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Paper Citation Record · LEDGER

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning

As of 10 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2601.08617.

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

pith.paper-citation-record.v1
2601.08617 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T10:52:03.139813Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

57 of 57 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved55
  • parse uncertain1
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d361444d-c4a6-4b78-8210-e9584b9e55aa · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Flamingo: a visual language model for few-shot learning

Reference 1

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source=pdf_text observed=2026-08-03T10:52:02.781278Z digest=sha256:2c801562e538b6591d03b237ed150e52d8fc57fb87d7bca5cbddd8cc2683d0e6

Observation 1ea7cd30-2200-4479-bc50-315288888433 · outbound

This paper cites Food-101 – mining discriminative components with random forests.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Food-101 – mining discriminative components with random forests

Reference 2

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source=pdf_text observed=2026-08-03T10:52:02.793669Z digest=sha256:df6c951c56c012392eae7d642c10486f7835f5b4fc5217c324b0ec164956a1b6

Observation 045e2953-f384-442e-8f76-413e724b830b · outbound

This paper cites Calibrating deep neural networks by pairwise constraints.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Calibrating deep neural networks by pairwise constraints

Reference 3

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source=pdf_text observed=2026-08-03T10:52:02.799887Z digest=sha256:c497640bb22dae04c19b1b70240303b1eec1f55b806aab65efb8b347480a5b53

Observation d2a6673d-0020-4e4a-92e7-c48628e5cf43 · outbound

This paper cites Cimpoi, S.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Cimpoi, S

Reference 4

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source=pdf_text observed=2026-08-03T10:52:02.805314Z digest=sha256:922d83cf44058c9ec9a48f395ad2b350dfe0e51ab59a3e99898f1c8e070aa79d

Observation 4721f8a8-30e6-445f-b587-2a4763571608 · outbound

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

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Imagenet: A large-scale hierarchical image database

Reference 5

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Observation cf4a0450-f6b8-4290-95e9-81445028f932 · outbound

This paper cites Weinberger.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Weinberger

Reference 6

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source=pdf_text observed=2026-08-03T10:52:02.816261Z digest=sha256:dbb6e19447ee645694bc4448b5b4c8ba902ccbb113692cf4e35bb89e1a48e696

Observation 79939933-fa7f-4692-b8ee-ea9ca6d69b91 · outbound

This paper cites Visual-language prompt tuning with knowledge-guided context optimization.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Visual-language prompt tuning with knowledge-guided context optimization

Reference 7

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source=pdf_text observed=2026-08-03T10:52:02.821553Z digest=sha256:3c15a9e863c340dd0038320c365fbe9b6e10743cb51fcd204d659d2551bd47aa

Observation 81b4b4d1-ece4-44fb-b7be-75f9cd3f55e6 · outbound

This paper cites an unresolved cited work.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-03T10:52:02.826662Z digest=sha256:2138489c7aa4232154c6cd8cb863c6fa723fe842d2d7b38fcd92e39b2821a53f

Observation da7fcd24-984c-4ec9-b6ed-53f332f2a695 · outbound

This paper cites Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan

Reference 9

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source=pdf_text observed=2026-08-03T10:52:02.834031Z digest=sha256:8b2261c1bb311d58d4d8aeb10969da6d33f330eb0125e70b2f981d170c5ef635

Observation 83504676-afc5-4407-8753-dcc5a956988b · outbound

This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.ICCV, 2021.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning The many faces of robustness: A critical analysis of out-of-distribution generalization.ICCV, 2021

Reference 10

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source=pdf_text observed=2026-08-03T10:52:02.839125Z digest=sha256:9c424adfaf96bd05e3ad81a3a422ad9cc52cd6e5b803e8b7356ba2d7b645f9b2

Observation 2b6739d0-5b16-4c71-840c-f947a9dca61b · outbound

This paper cites Natural adversarial examples.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Natural adversarial examples

Reference 11

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source=pdf_text observed=2026-08-03T10:52:02.845688Z digest=sha256:9574496378876cabaf13a211d7077206f539c78aec94c1a294a97e0a5fd20c7f

Observation e6c05e22-2179-4397-8814-88459749bbe9 · outbound

This paper cites an unresolved cited work.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Unresolved cited work

Reference 12

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source=pdf_text observed=2026-08-03T10:52:02.856613Z digest=sha256:026b0469d0ca07c48bdfe4b58d6729f09702f177f850a3855df94ca3f9de6b19

