf-798e36ae8828 · outbound

This paper cites Detclipv2: Scal- able open-vocabulary object detection pre-training via word- region alignment.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Detclipv2: Scal- able open-vocabulary object detection pre-training via word- region alignment

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:31.893897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 111672f4-3ed3-48d1-9d38-322dc61bee9e · outbound

This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation Bdd100k: A diverse driving dataset for heterogeneous multitask learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:31.861433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 78d9bc50-ba87-440b-93c7-41341260851a · outbound

This paper cites burn-in” stage to train our models on only labeled data. After that, run a teacher-student “mutual learning.

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation burn-in” stage to train our models on only labeled data. After that, run a teacher-student “mutual learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:31.833330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation e79f2ef8-7796-41af-a827-b7bbfb6683c6 · inbound

Training a Student Expert via Semi-Supervised Foundation Model Distillation cites this paper.

Training a Student Expert via Semi-Supervised Foundation Model Distillation Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:53:00.105296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.