Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models | Paper Citation Record

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This paper cites Stylip: Multi-scale style- conditioned prompt learning for clip-based domain gener- alization.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Stylip: Multi-scale style- conditioned prompt learning for clip-based domain gener- alization

Reference 1

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

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Food-101–mining discriminative components with random forests

Reference 2

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This paper cites LASP: Text-to- text optimization for language-aware soft prompting of vi- sion & language models.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models LASP: Text-to- text optimization for language-aware soft prompting of vi- sion & language models

Reference 3

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

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

Reference 4

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This paper cites TokenMixup: Efficient attention-guided token-level data augmentation for transformers.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models TokenMixup: Efficient attention-guided token-level data augmentation for transformers

Reference 5

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This paper cites Describing textures in the wild.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Describing textures in the wild

Reference 6

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This paper cites RandAugment: Practical automated data augmenta- tion with a reduced search space.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models RandAugment: Practical automated data augmenta- tion with a reduced search space

Reference 7

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Observation 89c20a9d-c461-4792-ae7f-897101ddb70c · outbound

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

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models ImageNet: A large-scale hierarchical image database

Reference 8

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Observation 6cb34863-c9a6-4791-b571-1d395c1c7e84 · outbound

This paper cites Bayesian prompt learn- ing for image-language model generalization.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Bayesian prompt learn- ing for image-language model generalization

Reference 9

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Observation dbeff9d6-0a42-42f2-9ff6-e10f89f3f288 · outbound

This paper cites De- coupling zero-shot semantic segmentation.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models De- coupling zero-shot semantic segmentation

Reference 10

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This paper cites Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 11

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Observation a0d50bc8-aed7-4d12-b9fe-3bba72eff8b7 · outbound

This paper cites Prompt- Det: Towards open-vocabulary detection using uncurated images.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Prompt- Det: Towards open-vocabulary detection using uncurated images

Reference 12

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Observation e99bd2ba-65d9-4e89-b62c-d1c9e880227b · outbound

This paper cites StyleAdv: Meta style adversarial training for cross-domain few-shot learning.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models StyleAdv: Meta style adversarial training for cross-domain few-shot learning

Reference 13

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This paper cites CLIP-adapter: Better vision-language models with fea- ture adapters.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models CLIP-adapter: Better vision-language models with fea- ture adapters

Reference 14

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This paper cites CLIP-S4: Language-guided self-supervised semantic seg- mentation.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models CLIP-S4: Language-guided self-supervised semantic seg- mentation

Reference 15

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This paper cites EuroSAT: A novel dataset and deep learn- ing benchmark for land use and land cover classification.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models EuroSAT: A novel dataset and deep learn- ing benchmark for land use and land cover classification

Reference 16

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

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models The many faces of robust- ness: A critical analysis of out-of-distribution generalization

Reference 17

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This paper cites Natural adversarial examples.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Natural adversarial examples

Reference 18

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This paper cites StyleMix: Sep- arating content and style for enhanced data augmentation.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models StyleMix: Sep- arating content and style for enhanced data augmentation

Reference 19

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This paper cites Arbitrary style transfer in real-time with adaptive instance normalization.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Arbitrary style transfer in real-time with adaptive instance normalization

Reference 20

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Observation cefd05dc-3dfe-4d98-8c0f-9843819bf688 · outbound

This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 21

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This paper cites MaPLe: Multi-modal prompt learning.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models MaPLe: Multi-modal prompt learning

Reference 22

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

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Self-regulating prompts: Foundational model adaptation without forgetting

Reference 23

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This paper cites How to adapt your large-scale vision-and-language model.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models How to adapt your large-scale vision-and-language model

Reference 24

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

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models 3D object representations for fine-grained categorization

Reference 25

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This paper cites Cross- domain ensemble distillation for domain generalization.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Cross- domain ensemble distillation for domain generalization

Reference 26

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This paper cites Decoupled Weight Decay Regularization.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Decoupled Weight Decay Regularization

Reference 27

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This paper cites Prompt distribution learning.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Prompt distribution learning

Reference 28

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This paper cites Fine-Grained Visual Classification of Aircraft.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Fine-Grained Visual Classification of Aircraft

Reference 29

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This paper cites I2DFormer: Learning image to docu- ment attention for zero-shot image classification.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models I2DFormer: Learning image to docu- ment attention for zero-shot image classification

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This paper cites Automated flower classification over a large number of classes.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Automated flower classification over a large number of classes

Reference 31

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Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Cats and dogs

Reference 32

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This paper cites Learn- ing transferable visual models from natural language super- vision.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Learn- ing transferable visual models from natural language super- vision

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This paper cites DenseCLIP: Language-guided dense prediction with context-aware prompting.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models DenseCLIP: Language-guided dense prediction with context-aware prompting

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This paper cites Fine-tuned CLIP models are efficient video learners.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Fine-tuned CLIP models are efficient video learners

Reference 35

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This paper cites Do ImageNet classifiers generalize to Im- ageNet? In International Conference on Machine Learning, pages 5389–5400.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Do ImageNet classifiers generalize to Im- ageNet? In International Conference on Machine Learning, pages 5389–5400

Reference 36

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Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Consistency-guided Prompt Learning for Vision-Language Models

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

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

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This paper cites Dropout: a simple way to prevent neural networks from overfitting.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Dropout: a simple way to prevent neural networks from overfitting

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This paper cites Multimodal few-shot learning with frozen language models.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Multimodal few-shot learning with frozen language models

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This paper cites Calculation of the Wasserstein distance be- tween probability distributions on the line.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Calculation of the Wasserstein distance be- tween probability distributions on the line

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This paper cites Learning robust global representations by penalizing local predictive power.Advances in Neural Information Pro- cessing Systems, 32, 2019.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Learning robust global representations by penalizing local predictive power.Advances in Neural Information Pro- cessing Systems, 32, 2019

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This paper cites Feature- based style randomization for domain generalization.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Feature- based style randomization for domain generalization

Reference 43

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

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models SUN database: Large-scale scene recognition from abbey to zoo

Reference 44

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Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models MMA: Multi-modal adapter for vision-language models

Reference 45

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This paper cites FILIP: Fine-grained Interactive Language-Image Pre-Training.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models FILIP: Fine-grained Interactive Language-Image Pre-Training

Reference 46

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This paper cites Barlow Twins: Self-supervised learning via redundancy reduction.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Barlow Twins: Self-supervised learning via redundancy reduction

Reference 47

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

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Conditional prompt learning for vision-language mod- els

Reference 48

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

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Learning to prompt for vision-language models

Reference 49

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This paper cites Domain Generalization with MixStyle.

Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models Domain Generalization with MixStyle

Reference 50

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