Observation 25a7da41-d0cf-417c-9254-49dd1d1bdd94 · outbound

This paper cites Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig

Reference 13

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source=pdf_text observed=2026-08-03T10:52:02.864238Z digest=sha256:c11f675b657b9dbac511fb0958bd33259b049a05e2db8294406511ec5b24061a

Observation e2b17526-01af-4a60-9af4-70678241b66c · outbound

This paper cites Simple but effective: CLIP embed- dings for embodied AI.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Simple but effective: CLIP embed- dings for embodied AI

Reference 14

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Observation 044e3057-d060-4132-a237-c5ef0db01c93 · outbound

This paper cites Biomedcoop: Learning to prompt for biomedi- cal vision-language models.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Biomedcoop: Learning to prompt for biomedi- cal vision-language models

Reference 15

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source=pdf_text observed=2026-08-03T10:52:02.878387Z digest=sha256:80bab69ac2df175c84a555ecc86ebdc3a597dc86bc867ab4caec66cac2b1d3e4

Observation 8e042156-b85c-444b-a865-b88292ebf123 · outbound

This paper cites 3d object representations for fine-grained categorization.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning 3d object representations for fine-grained categorization

Reference 16

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source=pdf_text observed=2026-08-03T10:52:02.884957Z digest=sha256:0c56a7d9856f7505b3a9a1ec6be7480c3466e6ff904dacfbd7baa8f2dcf5ae90

Observation 312ddf1f-d406-43ad-ace1-cdb085888f77 · outbound

This paper cites One-shot learning of object categories.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning One-shot learning of object categories.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

Reference 17

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source=pdf_text observed=2026-08-03T10:52:02.890945Z digest=sha256:e6e26cce8650e0ba1c6bea29522f929c2aa0330a57cfecc3256a8a756151140f

Observation 9e472948-1b49-43b9-a028-f32c93d05c8d · outbound

This paper cites Selvaraju, Akhilesh Deepak Got- mare, Shafiq Joty, Caiming Xiong, and Steven Hoi.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Selvaraju, Akhilesh Deepak Got- mare, Shafiq Joty, Caiming Xiong, and Steven Hoi

Reference 18

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source=pdf_text observed=2026-08-03T10:52:02.897211Z digest=sha256:9e8870e1d00324e94bf623f59337a804ffa596bf06d74af7ca709eff3c3f1afd

Observation 5abd3aa1-c9cf-4d4a-a9aa-8f0ef7c6a492 · outbound

This paper cites an unresolved cited work.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Unresolved cited work

Reference 19

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source=pdf_text observed=2026-08-03T10:52:02.905086Z digest=sha256:2a9916c5947800d0b579221352c69199996cddce5553dfe4b37433c2074c0b3b

Observation a0a65c92-5f91-416f-8dc5-f7861a8f4f67 · outbound

This paper cites CLIP-reID: exploiting vision-language model for image re-identification without concrete text labels.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning CLIP-reID: exploiting vision-language model for image re-identification without concrete text labels

Reference 20

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Observation 3b7fcbf6-6cf5-4159-b862-18deef90a85c · outbound

This paper cites The devil is in the margin: Margin-based label smooth- ing for network calibration.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning The devil is in the margin: Margin-based label smooth- ing for network calibration

Reference 21

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Observation 95f6eb96-c75d-4532-91f4-2db16adfa440 · outbound

This paper cites Class adaptive network calibration.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Class adaptive network calibration

Reference 22

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source=pdf_text observed=2026-08-03T10:52:02.927114Z digest=sha256:d295ad62205b2590cf8b9a36afcaf8d197f95a3d870eb1ff2d06cf34cfe2ad36

Observation 2d38fb82-fe67-4399-9508-44d0234d13c3 · outbound

This paper cites Visual instruction tuning, 2023.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Visual instruction tuning, 2023

Reference 23

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source=pdf_text observed=2026-08-03T10:52:02.933080Z digest=sha256:2cec0dc3175c67eda70118898db177c4bc0d64ea55ce61a943576b29381d7286

Observation c1551147-339c-44e1-b2c2-c6ae23669b2b · outbound

This paper cites Decoupled weight de- cay regularization.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Decoupled weight de- cay regularization

Reference 24

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Observation 7983b418-208c-47f3-b0ef-7e8c572b5c25 · outbound

This paper cites Contrast-Aware Calibration for Fine-Tuned CLIP: Leveraging Image-Text Alignment.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Contrast-Aware Calibration for Fine-Tuned CLIP: Leveraging Image-Text Alignment

