ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models | Paper Citation Record

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This paper cites Kernels for vector-valued functions: A review.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Kernels for vector-valued functions: A review

Reference 1

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This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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This paper cites Improved few-shot visual classification.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Improved few-shot visual classification

Reference 3

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Food-101–mining discriminative compo- nents with random forests

Reference 4

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This paper cites Universal multi-task kernels.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Universal multi-task kernels

Reference 5

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This paper cites PLOT: Prompt Learning with Optimal Transport for Vision-Language Models.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

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This paper cites Clip2scene: Towards label-efficient 3d scene understanding by clip.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Clip2scene: Towards label-efficient 3d scene understanding by clip

Reference 7

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This paper cites Describing textures in the wild.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Describing textures in the wild

Reference 8

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Imagenet: A large-scale hierarchical image database

Reference 9

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This paper cites Data determines distributional robustness in contrastive language image pre-training (clip).

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Data determines distributional robustness in contrastive language image pre-training (clip)

Reference 10

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This paper cites Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

Reference 11

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This paper cites CLIP-Adapter: Better Vision-Language Models with Feature Adapters.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models CLIP-Adapter: Better Vision-Language Models with Feature Adapters

Reference 12

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This paper cites Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nystr\"om Method, and Use of Kernels in Machine Learning: Tutorial and Survey.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nystr\"om Method, and Use of Kernels in Machine Learning: Tutorial and Survey

Reference 13

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This paper cites Open-vocabulary Object Detection via Vision and Language Knowledge Distillation.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Open-vocabulary Object Detection via Vision and Language Knowledge Distillation

Reference 14

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This paper cites Calip: Zero- shot enhancement of clip with parameter-free attention.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Calip: Zero- shot enhancement of clip with parameter-free attention

Reference 15

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This paper cites The elements of statistical learning: data mining, inference, and prediction, vol- ume 2.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models The elements of statistical learning: data mining, inference, and prediction, vol- ume 2

Reference 16

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This paper cites Robust nonparametric regression with metric-space valued output.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Robust nonparametric regression with metric-space valued output

Reference 17

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Eurosat: A novel dataset and deep learning benchmark for land use and land cover classi- fication

Reference 18

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models The many faces of robustness: A critical analysis of out- of-distribution generalization

Reference 19

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This paper cites Natural adversarial exam- ples.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Natural adversarial exam- ples

Reference 20

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This paper cites Scaling up visual and vision- language representation learning with noisy text su- pervision.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Scaling up visual and vision- language representation learning with noisy text su- pervision

Reference 21

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This paper cites Operator-valued kernels for learning from func- tional response data.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Operator-valued kernels for learning from func- tional response data

Reference 22

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This paper cites Maple: Multi-modal prompt learning.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Maple: Multi-modal prompt learning

Reference 23

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models 3d object representations for fine-grained cate- gorization

Reference 24

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This paper cites Adversarial regres- sion with doubly non-negative weighting matrices.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Adversarial regres- sion with doubly non-negative weighting matrices

Reference 25

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This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 26

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This paper cites Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm

Reference 27

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This paper cites Multimodality helps unimodality: Cross-modal few-shot learning with multimodal mod- els.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Multimodality helps unimodality: Cross-modal few-shot learning with multimodal mod- els

Reference 28

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This paper cites Frozen clip models are efficient video learners.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Frozen clip models are efficient video learners

Reference 29

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This paper cites Image segmen- tation using text and image prompts.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Image segmen- tation using text and image prompts

Reference 30

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This paper cites Fine-Grained Visual Classification of Aircraft.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Fine-Grained Visual Classification of Aircraft

Reference 31

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This paper cites On learning vector-valued functions.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models On learning vector-valued functions

Reference 32

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This paper cites On estimating regression.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models On estimating regression

Reference 33

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This paper cites Quality not quan- tity: On the interaction between dataset design and robustness of clip.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Quality not quan- tity: On the interaction between dataset design and robustness of clip

