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Paper Citation Record · LEDGER

Clustering-based aggregate value regression

As of 8 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 0 inbound Pith citation observations for arXiv:2508.15567.

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

pith.paper-citation-record.v1
2508.15567 v1

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measured 74 of 74 reference resolution

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measured 74 of 74 standing notices

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measured 0 of 0 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.

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Reference resolution

74 of 74 outbound references displayed

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External citation measurements

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Outbound references

Observation 90d1ca91-8276-4079-bd95-5608a80f50b5 · outbound

This paper cites Food-101–Mining Discriminative Components with Random Forests.

Clustering-based aggregate value regression Food-101–Mining Discriminative Components with Random Forests

Reference 1

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Observation 8b78154c-4f1e-44fc-9371-d680e3521bfb · outbound

This paper cites Information maximization for few-shot learning.

Clustering-based aggregate value regression Information maximization for few-shot learning

Reference 2

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Observation 39a59357-48a0-4112-b2de-7da251277a48 · outbound

This paper cites Describing Textures in the Wild.

Clustering-based aggregate value regression Describing Textures in the Wild

Reference 3

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Observation 3fbe639c-9639-45b1-8055-62c4d1d3b442 · outbound

This paper cites Imagenet: A Large-Scale Hierarchical Image Database.

Clustering-based aggregate value regression Imagenet: A Large-Scale Hierarchical Image Database

Reference 4

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Observation 1de50f76-72a8-4b45-9d8d-bb7ae6851b80 · outbound

This paper cites A normality test for multivariate dependent samples.Signal Processing, 201:108705, 2022.

Clustering-based aggregate value regression A normality test for multivariate dependent samples.Signal Processing, 201:108705, 2022

Reference 5

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Observation 90a68c74-1e9f-477d-8865-e6e25aee306b · outbound

This paper cites Joint normality test via two-dimensional projection.

Clustering-based aggregate value regression Joint normality test via two-dimensional projection

Reference 6

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This paper cites Frus- tratingly easy test-time adaptation of vision-language models.

Clustering-based aggregate value regression Frus- tratingly easy test-time adaptation of vision-language models

Reference 7

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Observation 7f71ffdb-64fa-4fce-9bc1-f928ee83b0da · outbound

This paper cites Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories.

Clustering-based aggregate value regression Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

Reference 8

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Observation d6fa09dd-05e1-4810-ad63-53d2ca21cdbd · outbound

This paper cites Diverse data augmenta- tion with diffusions for effective test-time prompt tuning.

Clustering-based aggregate value regression Diverse data augmenta- tion with diffusions for effective test-time prompt tuning

Reference 9

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Observation 2694e7e9-c4ca-4b2c-886c-dd0c199cc657 · outbound

This paper cites Online Gaussian Test-Time Adaptation of Vision-Language Models.

Clustering-based aggregate value regression Online Gaussian Test-Time Adaptation of Vision-Language Models

Reference 10

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This paper cites Clip-adapter: Better vision-language models with feature adapters.IJCV, 132(2), 2024.

Clustering-based aggregate value regression Clip-adapter: Better vision-language models with feature adapters.IJCV, 132(2), 2024

Reference 11

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Observation c40554e3-ee62-4180-b2cb-6dc86e23d573 · outbound

This paper cites Dota: Distributional test-time adaptation of vision-language models.arXiv preprint arXiv:2409.19375, 2024.

Clustering-based aggregate value regression Dota: Distributional test-time adaptation of vision-language models.arXiv preprint arXiv:2409.19375, 2024

Reference 12

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Observation c7e527ae-31af-406b-8402-07807ebac181 · outbound

This paper cites Discriminant analysis by gaussian mixtures.Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1):155–176, 1996.

Clustering-based aggregate value regression Discriminant analysis by gaussian mixtures.Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1):155–176, 1996

Reference 13

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Clustering-based aggregate value regression Unresolved cited work

Reference 14

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This paper cites The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization.

Clustering-based aggregate value regression The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization

Reference 15

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Clustering-based aggregate value regression Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 16

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This paper cites Natural Adversarial Examples.

