Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models | Paper Citation Record

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This paper cites Pattern recognition letters, 31(14): 2214–2224, 2010.

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Pattern recognition letters, 31(14): 2214–2224, 2010

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

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Align your prompts: Test-time prompting with distribution align- ment for zero-shot generalization

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

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Flamingo: a visual language model for few-shot learning

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Focused anchors loss: Cost- sensitive learning of discriminative features for imbalanced classification

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Food-101–mining discriminative components with random forests

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Describing textures in the wild

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Domain adaptation and sample bias correction theory and algorithm for regression

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This paper cites Monotonicity of Entropy and Fisher Information: A Quick Proof via Maximal Correlation.

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Monotonicity of Entropy and Fisher Information: A Quick Proof via Maximal Correlation

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Imagenet: A large-scale hierarchical image database

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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This paper cites A video saliency detection model in com- pressed domain.

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models A video saliency detection model in com- pressed domain

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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.

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Dropout as a bayesian approximation: Representing model uncertainty in deep learning

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Clip-adapter: Better vision-language models with feature adapters

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models On calibration of modern neural networks

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Model selection and the princi- ple of minimum description length

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This paper cites Deep residual learning for image recognition.

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Deep residual learning for image recognition

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Generalization bounds: Perspec- tives from information theory and pac-bayes

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This paper cites Benchmarking neu- ral network robustness to common corruptions and perturba- tions.

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Benchmarking neu- ral network robustness to common corruptions and perturba- tions

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

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models The many faces of robust- ness: A critical analysis of out-of-distribution generalization

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Natural adversarial examples

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Scaling up visual and vision-language representa- tion learning with noisy text supervision

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Calibrated lan- guage models must hallucinate

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This paper cites Technical report on label- informed logit redistribution for better domain generaliza- tion in low-shot classification with foundation models.arXiv preprint arXiv:2501.17595, 2025.

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Technical report on label- informed logit redistribution for better domain generaliza- tion in low-shot classification with foundation models.arXiv preprint arXiv:2501.17595, 2025

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Causal covari- ate shift correction using fisher information penalty

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Maple: Multi-modal prompt learning

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models 3d object representations for fine-grained categorization

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Verified un- certainty calibration

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Theory of point esti- mation

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Deeper, broader and artier domain generaliza- tion

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Fine-Grained Visual Classification of Aircraft

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Lecture notes on advanced statistical theory

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Study on the impact of partition-induced dataset shift on k-fold cross-validation

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models When does label smoothing help? Advances in neural in- formation processing systems, 32, 2019

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Robust calibration of large vision- language adapters

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Automated flower classification over a large number of classes

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Blackvip: Black-box visual prompting for robust transfer learning

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Be confident in what you know: Bayesian parameter efficient fine-tuning of vision foundation models

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Prevalence of neural collapse during the terminal phase of deep learning training

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Cats and dogs

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Regularizing Neural Networks by Penalizing Confident Output Distributions

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Learning transferable visual models from natural language supervi- sion

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Do imagenet classifiers generalize to im- agenet? In International conference on machine learning , pages 5389–5400

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Focal loss for dense ob- ject detection

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Improving predictive inference un- der covariate shift by weighting the log-likelihood function

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Input- dependent estimation of generalization error under covariate shift

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Covariate shift adaptation by importance weighted cross validation

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models On mixup train- ing: Improved calibration and predictive uncertainty for deep neural networks

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Toward a holis- tic evaluation of robustness in clip models

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Sparkr: Scal- ing r programs with spark

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This paper cites Calibration in Deep Learning: A Survey of the State-of-the-Art.

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Calibration in Deep Learning: A Survey of the State-of-the-Art

Reference 55

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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.

Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Learning robust global representations by penalizing local predictive power.Advances in Neural Information Pro- cessing Systems, 32, 2019

Reference 56

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Open-vocabulary calibration for fine-tuned clip

Reference 58

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models PAC-Bayes Information Bottleneck

Reference 59

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Mitigating overconfidence in large language models: A behavioral lens on confidence estimation and calibration

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Robust fine-tuning of zero-shot models

Reference 61

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Sun database: Large-scale scene recognition from abbey to zoo

Reference 62

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Any-shift prompt- ing for generalization over distributions

Reference 63

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models When and how mixup improves calibration

Reference 64

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Reference 65

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Large lan- guage models as commonsense knowledge for large-scale task planning

Reference 66

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Learning to prompt for vision-language models

Reference 67

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Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models Unresolved cited work

Reference 2022

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