Reference 25

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source=pdf_text observed=2026-08-03T10:52:02.947578Z digest=sha256:8d829dc85124f38879a4e428b3b7f82cbffa1d2529e15fe1ea147f56091c655a

Observation 4416cb08-c621-438f-b758-af53985ec52c · outbound

This paper cites an unresolved cited work.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Unresolved cited work

Reference 26

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Observation 5561ff0d-4d35-4f84-9250-472eec0c7c0c · outbound

This paper cites Test- time prompt tuning for zero-shot generalization in vision- language models.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Test- time prompt tuning for zero-shot generalization in vision- language models

Reference 27

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Observation 1cafa23b-bfc5-4533-809d-37dba075f64a · outbound

This paper cites Revisiting the calibration of modern neu- ral networks.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Revisiting the calibration of modern neu- ral networks

Reference 28

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Observation aa02ad61-e1ef-4b83-a859-4f4c3e6f5498 · outbound

This paper cites Bayesadapter: enhanced uncertainty estimation in CLIP few-shot adaptation.International Journal of Computer Vision (IJCV), 2025.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Bayesadapter: enhanced uncertainty estimation in CLIP few-shot adaptation.International Journal of Computer Vision (IJCV), 2025

Reference 29

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Observation c9354d2b-d7c0-4236-81ab-792c386dd73f · outbound

This paper cites Slip: Self-supervision meets language-image pre- training.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Slip: Self-supervision meets language-image pre- training

Reference 30

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source=pdf_text observed=2026-08-03T10:52:02.983946Z digest=sha256:9a33585d3346e96b1cf1394435071c445bc7a0b8848a9e2a94ca6a271a43c216

Observation 45d8ccf9-5aa5-4211-80f8-1322a66f47d7 · outbound

This paper cites Calibrating deep neural networks using focal loss.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Calibrating deep neural networks using focal loss

Reference 31

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source=pdf_text observed=2026-08-03T10:52:02.990101Z digest=sha256:4699518dca5f86de231b307304ff833109505604d3465bec5e53432464c469bc

Observation 7d8c9d16-375b-4e83-8719-f3afdadf9b86 · outbound

This paper cites When does label smoothing help? NeurIPS, 32, 2019.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning When does label smoothing help? NeurIPS, 32, 2019

Reference 32

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source=pdf_text observed=2026-08-03T10:52:02.995501Z digest=sha256:c24a6d4dcc10a5f9b1fbf50eaab023919d95a3f805b7f145a252b6e4e8489cd4

Observation 0599720d-135d-472e-aa1e-dfdbf34e1bcf · outbound

This paper cites Robust calibration of large vision- language adapters.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Robust calibration of large vision- language adapters

Reference 33

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source=pdf_text observed=2026-08-03T10:52:03.000137Z digest=sha256:c5ea4b2e2b3e09b55719b48fca74432ae27ab8de9e4b1b1db847e39ec60b79cc

Observation e18fbb7d-341d-4cc9-b60a-fc345c2ec851 · outbound

This paper cites Nilsback and A.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Nilsback and A

Reference 34

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source=pdf_text observed=2026-08-03T10:52:03.004921Z digest=sha256:485bf530ef5d441991cc18e47bd7826db188cfc7c58a72f4c02ecb6f194d72fc

Observation 815db61f-4446-4c6e-a3db-09e45651308a · outbound

This paper cites Towards calibrated robust fine- tuning of vision-language models.Advances in Neural Infor- mation Processing Systems, 37:12677–12707, 2024.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Towards calibrated robust fine- tuning of vision-language models.Advances in Neural Infor- mation Processing Systems, 37:12677–12707, 2024

Reference 35

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Observation 1e6193cd-981e-45e3-9b7e-5bc42a1dd700 · outbound

This paper cites VLP: Vision language planning for autonomous driv- ing.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning VLP: Vision language planning for autonomous driv- ing

Reference 36

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source=pdf_text observed=2026-08-03T10:52:03.014028Z digest=sha256:ac32563568a36dbb12e9eb580c404e6b9ed852411bda3e4046e8bae693535c26

Observation 39988803-00eb-45d1-b573-3ab1c713f483 · outbound

This paper cites an unresolved cited work.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Unresolved cited work

Reference 37

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source=pdf_text observed=2026-08-03T10:52:03.018700Z digest=sha256:cde09975ccaecba74e9bf1dbfbca0f96736e2dda13d96904bb6919e83cd3af6b

Observation cc4367c8-26d9-48d1-8784-d32a0b82266d · outbound

This paper cites Regularizing neural networks by pe- nalizing confident output distributions.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Regularizing neural networks by pe- nalizing confident output distributions