Clustering-based aggregate value regression Natural Adversarial Examples

Reference 17

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Observation fa4324ce-67e4-4d57-a137-fd3c082adb02 · outbound

This paper cites A class of invariant consistent tests for multivariate normality.

Clustering-based aggregate value regression A class of invariant consistent tests for multivariate normality

Reference 18

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Observation 397582c5-e774-42f2-a9fa-3d3c014ba65c · outbound

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Clustering-based aggregate value regression Test-time classifier adjustment module for model-agnostic domain generalization

Reference 19

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Observation 2a20ae8d-6097-433a-a3f3-2cbdcb2afe22 · outbound

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Clustering-based aggregate value regression Transductive inference for text classification using support vector machines

Reference 20

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Clustering-based aggregate value regression Label propagation for zero-shot classification with vision-language models

Reference 21

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Clustering-based aggregate value regression Efficient test-time adaptation of vision-language models

Reference 22

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Clustering-based aggregate value regression 3D Object Representations for Fine-Grained Categorization

Reference 23

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This paper cites Estimation of the precision matrix of a singular wishart distribution and its application in high-dimensional data.

Clustering-based aggregate value regression Estimation of the precision matrix of a singular wishart distribution and its application in high-dimensional data

Reference 24

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Observation 7da17092-8ba0-4ebe-9071-75090ee8de85 · outbound

This paper cites Ra-tta: Retrieval-augmented test-time adaptation for vision-language models.

Clustering-based aggregate value regression Ra-tta: Retrieval-augmented test-time adaptation for vision-language models

Reference 25

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This paper cites BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation.

Clustering-based aggregate value regression BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Reference 26

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Clustering-based aggregate value regression Align Before Fuse: Vision and Language Representation Learning with Momentum Distillation

Reference 27

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This paper cites Using discriminant analysis for multi-class classification: an experimental investigation.Knowledge and information systems, 10:453–472, 2006.

Clustering-based aggregate value regression Using discriminant analysis for multi-class classification: an experimental investigation.Knowledge and information systems, 10:453–472, 2006

Reference 28

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This paper cites Text and image are mutually beneficial: Enhancing training-free few-shot classification with clip.

Clustering-based aggregate value regression Text and image are mutually beneficial: Enhancing training-free few-shot classification with clip

Reference 29

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Clustering-based aggregate value regression Efficient and context-aware label propagation for zero-/few-shot training-free adaptation of vision-language model

Reference 30

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Clustering-based aggregate value regression Learning to propagate labels: Transductive propagation network for few-shot learning

Reference 31

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Clustering-based aggregate value regression Swapprompt: Test-time prompt adaptation for vision-language models

Reference 32

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Clustering-based aggregate value regression Fine-Grained Visual Classification of Aircraft

Reference 33

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Clustering-based aggregate value regression Test-time prompt tuning for zero-shot generalization in vision-language models

Reference 34

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Clustering-based aggregate value regression Black-box test-time prompt tuning for vision-language models

Reference 35

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This paper cites A random-projection based test of gaussianity for stationary processes.Computational Statistics & Data Analysis, 75:124–141, 2014.

Clustering-based aggregate value regression A random-projection based test of gaussianity for stationary processes.Computational Statistics & Data Analysis, 75:124–141, 2014

Reference 36

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Clustering-based aggregate value regression Automated Flower Classification over a Large Number of Classes

Reference 37

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Clustering-based aggregate value regression Cats and Dogs

Reference 38

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Observation edecc1ae-dafc-41dd-b97f-bcac466f34a8 · outbound

This paper cites The matrix cookbook.Technical University of Denmark, 7(15):510, 2008.

Clustering-based aggregate value regression The matrix cookbook.Technical University of Denmark, 7(15):510, 2008

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Observation 7d0f67cd-8566-42a9-85ac-b809310390d8 · outbound

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

Clustering-based aggregate value regression Learning transferable visual models from natural language supervision

Reference 40

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source=pdf_text observed=2026-08-05T17:52:15.729984Z digest=sha256:37e415a1942df21a837f0acec1b434c1d0590003e4ffdef8050b039cb4a7be6e

Observation ab303d02-910e-4206-a492-737a4d9f5e20 · outbound

This paper cites Do imagenet classifiers generalize to imagenet? InICML, 2019.

