Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation | Paper Citation Record

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Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Unresolved cited work

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This paper cites Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration.IEEE transac- tions on medical imaging, 33(2):577–590.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration.IEEE transac- tions on medical imaging, 33(2):577–590

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This paper cites Pixel matching network for cross-domain few- shot segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Pixel matching network for cross-domain few- shot segmentation

Reference 3

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This paper cites Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformation

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This paper cites Adaptformer: Adapting vision transformers for scalable visual recogni- tion.Advances in Neural Information Processing Systems, 35:16664–16678.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Adaptformer: Adapting vision transformers for scalable visual recogni- tion.Advances in Neural Information Processing Systems, 35:16664–16678

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This paper cites Vision Transformer Adapter for Dense Predictions.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Vision Transformer Adapter for Dense Predictions

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This paper cites Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 7

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This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

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This paper cites Deepglobe 2018: A challenge to parse the earth through satellite images.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Deepglobe 2018: A challenge to parse the earth through satellite images

Reference 9

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This paper cites Few-shot semantic segmen- tation with prototype learning.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Few-shot semantic segmen- tation with prototype learning

Reference 10

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This paper cites The pascal visual object classes (voc) challenge.International journal of computer vision, 88(2):303–338.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation The pascal visual object classes (voc) challenge.International journal of computer vision, 88(2):303–338

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This paper cites Self- support few-shot semantic segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Self- support few-shot semantic segmentation

Reference 12

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This paper cites Adapt- ing in-domain few-shot segmentation to new domains with- out retraining.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Adapt- ing in-domain few-shot segmentation to new domains with- out retraining

Reference 13

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This paper cites Eva: Exploring the limits of masked visual representa- tion learning at scale.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Eva: Exploring the limits of masked visual representa- tion learning at scale

Reference 14

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This paper cites Eva-02: A visual representation for neon genesis.Image and Vision Computing, 149:105171.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Eva-02: A visual representation for neon genesis.Image and Vision Computing, 149:105171

Reference 15

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This paper cites Dat- acomp: In search of the next generation of multimodal datasets.Advances in Neural Information Processing Sys- tems, 36:27092–27112.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Dat- acomp: In search of the next generation of multimodal datasets.Advances in Neural Information Processing Sys- tems, 36:27092–27112

Reference 16

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This paper cites Note: Robust continual test- time adaptation against temporal correlation.Advances in Neural Information Processing Systems, 35:27253–27266.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Note: Robust continual test- time adaptation against temporal correlation.Advances in Neural Information Processing Systems, 35:27253–27266

Reference 17

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This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 18

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This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Momentum contrast for unsupervised visual rep- resentation learning

Reference 19

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This paper cites Masked autoencoders are scalable vision learners.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Masked autoencoders are scalable vision learners

Reference 20

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This paper cites Apseg: Auto-prompt network for cross-domain few-shot semantic segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Apseg: Auto-prompt network for cross-domain few-shot semantic segmentation

Reference 21

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This paper cites Adapt before comparison: A new perspective on cross-domain few-shot segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Adapt before comparison: A new perspective on cross-domain few-shot segmentation

Reference 22

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This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Lora: Low-rank adaptation of large language models.ICLR, 1(2):3

Reference 23

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This paper cites Automatic tuberculosis screening using chest radio- graphs.IEEE transactions on medical imaging, 33(2):233– 245.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Automatic tuberculosis screening using chest radio- graphs.IEEE transactions on medical imaging, 33(2):233– 245

Reference 24

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This paper cites Tinytta: Efficient test-time adaptation via early-exit ensembles on edge de- vices.Advances in Neural Information Processing Systems, 37:43274–43299.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Tinytta: Efficient test-time adaptation via early-exit ensembles on edge de- vices.Advances in Neural Information Processing Systems, 37:43274–43299

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This paper cites Membn: Robust test-time adaptation via batch norm with statistics memory.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Membn: Robust test-time adaptation via batch norm with statistics memory

Reference 26

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This paper cites Segment any- thing.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Segment any- thing

Reference 27

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This paper cites Learning what not to segment: A new perspective on few- shot segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Learning what not to segment: A new perspective on few- shot segmentation

Reference 28

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This paper cites Base and meta: A new perspective on few-shot segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10669–10686.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Base and meta: A new perspective on few-shot segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10669–10686

Reference 29

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This paper cites Surgical Fine-Tuning Improves Adaptation to Distribution Shifts.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Surgical Fine-Tuning Improves Adaptation to Distribution Shifts

Reference 30

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This paper cites Cross-domain few-shot se- mantic segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Cross-domain few-shot se- mantic segmentation

Reference 31

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This paper cites Adaptive prototype learning and allocation for few-shot segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Adaptive prototype learning and allocation for few-shot segmentation

Reference 32

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This paper cites Fss-1000: A 1000-class dataset for few- shot segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Fss-1000: A 1000-class dataset for few- shot segmentation

Reference 33

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This paper cites Dual-agent optimization framework for cross- domain few-shot segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Dual-agent optimization framework for cross- domain few-shot segmentation

Reference 34

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This paper cites A comprehensive sur- vey on test-time adaptation under distribution shifts.Interna- tional Journal of Computer Vision, 133(1):31–64.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation A comprehensive sur- vey on test-time adaptation under distribution shifts.Interna- tional Journal of Computer Vision, 133(1):31–64

