Paper Citation Record · LEDGER

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting

As of 15 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2507.20834.

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

pith.paper-citation-record.v1
2507.20834 v1

Coverage vector

measured 53 of 53 reference resolution

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measured 54 of 54 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

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External citation measurements

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Outbound references

Observation e87f0dd0-161a-4c6f-a371-7943c3e68476 · outbound

This paper cites Analysis of representations for domain adaptation.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Analysis of representations for domain adaptation

Reference 1

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Observation a53a75d9-9601-4b18-bc5c-7cf6747271a8 · outbound

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

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Food-101 – mining discriminative components with random forests

Reference 2

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Observation d61bc0de-2536-4925-8934-03bc7a17a1cb · outbound

This paper cites Self-supervised learning for few-shot im- age classification.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Self-supervised learning for few-shot im- age classification

Reference 3

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Observation 063c82c7-3e62-41da-8ded-91ccccaf2107 · outbound

This paper cites A Closer Look at Few-shot Classification.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting A Closer Look at Few-shot Classification

Reference 4

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This paper cites Describing textures in the wild.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Describing textures in the wild

Reference 5

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Observation 6669eafe-2a71-4808-9650-122de2c2e10c · outbound

This paper cites Salun: Empowering machine un- learning via gradient-based weight saliency in both image classification and generation.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Salun: Empowering machine un- learning via gradient-based weight saliency in both image classification and generation

Reference 6

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Observation 8dd26877-cdac-4764-974d-d044718cc924 · outbound

This paper cites Learning gen- erative visual models from few training examples: An in- cremental bayesian approach tested on 101 object cate- gories.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Learning gen- erative visual models from few training examples: An in- cremental bayesian approach tested on 101 object cate- gories

Reference 7

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Observation c311a435-f698-46df-b080-f1749172b1ff · outbound

This paper cites Fast machine unlearning without retraining through selective synaptic dampening.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Fast machine unlearning without retraining through selective synaptic dampening

Reference 8

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Observation cf1df365-9633-442d-bf7e-4483d4ec0a72 · outbound

This paper cites Gen- eralization bounds for few-shot transfer learning with pre- trained classifiers.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Gen- eralization bounds for few-shot transfer learning with pre- trained classifiers

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:17:47.749441Z

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Observation 20ec5353-ac32-40e7-a6f5-e6d465c01fb1 · outbound

This paper cites Clip-adapter: Better vision-language models with feature adapters.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Clip-adapter: Better vision-language models with feature adapters

Reference 10

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Observation 2e1c73c0-c711-467f-a51d-43cb341d4071 · outbound

This paper cites Amne- siac machine learning, 2020.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Amne- siac machine learning, 2020

Reference 11

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Observation 0cd64bc4-4868-4eb5-97ee-574dc66f5659 · outbound

This paper cites A kernel method for the two- sample-problem.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting A kernel method for the two- sample-problem

Reference 12

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Observation b68934a5-a705-49eb-9e1a-7aace2365897 · outbound

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

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Visual-language prompt tuning with knowledge-guided context optimization

Reference 13

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This paper cites Tcp: Textual-based class-aware prompt tuning for visual-language model.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Tcp: Textual-based class-aware prompt tuning for visual-language model

Reference 14

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Observation e87595d4-4d34-4af2-ad30-fce2353705cc · outbound

This paper cites Fine-grained visual-textual representation learning.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Fine-grained visual-textual representation learning

Reference 15

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Observation 2372bef2-687a-4518-bee5-1e5fda07e29a · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 16

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Observation d4a46fef-096c-4858-84ab-45b6a75009a3 · outbound

This paper cites Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference

Reference 17

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Observation 92a673a4-df35-4787-b15e-1d0d747a1c94 · outbound

This paper cites Maple: Multi-modal prompt learning.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Maple: Multi-modal prompt learning

Reference 18

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Observation ed19e21b-4f83-495c-a386-9a8c0d95ee9e · outbound

This paper cites Self-regulating prompts: Foundational model adaptation without forgetting.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Self-regulating prompts: Foundational model adaptation without forgetting

Reference 19

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Observation 58a058f3-1c47-41b5-a624-701f1a0acca5 · outbound

This paper cites Novel dataset for fine-grained image categorization.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Novel dataset for fine-grained image categorization

Reference 20

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Observation f4efcd33-869e-4e5f-978d-1e77ada0f895 · outbound

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

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting 3d object representations for fine-grained categorization

Reference 21

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Observation 09556911-1a32-489d-ad09-11f4e930223c · outbound

This paper cites Clip adaptation by intra-modal overlap reduction.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Clip adaptation by intra-modal overlap reduction

Reference 22

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Observation a76679e7-8828-4022-be5b-7ddd9491769f · outbound

This paper cites Kravets and V.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Kravets and V

Reference 23

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Observation f20802f4-4ae7-4902-80c1-59c7cf5dbb0f · outbound

This paper cites Namboodiri.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Namboodiri

Reference 24

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Observation 221b4a48-3b3d-468e-8bf1-123010e200c6 · outbound

This paper cites Fine-grained visual classi- fication of aircraft, 2013.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Fine-grained visual classi- fication of aircraft, 2013

Reference 25

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This paper cites McInnes, J.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting McInnes, J

Reference 26

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This paper cites Nilsback and Andrew Zisserman.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Nilsback and Andrew Zisserman