Reference 38

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source=pdf_text observed=2026-08-03T10:52:03.023393Z digest=sha256:72ed0ddba6b50866e6fdfc03b4a8e0b835eb02a62901e941d878f32c4172f545

Observation 0fc01866-c275-4033-bf14-cb9afdaa8395 · outbound

This paper cites Learning transferable visual models from natural language supervision.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Learning transferable visual models from natural language supervision

Reference 39

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source=pdf_text observed=2026-08-03T10:52:03.028763Z digest=sha256:c61deeb5e9970fd32e1b1afa8a75595fd614b71168a8c90313128194d63761b7

Observation 1fdb4f94-26b2-4767-b2dc-5f370d00ea0a · outbound

This paper cites Do ImageNet classifiers generalize to Ima- geNet? InProceedings of the 36th International Conference on Machine Learning, pages 5389–5400.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Do ImageNet classifiers generalize to Ima- geNet? InProceedings of the 36th International Conference on Machine Learning, pages 5389–5400

Reference 40

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no resolver link, observed 2026-08-03T10:52:03.035386Z

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source=pdf_text observed=2026-08-03T10:52:03.035386Z digest=sha256:306283fb036a3ca5d01d3cc4a19f038ed1a4e79bdeebf52e8377fe8914dac937

Observation 7affa219-5227-4e61-96da-e1be17a35ca9 · outbound

This paper cites O-TPT: Orthogonality constraints for calibrating test-time prompt tuning in vision-language models.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning O-TPT: Orthogonality constraints for calibrating test-time prompt tuning in vision-language models

Reference 41

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source=pdf_text observed=2026-08-03T10:52:03.041165Z digest=sha256:0dda6bc3665a4e25eb546fe00ff80cda151ba58ac57413a839537f857fe4c0ab

Observation 3d4c001b-8268-4ec6-8f9e-3962a1ddc3bf · outbound

This paper cites Test- time prompt tuning for zero-shot generalization in vision- language models.Advances in Neural Information Process- ing Systems, 35:14274–14289, 2022.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Test- time prompt tuning for zero-shot generalization in vision- language models.Advances in Neural Information Process- ing Systems, 35:14274–14289, 2022

Reference 42

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source=pdf_text observed=2026-08-03T10:52:03.047061Z digest=sha256:7fd19fe9f706c1e0b7dfacb849a24d5281aa2db465283d11a77b61ecf24edc99

Observation 8188788d-01ed-42f9-ba0a-3d2bce990432 · outbound

This paper cites Few-shot, now for real: Medi- cal VLMs adaptation without balanced sets or validation.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Few-shot, now for real: Medi- cal VLMs adaptation without balanced sets or validation

Reference 43

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source=pdf_text observed=2026-08-03T10:52:03.052657Z digest=sha256:30ce83cad9710b1b29fbe48272e8d337704ac8665e32e192101f130f98392ca7

Observation 6e5b4bef-147c-4abc-88bd-0130a1ecfc2c · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 44

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source=pdf_text observed=2026-08-03T10:52:03.063757Z digest=sha256:c6eaed420d0353dc99443e10a6c1883a157cb86a1ecffdf7b024649a784096e4

Observation 8dd32646-e4ae-45fb-8340-da0349f7a204 · outbound

This paper cites An empirical study into what matters for calibrating vision-language models.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning An empirical study into what matters for calibrating vision-language models

Reference 45

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no resolver link, observed 2026-08-03T10:52:03.069655Z

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source=pdf_text observed=2026-08-03T10:52:03.069655Z digest=sha256:2bf892a3bff71183473bfe370da5866be462ac4bd6cc61d620945a97d3466b2f

Observation df57c9ec-21f3-42eb-8e61-fe62204f38ec · outbound

This paper cites Learning robust global representations by penalizing local predictive power.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Learning robust global representations by penalizing local predictive power

Reference 46

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source=pdf_text observed=2026-08-03T10:52:03.082824Z digest=sha256:aed5ff8e9297848d77922fcfbb6383f345159ae6ff9396d9ba57686faf891baf

Observation 4c204992-fda6-4351-906b-127a97ef4e19 · outbound

This paper cites Open-vocabulary calibration for fine-tuned CLIP.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Open-vocabulary calibration for fine-tuned CLIP

Reference 47

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source=pdf_text observed=2026-08-03T10:52:03.088904Z digest=sha256:ab9295961ed10334cb590173663c4dafa4fd26ab9433e492e581d985920acbf9

