CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers | Paper Citation Record

Observation 4fb2fde8-3d7b-4637-aee4-bfa1b986ed4b · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Semi-supervised semantic segmentation needs strong, varied perturbations,

Reference 1

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Observation cfef7b8f-c4e7-4c9e-a6db-15c89f7d6dfe · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Fixmatch: Simplifying semi-supervised learning with consistency and confidence,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.712683Z

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Observation 0c8fdb33-aba1-43ec-bf9f-f5d92b6ea364 · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Revisiting weak-to- strong consistency in semi-supervised semantic segmentation,

Reference 3

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Observation 9a296b6d-0612-4e8a-ae66-cc95adfc0b32 · outbound

This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,

Reference 4

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Observation 7cdd14c8-21f8-4dab-bcfc-b639ccbb8ece · outbound

This paper cites Encoder- decoder with atrous separable convolution for semantic image segmenta- tion,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Encoder- decoder with atrous separable convolution for semantic image segmenta- tion,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.687692Z

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Observation ba8f9e44-aa8d-4fa5-a9ac-6b54f0080d75 · outbound

This paper cites Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.677812Z

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Observation 6ec3e2fe-a221-46b6-8025-0064169fbf38 · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Freematch: Self-adaptive thresholding for semi-supervised learning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.667793Z

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Observation aa68ad86-501b-4b82-b90a-d6cb881e7216 · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Softmatch: Addressing the quantity-quality tradeoff in semi-supervised learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.657913Z

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Observation af886e06-a299-439a-95f8-27d365c307f8 · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Semi-supervised semantic segmentation using unreliable pseudo-labels,

Reference 9

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Observation a3d2e805-3e43-4297-aa3a-6b5137cd8b5e · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Re-distributing biased pseudo labels for semi-supervised semantic segmentation: A baseline investigation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.641980Z

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Observation 561b97b0-4256-49ad-9957-55f4707c2d96 · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Semi-supervised semantic segmentation via adaptive equalization learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.631847Z

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Observation dcb3c25a-7803-4dfd-9e83-5aae9d3588ef · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers CW-BASS: Confidence- weighted boundary-aware learning for semi-supervised semantic seg- mentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.621589Z

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Observation 5e8091ef-5ac8-4561-b791-24d9a3320f10 · outbound

This paper cites Dinov2: Learning robust visual features without supervision,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Dinov2: Learning robust visual features without supervision,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.611530Z

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Observation 96e9cc4b-b058-47d0-9c47-84c4ba5b9e0c · outbound

This paper cites Vision transformers for dense prediction,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Vision transformers for dense prediction,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T23:39:12.048675Z

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Observation 92d50e38-8d6e-4eb6-9092-1dd4ad5748f6 · outbound

This paper cites UniMatch V2: Pushing the limit of semi-supervised semantic segmentation,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers UniMatch V2: Pushing the limit of semi-supervised semantic segmentation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.595408Z

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Observation 807d9ce7-4098-4743-aec2-b7e8763d49cb · outbound

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

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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Observation 57fbfb76-5656-471a-b01d-94ab529095ab · outbound

This paper cites Pseudo-labeling and confirmation bias in deep semi-supervised learning,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Pseudo-labeling and confirmation bias in deep semi-supervised learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.585155Z

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Observation a797a37c-a988-47d2-9b3c-b434a0b4173d · outbound

This paper cites Fine-tuning can distort pretrained features and underperform out-of-distribution,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Fine-tuning can distort pretrained features and underperform out-of-distribution,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.574591Z

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Observation 6d4dbb89-be74-4b4c-92d2-c1f81fc591a7 · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.564828Z

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Observation 6b99237a-9053-4cd6-ae38-0e74ff6541de · outbound

This paper cites Perturbed and strict mean teachers for semi-supervised semantic seg- mentation,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Perturbed and strict mean teachers for semi-supervised semantic seg- mentation,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T23:39:12.066551Z

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Observation fc58d123-8198-4b10-8c05-fb9e93d4f7ca · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers St++: Make self-training work better for semi-supervised semantic segmentation,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T23:39:12.069574Z

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Observation 8af701ec-ac57-4c18-9e10-8df312e97530 · outbound

This paper cites Augseg: Maximizing the utility of unlabeled data for semi-supervised semantic segmentation,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Augseg: Maximizing the utility of unlabeled data for semi-supervised semantic segmentation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.542953Z

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Observation 6e189b5d-ca15-44e4-9227-3d297587da95 · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Allspark: Reborn labeled features from unlabeled in transformer for semi-supervised semantic segmentation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.531756Z