Reference 27

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Observation 1a2f9074-be74-4770-9908-629fe5f2b143 · outbound

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

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Learning transferable visual models from natural language supervision

Reference 28

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This paper cites Transtrum, James P.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Transtrum, James P

Reference 29

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This paper cites Consistency-guided prompt learning for vision-language models.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Consistency-guided prompt learning for vision-language models

Reference 30

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Observation a5001b1c-894b-45e3-b841-1dcef81a5b2c · outbound

This paper cites Membership inference attacks against machine learning models.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Membership inference attacks against machine learning models

Reference 31

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Observation f9967565-1ada-4777-b2d2-9bbb2fc581bf · outbound

This paper cites Plantdoc: A dataset for visual plant disease detection.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Plantdoc: A dataset for visual plant disease detection

Reference 32

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Observation ebdfa429-2fc2-4765-a9f5-a24c588fdbec · outbound

This paper cites Ucf101: A dataset of 101 human actions classes from videos in the wild, 2012.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Ucf101: A dataset of 101 human actions classes from videos in the wild, 2012

Reference 33

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This paper cites Thudi, G.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Thudi, G

Reference 34

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Observation 45ffc4d1-7323-4be9-b0da-f89653b611f7 · outbound

This paper cites Sus-x: Training-free name-only transfer of vision-language models.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Sus-x: Training-free name-only transfer of vision-language models

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:17:47.169528Z

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Observation 1e0e1660-4bde-4013-85ba-eaa08c421423 · outbound

This paper cites Matching networks for one shot learning.Ad- vances in neural information processing systems , 29, 2016.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Matching networks for one shot learning.Ad- vances in neural information processing systems , 29, 2016

Reference 36

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Observation c32a8145-62bc-4915-8b0b-5c49ec31846a · outbound

This paper cites Machine unlearning of features and la- bels.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Machine unlearning of features and la- bels

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:17:47.136502Z

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This paper cites Ehinger, Aude Oliva, and Antonio Torralba.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Ehinger, Aude Oliva, and Antonio Torralba

Reference 38

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This paper cites Sep: Self-enhanced prompt tuning for visual-language model,.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Sep: Self-enhanced prompt tuning for visual-language model,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:17:47.104885Z

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Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Unresolved cited work

Reference 40

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This paper cites Low-rank few-shot adaptation of vision-language models.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Low-rank few-shot adaptation of vision-language models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:17:47.066177Z

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Observation 747dc2a3-5556-4c44-a87e-d36366b2bad6 · outbound

This paper cites Tip- adapter: Training-free adaption of clip for few-shot classi- fication.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Tip- adapter: Training-free adaption of clip for few-shot classi- fication

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:17:47.048312Z

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Observation c9bcd62f-6b86-4389-86b0-ec195df1349e · outbound

This paper cites Learning to prompt for vision-language models.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Learning to prompt for vision-language models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:17:47.032178Z

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Observation 543a361e-45b7-4828-ac6c-5885429606a0 · outbound

This paper cites Conditional prompt learning for vision-language mod- els.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Conditional prompt learning for vision-language mod- els

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:17:47.011756Z

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Observation 3fa67172-6409-4bb2-8bf1-f3d575f9338b · outbound

This paper cites Prompt-aligned gradient for prompt tuning.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Prompt-aligned gradient for prompt tuning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:17:46.992721Z

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Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Unresolved cited work

Reference 46

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Observation c264a5d7-3882-4804-a609-b2de63d30652 · outbound

This paper cites Our work builds upon the Self- Enhanced Prompt Tuning (SEP) method, which refines vi- sual prompts using a Token Fusion Module (TFM).

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Our work builds upon the Self- Enhanced Prompt Tuning (SEP) method, which refines vi- sual prompts using a Token Fusion Module (TFM)

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:17:46.954257Z

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Observation 232ea8ac-63d0-4512-b6ce-a8a57cc1b5ef · outbound

This paper cites Ablation on α parameter of SEPRES.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Ablation on α parameter of SEPRES

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:17:46.936831Z

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Observation a6c5c238-6288-434c-82e6-5bb2c5062d00 · outbound

This paper cites 4.1 of the main paper.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting 4.1 of the main paper

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:17:46.920396Z

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Observation 4eafbfeb-3f29-4b8a-8fd3-6827a90722ff · outbound

This paper cites an unresolved cited work.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-06T13:17:46.903485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e61fdcd5-7465-489d-aa29-f3d1f88e83f7 · outbound

This paper cites an unresolved cited work.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-06T13:17:46.885505Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 35733d7f-11fc-4d66-b7f4-e8836b043eea · outbound

This paper cites 5.3 evaluated under two distinct settings - excluding a subset and unlearning a subset.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting 5.3 evaluated under two distinct settings - excluding a subset and unlearning a subset

Reference 52

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T13:17:46.846620Z

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Observation a9e8f19b-9671-4e38-beb4-7a0eb4a78047 · outbound

This paper cites Default runs unlearned CLIP.

Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting Default runs unlearned CLIP

Reference 53

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T13:17:46.749919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation fd8765f9-8732-4ee0-aa3b-513ba2534aac · inbound

TCLA: Training-Free Class-wise Logit Adaptation for Medical Vision-Language Models cites this paper.

TCLA: Training-Free Class-wise Logit Adaptation for Medical Vision-Language Models Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-13T02:07:57.055712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.