Observation 23c52554-ff37-49f0-8f64-17bb55adfb84 · outbound

This paper cites Sun database: Large-scale scene 10 recognition from abbey to zoo.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Sun database: Large-scale scene 10 recognition from abbey to zoo

Reference 48

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source=pdf_text observed=2026-08-03T10:52:03.094112Z digest=sha256:e23866c15a739ed6942d70f8572516ad9caac466e1dfc92168b33b2ddef275d9

Observation 87fe0415-628f-4659-89e4-e38998a9b1e8 · outbound

This paper cites Demysti- fying CLIP data.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Demysti- fying CLIP data

Reference 49

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source=pdf_text observed=2026-08-03T10:52:03.100591Z digest=sha256:2ab988f9060d9dd2433fbfc4676d39dfe160de910b965fe35f0e3d3a7230c42e

Observation 5ace1d15-af96-40bd-ae95-466f67b7d6c0 · outbound

This paper cites Beyond probability partitions: Calibrating neural networks with semantic aware grouping.Advances in Neural Information Processing Systems, 36:58448–58460,.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Beyond probability partitions: Calibrating neural networks with semantic aware grouping.Advances in Neural Information Processing Systems, 36:58448–58460,

Reference 50

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source=pdf_text observed=2026-08-03T10:52:03.105431Z digest=sha256:ec1dd8c0ca2e732bd49af8f62f1fafae98a4a9d0107c4774d5d450326bc9907c

Observation aa8afaec-3e22-4df4-b722-abdf03a30f42 · outbound

This paper cites Hasegawa-Johnson, Yingzhen Li, and Chang D.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Hasegawa-Johnson, Yingzhen Li, and Chang D

Reference 51

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source=pdf_text observed=2026-08-03T10:52:03.110974Z digest=sha256:ee4be5e34b44f5b6d7cb59b732d57ba64462aa570d1cfb9c95cc2ca0232aebf0

Observation 6896931d-c33d-4b08-8237-34f3cf2f3aa4 · outbound

This paper cites Cutmix: Regular- ization strategy to train strong classifiers with localizable fea- tures.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Cutmix: Regular- ization strategy to train strong classifiers with localizable fea- tures

Reference 52

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source=pdf_text observed=2026-08-03T10:52:03.116538Z digest=sha256:a3a85da64ee6c9e01dadffca734b4102b32157d892ccaa10e22691c3a92a5ba8

Observation 9a171071-70d7-461e-943a-545bd1d78ed7 · outbound

This paper cites Dauphin, and David Lopez-Paz.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Dauphin, and David Lopez-Paz

Reference 53

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source=pdf_text observed=2026-08-03T10:52:03.122156Z digest=sha256:0383d1875682da36c2d9907374109d6193f7389f2be2ed31bc9208b305b58a6d

Observation 05978e98-4aad-42c7-a878-b4d93aca1c6b · outbound

This paper cites Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning

Reference 54

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source=pdf_text observed=2026-08-03T10:52:03.127331Z digest=sha256:9828ae496a22978014363510de525f3370c135af3630ff36071baf2d03bd52ab

Observation 731ee871-f496-45fd-9911-98a3f9a9e62f · outbound

This paper cites Conditional prompt learning for vision-language models.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Conditional prompt learning for vision-language models

Reference 55

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source=pdf_text observed=2026-08-03T10:52:03.132563Z digest=sha256:7f1336c913b18404db289dd3a79c35c6bcaa7321905392a1726ffcb6e12490e7

Observation 3e9b26f4-0745-4ade-ac7b-82b0f3e8266f · outbound

This paper cites Learning to prompt for vision-language models.Inter- national Journal of Computer Vision (IJCV), 2022.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Learning to prompt for vision-language models.Inter- national Journal of Computer Vision (IJCV), 2022

Reference 56

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malformed identifier
no resolver link, observed 2026-08-03T10:52:03.139813Z

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source=pdf_text observed=2026-08-03T10:52:03.139813Z digest=sha256:c68d95d47ed6633063b68442f96164e78f63292352706941d310b0956cf2c4e3

Observation e21361fd-2f78-45fe-862f-a87546526746 · outbound

This paper cites an unresolved cited work.

SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning Unresolved cited work

Reference 2022

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parse uncertain
no resolver link, observed 2026-08-03T10:52:02.789054Z

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source=pdf_text observed=2026-08-03T10:52:02.789054Z digest=sha256:808f88b14d88f8bbd5bfc6e1792b2c7d13cb4e65cc771c1be2c8bdd10e420f26

Pith citing papers

No inbound Pith citation observations are available.