Reference 34

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This paper cites Au- tomated flower classification over a large number of classes.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Au- tomated flower classification over a large number of classes

Reference 35

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This paper cites Generative local metric learning for kernel regression.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Generative local metric learning for kernel regression

Reference 36

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This paper cites Introduction to radial basis function networks, 1996.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Introduction to radial basis function networks, 1996

Reference 37

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This paper cites Black box few-shot adaptation for vision-language models.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Black box few-shot adaptation for vision-language models

Reference 38

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This paper cites Practical perfor- mance of several data driven bandwidth selectors.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Practical perfor- mance of several data driven bandwidth selectors

Reference 39

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This paper cites Cats and dogs.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Cats and dogs

Reference 40

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This paper cites Learning transferable vi- sual models from natural language supervision.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Learning transferable vi- sual models from natural language supervision

Reference 41

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This paper cites Random features for large-scale kernel machines.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Random features for large-scale kernel machines

Reference 42

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This paper cites Zero-shot text-to-image generation.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Zero-shot text-to-image generation

Reference 43

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This paper cites Do imagenet classifiers gen- eralize to imagenet? In International conference on machine learning, pp.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Do imagenet classifiers gen- eralize to imagenet? In International conference on machine learning, pp

Reference 44

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This paper cites Consistency-guided Prompt Learning for Vision-Language Models.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Consistency-guided Prompt Learning for Vision-Language Models

Reference 45

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This paper cites Multivariate locally weighted least squares regression.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Multivariate locally weighted least squares regression

Reference 46

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Align your prompts: Test-time prompting with distribution alignment for zero-shot generalization

Reference 47

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This paper cites Proposalclip: Unsupervised open-category object proposal generation via exploiting clip cues.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Proposalclip: Unsupervised open-category object proposal generation via exploiting clip cues

Reference 48

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Test-time prompt tuning for zero-shot generalization in vision-language models

Reference 49

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This paper cites A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models

Reference 50

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 51

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This paper cites Alpha-CLIP: A CLIP Model Focusing on Wherever You Want.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Alpha-CLIP: A CLIP Model Focusing on Wherever You Want

Reference 52

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Sus-x: Training-free name-only transfer of vision-language models

Reference 53

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This paper cites ActionCLIP: A New Paradigm for Video Action Recognition.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models ActionCLIP: A New Paradigm for Video Action Recognition

Reference 54

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ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation

Reference 55

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ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Metric learn- ing for kernel regression

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:27.946720Z

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Sun database: Large- scale scene recognition from abbey to zoo

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:27.929010Z

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Visual- language prompt tuning with knowledge-guided con- text optimization

Reference 58

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This paper cites Orthogonal random features.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Orthogonal random features

Reference 59

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ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Task residual for tuning vision-language mod- els

Reference 60

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ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Unified Vision and Language Prompt Learning

Reference 61

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This paper cites Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Reference 62

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This paper cites Pointclip: Point cloud understanding by clip.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Pointclip: Point cloud understanding by clip

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:27.860820Z

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This paper cites Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:27.843680Z

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Dual memory networks: A versatile adaptation approach for vision-language models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:27.824440Z

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ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Conditional prompt learning for vision- language models

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:27.804382Z

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Observation 716c2a72-85a7-45ec-b9fb-ef3ca2ffac9d · outbound

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Learning to prompt for vision-language models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:27.787594Z

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Observation 2ed0a82d-ff2f-463b-a9af-6d6743eef913 · outbound

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

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Not all features matter: Enhancing few-shot clip with adaptive prior refinement

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:27.769355Z

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ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-10T18:40:27.734124Z

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Observation 5e3df57c-17d6-4305-904d-a6ebedc92d48 · outbound

This paper cites Detailed derivations A.1.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Detailed derivations A.1

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:40:27.752363Z

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