Clustering-based aggregate value regression Do imagenet classifiers generalize to imagenet? InICML, 2019

Reference 41

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source=pdf_text observed=2026-08-05T17:52:15.817022Z digest=sha256:2748f129d5cbb14acc2b756505949d45f7893c7166c038215889886b322acdf9

Observation 6f84c52a-c69e-4cba-839c-822755393e11 · outbound

This paper cites An extension of shapiro and wilk’s w test for normality to large samples.

Clustering-based aggregate value regression An extension of shapiro and wilk’s w test for normality to large samples

Reference 42

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source=pdf_text observed=2026-08-05T17:52:15.875442Z digest=sha256:f4e5d3d1f81beebc779f7465f3cf0fc8d0e204c3be0331a9a4b249a4b6b901cd

Observation 41539fea-444c-484d-abae-62906e86481a · outbound

This paper cites Align your prompts: Test-time prompting with distribution alignment for zero-shot generalization.

Clustering-based aggregate value regression Align your prompts: Test-time prompting with distribution alignment for zero-shot generalization

Reference 43

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source=pdf_text observed=2026-08-05T17:52:15.968095Z digest=sha256:2cb3cb1961b5992af57555258775f1c4982aeb2d88c1e0c583695a33d492c808

Observation d839f897-7875-4b26-8910-aa7763776cb9 · outbound

This paper cites An analysis of variance test for normality.Biometrika, 52(3):591– 611, 1965.

Clustering-based aggregate value regression An analysis of variance test for normality.Biometrika, 52(3):591– 611, 1965

Reference 44

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source=pdf_text observed=2026-08-05T17:52:16.003212Z digest=sha256:9feb0c38c762203a7b4f4442ebffe00e5bed1ee9bfe321ab759f0950c778b02b

Observation f6b610c0-94f4-4320-8257-94b9d4f41f78 · outbound

This paper cites High-dimensional linear discriminant analysis classifier for spiked covariance model.Journal of Machine Learning Research, 21(112):1–24, 2020.

Clustering-based aggregate value regression High-dimensional linear discriminant analysis classifier for spiked covariance model.Journal of Machine Learning Research, 21(112):1–24, 2020

Reference 45

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source=pdf_text observed=2026-08-05T17:52:16.064041Z digest=sha256:7010badbf1941bc5ff040405c7721503ab33e54f5927cbd726473a78b8d7837c

Observation 7206b777-a3b2-40e3-8f9d-7e2e864798aa · outbound

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

Clustering-based aggregate value regression UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 46

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source=pdf_text observed=2026-08-05T17:52:16.138204Z digest=sha256:18ed7c2fcc04507536f0870dd0d89f5e18d57567522c968698889b74c40730e7

Observation 07b41894-4c46-46a7-a4f4-e1efad15ab55 · outbound

This paper cites Just shift it: Test-time prototype shifting for zero-shot generalization with vision-language models.

Clustering-based aggregate value regression Just shift it: Test-time prototype shifting for zero-shot generalization with vision-language models

Reference 47

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source=pdf_text observed=2026-08-05T17:52:16.226694Z digest=sha256:242343f285e3d8a9d26067e7ce4a20b4dc27707c76f954c421e734b32c8c9e52

Observation e535d5c7-8b48-487f-ba52-ba8945c5ef9b · outbound

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

Clustering-based aggregate value regression Sus-x: Training-free name-only transfer of vision-language models

Reference 48

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source=pdf_text observed=2026-08-05T17:52:16.362852Z digest=sha256:881505215e7a572a372d5f5c6d14cb1afd9ed88ac17a9b02bd83824f21ae2c1c

Observation 162ff320-0092-479c-a30b-ecb1ddc0b4fd · outbound

This paper cites Discriminative gaussian process latent variable model for classification.