Reference 35

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This paper cites Textual and visual guided task adaptation for source-free cross-domain few-shot segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Textual and visual guided task adaptation for source-free cross-domain few-shot segmentation

Reference 36

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This paper cites The devil is in low-level features for cross-domain few-shot seg- mentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation The devil is in low-level features for cross-domain few-shot seg- mentation

Reference 37

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This paper cites Simpler is better: Few-shot semantic seg- mentation with classifier weight transformer.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Simpler is better: Few-shot semantic seg- mentation with classifier weight transformer

Reference 38

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This paper cites Hypercorrela- tion squeeze for few-shot segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Hypercorrela- tion squeeze for few-shot segmentation

Reference 39

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This paper cites Cross-domain few-shot segmentation via iterative support-query correspon- dence mining.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Cross-domain few-shot segmentation via iterative support-query correspon- dence mining

Reference 40

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation DINOv2: Learning Robust Visual Features without Supervision

Reference 41

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This paper cites Hierarchical dense cor- relation distillation for few-shot segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Hierarchical dense cor- relation distillation for few-shot segmentation

Reference 42

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

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Learning transferable visual models from natural language supervi- sion

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This paper cites Do vision trans- formers see like convolutional neural networks?Advances in neural information processing systems, 34:12116–12128.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Do vision trans- formers see like convolutional neural networks?Advances in neural information processing systems, 34:12116–12128

Reference 44

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This paper cites LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-20T06:48:05.869978Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 88aa93e0-3607-4c83-a56c-a9b4db2b39ae · outbound

This paper cites DINOv3.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation DINOv3

Reference 46

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T06:48:05.872626Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8aa7d6dc-f520-4136-a641-1ecb21a2767c · outbound

This paper cites Domain-rectifying adapter for cross-domain few-shot segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Domain-rectifying adapter for cross-domain few-shot segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T06:48:06.554868Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a1ddc1f2-6b5d-48d8-ba8e-921c3663d269 · outbound

This paper cites Prior guided feature enrich- ment network for few-shot segmentation.IEEE transactions on pattern analysis and machine intelligence, 44(2):1050– 1065.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Prior guided feature enrich- ment network for few-shot segmentation.IEEE transactions on pattern analysis and machine intelligence, 44(2):1050– 1065

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T06:48:06.544929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a42a5778-1c89-4e48-a92b-c69847a3a961 · outbound

This paper cites Lightweight frequency masker for cross-domain few-shot se- mantic segmentation.Advances in Neural Information Pro- cessing Systems, 37:96728–96749.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Lightweight frequency masker for cross-domain few-shot se- mantic segmentation.Advances in Neural Information Pro- cessing Systems, 37:96728–96749

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T06:48:06.506443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4b365a9a-8ffa-4d84-a146-385ecb193faa · outbound

This paper cites Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-20T06:48:05.843824Z

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Observation b27c444d-e178-40f7-8e8b-3d7ea25d92a7 · outbound

This paper cites Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-20T06:48:05.866531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e569b4a7-2557-4800-83a5-a88924f05db0 · outbound

This paper cites The ham10000 dataset, a large collection of multi-source der- matoscopic images of common pigmented skin lesions.Sci- entific data, 5(1):1–9.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation The ham10000 dataset, a large collection of multi-source der- matoscopic images of common pigmented skin lesions.Sci- entific data, 5(1):1–9

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T06:48:06.495712Z

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Observation ddd52ff2-2e41-4315-ac66-7cbcd2b40a1f · outbound

This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-20T06:48:05.827618Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 826dc2cc-576a-47fc-bb8f-f7d86bfe6495 · outbound

This paper cites Panet: Few-shot image semantic seg- mentation with prototype alignment.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Panet: Few-shot image semantic seg- mentation with prototype alignment

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T06:48:06.469631Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation adc93a69-0c47-4d08-881e-9976f6433084 · outbound

This paper cites Continual test-time domain adaptation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Continual test-time domain adaptation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T06:48:06.477825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3d026754-98f2-4f9f-ba26-3f6f3865a2bb · outbound

This paper cites Adap- tive agent transformer for few-shot segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Adap- tive agent transformer for few-shot segmentation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T06:48:06.465873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 53aeae73-7be1-438d-8be0-f00f8fa01571 · outbound

This paper cites A survey of efficient fine- tuning methods for vision-language models—prompt and adapter.Computers & Graphics, 119:103885.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation A survey of efficient fine- tuning methods for vision-language models—prompt and adapter.Computers & Graphics, 119:103885

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T06:48:06.464014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0d45d05c-28cc-42c4-9666-128baf978877 · outbound

This paper cites Prototype mixture models for few-shot semantic segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Prototype mixture models for few-shot semantic segmentation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T06:48:06.467676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e251c2ed-6d3b-43b8-86c4-3f3ae664f833 · outbound

This paper cites Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T06:48:06.473926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a78807ab-6c8b-4c1f-b132-f30f6ec89cbb · outbound

This paper cites Canet: Class-agnostic segmentation networks with it- erative refinement and attentive few-shot learning.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Canet: Class-agnostic segmentation networks with it- erative refinement and attentive few-shot learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T06:48:06.458004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 931c2725-8514-4abc-8435-0ab1d257b837 · outbound

This paper cites Few-shot segmentation via cycle-consistent trans- former.Advances in neural information processing systems, 34:21984–21996.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Few-shot segmentation via cycle-consistent trans- former.Advances in neural information processing systems, 34:21984–21996

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T06:48:06.459991Z

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