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Observation 6e40c63d-679f-4a05-bf2e-f7b3faf02868 · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers CorrMatch: Label propagation via correlation matching for semi-supervised semantic segmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.522603Z

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Observation 9dcac8e3-df08-43e3-985b-411cfc0928b1 · outbound

This paper cites Dash: Semi-supervised learning with dynamic thresholding,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Dash: Semi-supervised learning with dynamic thresholding,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.513550Z

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Observation d2f2c060-3ff7-4f46-9ecc-0d86b98027bc · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers CAFS: Class Adaptive Framework for Semi-Supervised Semantic Segmentation

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:39:12.349790Z

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Observation f7f8acbb-3559-44c1-8a72-a55b5a589d9e · outbound

This paper cites FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:39:12.336946Z

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Observation 4fd9debe-a5b1-4001-8b8f-544c0cd8d02a · outbound

This paper cites Realistic evaluation of deep semi-supervised learning algorithms,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Realistic evaluation of deep semi-supervised learning algorithms,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.504078Z

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Observation 09836ef5-8813-4d38-8775-186fa09e13dd · outbound

This paper cites Rethinking Semi-supervised Segmentation Beyond Accuracy: Reliability and Robustness.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Rethinking Semi-supervised Segmentation Beyond Accuracy: Reliability and Robustness

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:39:12.322865Z

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Observation 7ea528a3-5602-490c-9ac5-b036d291a9ec · outbound

This paper cites SemiVL: Semi-supervised semantic segmentation with vision-language guidance,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers SemiVL: Semi-supervised semantic segmentation with vision-language guidance,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.494801Z

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Observation 5b4449f0-8d23-4133-b11e-6a65d4d5e9ea · outbound

This paper cites Learning with noisy labels,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Learning with noisy labels,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.485534Z

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Observation ff38c138-9b94-4cc3-8d7f-8ebed7855df6 · outbound

This paper cites Confident learning: Estimating uncertainty in dataset labels,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Confident learning: Estimating uncertainty in dataset labels,

Reference 32

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Observation 2e34e208-c13e-445d-a068-e8016e77a864 · outbound

This paper cites On calibration of modern neural networks,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers On calibration of modern neural networks,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.467648Z

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Observation 78ca036c-0cdf-4b9e-afc2-c20ca9a29ebb · outbound

This paper cites Beta calibration: A well-founded and easily implemented improvement on logistic calibration for binary classifiers,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Beta calibration: A well-founded and easily implemented improvement on logistic calibration for binary classifiers,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.458389Z

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Observation 30cc42fb-f914-4f82-93d1-2e930380f6df · outbound

This paper cites Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.449099Z

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Observation 57745b6f-7b5d-4567-8dcb-d104dac20623 · outbound

This paper cites Improving calibration for long- tailed recognition,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Improving calibration for long- tailed recognition,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.439953Z

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Observation 8cba9739-2bff-4258-98a4-0a2588461ac6 · outbound

This paper cites Theoretical analysis of self- training with deep networks on unlabeled data,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Theoretical analysis of self- training with deep networks on unlabeled data,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.430822Z

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Observation 16a0570b-7575-4b23-8aab-6de1477f9166 · outbound

This paper cites Learning imbal- anced datasets with label-distribution-aware margin loss,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Learning imbal- anced datasets with label-distribution-aware margin loss,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.421500Z

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Observation 484b2bd0-0506-4037-abbd-2696e57edeb4 · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Seesaw loss for long-tailed instance segmentation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.412068Z

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Observation a5c66536-393e-4f7a-bb76-c2c22defd4d2 · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers CReST: A class-rebalancing self-training framework for imbalanced semi-supervised learning,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.401856Z

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Observation d4e1bd78-8a36-413b-a903-5ae7fbc53869 · outbound

This paper cites Class-imbalanced semi-supervised learning with adaptive thresholding,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Class-imbalanced semi-supervised learning with adaptive thresholding,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.392382Z

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Observation f953d092-341b-4459-817c-30b9de6eba02 · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Towards the uncharted: Density-descending feature perturbation for semi-supervised semantic segmentation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.382813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4eb02d44-8aae-4ac3-a163-a9e1f53afce9 · outbound

This paper cites Revisiting and maximizing temporal knowledge in semi-supervised semantic segmentation,.

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Revisiting and maximizing temporal knowledge in semi-supervised semantic segmentation,

Reference 43

Resolution: verified exact; raw_fallback, observed 2026-08-15T23:39:12.309057Z

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Observation 311409c4-4d42-46e1-b8fb-2b5f4782c6ba · outbound

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

CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers Beyond pixels: Semi- supervised semantic segmentation with a multi-scale patch-based multi- label classifier,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:39:12.373335Z

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

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

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

Reference 45

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

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