Clustering-based aggregate value regression Discriminative gaussian process latent variable model for classification

Reference 49

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source=pdf_text observed=2026-08-05T17:52:16.448902Z digest=sha256:83013bbfdf2bcaff9aeb1f2cf2b77f8f5ab88cfb0d6900d06678112f7a1470d6

Observation 8887e1aa-7aea-42af-b276-489b2fe00b9c · outbound

This paper cites Tent: Fully test-time adaptation by entropy minimization.

Clustering-based aggregate value regression Tent: Fully test-time adaptation by entropy minimization

Reference 50

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source=pdf_text observed=2026-08-05T17:52:16.512410Z digest=sha256:4729363a0fabfe32c565188cbadc20d7333a6daf624378e4d03e5441009a3d70

Observation 5f555050-64ef-4ebe-80e8-5515718e6d20 · outbound

This paper cites Learning Robust Global Representations by Penalizing Local Rredictive Power.

Clustering-based aggregate value regression Learning Robust Global Representations by Penalizing Local Rredictive Power

Reference 51

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source=pdf_text observed=2026-08-05T17:52:16.587467Z digest=sha256:92ceb53b3bf5dc444aa171548fcbb7122d463c93468e516eb879c912367031fb

Observation 2ddeb9a1-8c32-4145-ad68-e8f0277f3b28 · outbound

This paper cites A hard-to-beat baseline for training-free clip-based adaptation.

Clustering-based aggregate value regression A hard-to-beat baseline for training-free clip-based adaptation

Reference 52

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source=pdf_text observed=2026-08-05T17:52:16.647576Z digest=sha256:ace9d7c839a1b5c0b74df725a1b3cb42b5c44cc022b9a0d8057eb84dab6f8de9

Observation 5257d646-075c-4e26-b9e5-de656dfbd317 · outbound

This paper cites Is less more? exploring token condensation as training-free adaptation for clip.

Clustering-based aggregate value regression Is less more? exploring token condensation as training-free adaptation for clip

Reference 53

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source=pdf_text observed=2026-08-05T17:52:16.755925Z digest=sha256:829e82c7bc5c3ad5dfa199cf70a316cdb4437da1e3924945cc23996f29f22c5c

Observation a1689d06-8da2-4e84-bef0-fe47e69904f1 · outbound

This paper cites Sun Database: Large-Scale Scene Recognition from Abbey to Zoo.

Clustering-based aggregate value regression Sun Database: Large-Scale Scene Recognition from Abbey to Zoo

Reference 54

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source=pdf_text observed=2026-08-05T17:52:16.825406Z digest=sha256:e3ad368c96814787343bb97cecdb3f99205597d2496faf17c2770edecd2bf3a6

Observation 97a306f0-a8dd-4e36-990a-2400e2777f1c · outbound

This paper cites Dynaprompt: Dynamic test-time prompt tuning.

Clustering-based aggregate value regression Dynaprompt: Dynamic test-time prompt tuning

Reference 55

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-05T17:52:16.894825Z digest=sha256:536285a4467e5a0a303deeccd274ec236eed0d95b3fb3ab459063c1d2d0c82b2

Observation 9dea127c-df4e-499f-a6df-aa24b25d14cd · outbound

This paper cites C-tpt: Calibrated test-time prompt tuning for vision-language models via text feature dispersion.

Clustering-based aggregate value regression C-tpt: Calibrated test-time prompt tuning for vision-language models via text feature dispersion

Reference 56

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-05T17:52:16.991674Z digest=sha256:7d0fc830af5f171b597d175d13cc4a2e7feea5037b932298bbcb7c22699b70f7

Observation 6d269f3b-4654-4d03-88c0-a90b19c14e8b · outbound

This paper cites Task residual for tuning vision- language models.

Clustering-based aggregate value regression Task residual for tuning vision- language models

Reference 57

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source=pdf_text observed=2026-08-05T17:52:17.068354Z digest=sha256:f92680fbe5ef31ccedb84c7d5573bf6b071fca38fd57267f287801f25cd568f5

Observation 17b37dd4-4066-415b-99d8-c7e125ba85ff · outbound

This paper cites On the test-time zero-shot generalization of vision- language models: Do we really need prompt learning? InCVPR, 2024.

Clustering-based aggregate value regression On the test-time zero-shot generalization of vision- language models: Do we really need prompt learning? InCVPR, 2024

Reference 58

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source=pdf_text observed=2026-08-05T17:52:17.159060Z digest=sha256:b60baffc60cffd0ee974a8566fd0fb284a8646dd3d89f64e6956e30a9e0d6d04

Observation 123757ff-eb55-40aa-884a-d03ee22ff986 · outbound

This paper cites Realistic test-time adaptation of vision-language models.

Clustering-based aggregate value regression Realistic test-time adaptation of vision-language models

Reference 59

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source=pdf_text observed=2026-08-05T17:52:17.230232Z digest=sha256:bd5764604da306e62d7957a0e8677636cb9f003ce5392f8706d104ee782900f2

Observation 100871cc-205f-4ed6-a375-846a5ba4df64 · outbound

This paper cites Boosting vision-language models with transduction.

Clustering-based aggregate value regression Boosting vision-language models with transduction

Reference 60

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source=pdf_text observed=2026-08-05T17:52:17.293413Z digest=sha256:ac8e7edaac9d140f8d5f498adb3daa5b33f03155038c14f2864386faec485724

Observation 26a95091-b029-4fad-95d1-6b0033a7c385 · outbound

This paper cites Boosting vision-language models for histopathology classification: Predict all at once.

Clustering-based aggregate value regression Boosting vision-language models for histopathology classification: Predict all at once

Reference 61

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source=pdf_text observed=2026-08-05T17:52:17.383392Z digest=sha256:bd035c02393fdb3be3e0b5e79365c2f4cc56fb238bf9c87a7a8eb4c0088db011

Observation 1388aee8-9ae9-4bb7-bc11-1804b6419dd0 · outbound

This paper cites Dual prototype evolving for test-time generalization of vision-language models.

Clustering-based aggregate value regression Dual prototype evolving for test-time generalization of vision-language models

Reference 62

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source=pdf_text observed=2026-08-05T17:52:17.455559Z digest=sha256:c3f7bcf8d612ae3411fd08d835145c495a288cc3f7cb4f94ba5016e6e614baba

Observation a7185619-ab39-4a2b-bbb2-ef1394a06b9a · outbound

This paper cites Historical test-time prompt tuning for vision foundation models.

Clustering-based aggregate value regression Historical test-time prompt tuning for vision foundation models

Reference 63

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source=pdf_text observed=2026-08-05T17:52:17.514778Z digest=sha256:6ed2eb8158ed203db250f7d7fbb20e83ce49b51e8aa5f01c0bc52ac4d7e75fd3

Observation a441098c-0307-4936-b613-17a3bd923ea8 · outbound

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

Clustering-based aggregate value regression Tip-adapter: Training-free adaption of clip for few-shot classification

Reference 64

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source=pdf_text observed=2026-08-05T17:52:17.632482Z digest=sha256:0022badbd33158688e29249a8104e3b8e5e31b08f2468aadf00553eb7fa71fb4

Observation 2072ea5d-d3cd-4cc8-8b54-811bc62f03a5 · outbound

This paper cites Boostadapter: Improving vision-language test-time adaptation via regional bootstrapping.

Clustering-based aggregate value regression Boostadapter: Improving vision-language test-time adaptation via regional bootstrapping

Reference 65

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source=pdf_text observed=2026-08-05T17:52:17.700628Z digest=sha256:1cff81fa5ec4635015a00c22c24948429e78fe1c526e2602e0e12e3618535933

Observation 1e4bfd14-07bf-474f-96c3-435de18365a4 · outbound

This paper cites Dual memory networks: A versatile adaptation approach for vision-language models.

Clustering-based aggregate value regression Dual memory networks: A versatile adaptation approach for vision-language models

Reference 66

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source=pdf_text observed=2026-08-05T17:52:17.835924Z digest=sha256:a1d4776d2f3c5767ef3734e723dd1b10409922ec7b3bf55c26293999d7d0ea5d

Observation 586308bb-695a-4ea3-a2cf-850e61b5d95b · outbound

This paper cites Dpcore: Dynamic prompt coreset for continual test-time adaptation.

Clustering-based aggregate value regression Dpcore: Dynamic prompt coreset for continual test-time adaptation

Reference 67

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source=pdf_text observed=2026-08-05T17:52:17.936800Z digest=sha256:e2ffeeeb9ecdee697769bca8812949d00c9446fbec4b2da4df10c7b9e3bcc81e

Observation 8f350e15-dc50-41a7-a7e4-5e5f6ebd1af5 · outbound

This paper cites Learning with local and global consistency.

Clustering-based aggregate value regression Learning with local and global consistency

Reference 68

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source=pdf_text observed=2026-08-05T17:52:18.010214Z digest=sha256:833809dc88d3f307191f8a69807ab2a7e2cabbb335cdc111d20fe0dc716c01d7

Observation 13566bc7-d370-4ec6-af69-52eca2fe4c5f · outbound

This paper cites Bayesian test-time adaptation for vision-language models.

Clustering-based aggregate value regression Bayesian test-time adaptation for vision-language models

Reference 69

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source=pdf_text observed=2026-08-05T17:52:18.120894Z digest=sha256:118c98bce64f8d1e30845af082ac3184f511f94939b732b1deda12081507b5c9

Observation 1b20693e-c8d2-43db-863a-0bac02456020 · outbound

This paper cites Not all features matter: Enhancing few-shot clip with adaptive prior refinement.

Clustering-based aggregate value regression Not all features matter: Enhancing few-shot clip with adaptive prior refinement

Reference 70

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source=pdf_text observed=2026-08-05T17:52:18.198323Z digest=sha256:e2d73b3980a67419081d8a1e7095301312ef0e8c06b744c78bf63d7080349d92

Observation ad3c4893-6e91-4d85-9ca4-fb9ba612295c · outbound

This paper cites Enhancing zero-shot vision models by label-free prompt distribution learning and bias correcting.

Clustering-based aggregate value regression Enhancing zero-shot vision models by label-free prompt distribution learning and bias correcting

Reference 71

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source=pdf_text observed=2026-08-05T17:52:18.261191Z digest=sha256:76cfa5197411b0235c5737780f9a55f81cddd3325ff26d8423939230df2a1c43

Observation e6ad9063-e25b-4256-a825-1de392764050 · outbound

This paper cites Awt: Transferring vision-language models via augmentation, weighting, and transportation.

Clustering-based aggregate value regression Awt: Transferring vision-language models via augmentation, weighting, and transportation

Reference 72

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source=pdf_text observed=2026-08-05T17:52:18.337167Z digest=sha256:787b2b5c8b80624850629cb5b4007edb94e1503d9afdc9e06694dc5fbf587540

Observation ae1a233a-76b0-4f27-baf7-e907078da5bf · outbound

This paper cites Efficient Test-Time Prompt Tuning for Vision-Language Models.

Clustering-based aggregate value regression Efficient Test-Time Prompt Tuning for Vision-Language Models

Reference 73

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source=pdf_text observed=2026-08-05T17:52:18.399067Z digest=sha256:2d92735a26d36e3a6d99c91c29b330b2d1995643e5906bbb6f09aa780a0ac511

Observation 49710201-58b1-4500-89fe-2dc538b7a1aa · outbound

This paper cites Laplacian regularized few-shot learning.

Clustering-based aggregate value regression Laplacian regularized few-shot learning

Reference 74

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no resolver link, observed 2026-08-05T17:52:18.464538Z

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source=pdf_text observed=2026-08-05T17:52:18.464538Z digest=sha256:bfd63c5ca499edaa73c0cc70b165eed90caf38ab9a0eedb4b51e9a0b254e173b

Pith citing papers

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