Paper Citation Record · LEDGER

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling

As of 20 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2412.07077.

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pith.paper-citation-record.v1
2412.07077 v1

Coverage vector

measured 45 of 45 reference resolution

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measured 45 of 45 standing notices

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

45 of 45 outbound references displayed

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External citation measurements

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Outbound references

Observation 863d29a8-d7a7-441d-bfaa-56058254d596 · outbound

This paper cites A simple zero-shot prompt weight- ing technique to improve prompt ensembling in text-image models.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling A simple zero-shot prompt weight- ing technique to improve prompt ensembling in text-image models

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This paper cites VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

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

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Food-101–mining discriminative components with random forests

Reference 3

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This paper cites Lan- guage models are few-shot learners.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Lan- guage models are few-shot learners

Reference 4

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Observation 9da2735c-f6f8-429e-849f-2e8a98ca542b · outbound

This paper cites Apollo: Unified adapter and prompt learning for vision lan- guage models.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Apollo: Unified adapter and prompt learning for vision lan- guage models

Reference 5

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Observation e761425b-e13a-4737-be5d-3224d063b9e4 · outbound

This paper cites Describing textures in the wild.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Describing textures in the wild

Reference 6

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Observation ff319a7b-b599-4f03-95ce-c17e6a1e731a · outbound

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

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Imagenet: A large-scale hierarchical image database

Reference 7

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

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This paper cites Ensemble methods in machine learn- ing.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Ensemble methods in machine learn- ing

Reference 9

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Observation 60f3cfde-f374-45b0-bedb-594b853c2b61 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling An image is worth 16x16 words: Transformers for image recognition at scale, 2021

Reference 10

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Observation f020de12-0265-4aa2-a42a-1014b57615f2 · outbound

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

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling 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 8217c723-6701-4d76-984a-28957de6a37e · outbound

This paper cites Deep Ensembles: A Loss Landscape Perspective.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Deep Ensembles: A Loss Landscape Perspective

Reference 12

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Observation 95225b2a-49f1-4517-a5fd-d7f7fac22ff4 · outbound

This paper cites The vendi score: A diversity evaluation metric for machine learning, 2023.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling The vendi score: A diversity evaluation metric for machine learning, 2023

Reference 13

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

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 14

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Observation 62cb5589-cc16-4651-8208-925d80d296d8 · outbound

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

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling The many faces of robust- ness: A critical analysis of out-of-distribution generalization

Reference 15

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Observation 60e9411b-e350-4b4b-9af1-9a0b1dd8cbb0 · outbound

This paper cites Natural adversarial examples.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Natural adversarial examples

Reference 16

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Observation 38d5ba68-f305-458c-9ac6-847cc7fc5483 · outbound

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

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 17

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This paper cites Maple: Multi-modal prompt learning.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Maple: Multi-modal prompt learning

Reference 18

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

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Self-regulating prompts: Foundational model adaptation without forgetting

Reference 19

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

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling 3d object representations for fine-grained categorization

Reference 20

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Read-only prompt optimization for vision-language few-shot learning

Reference 21

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Observation 23ac0879-9ab0-4aff-a32c-fbe5ea347c54 · outbound

This paper cites Prompt distribution learning.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Prompt distribution learning

Reference 22

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Observation 8ca297be-0d81-4b6c-b2c8-c035dc486d80 · outbound

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Fine-Grained Visual Classification of Aircraft

Reference 23

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Automated flower classification over a large number of classes

Reference 24

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Cats and dogs

Reference 25

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This paper cites Learning transferable visual models from natural language supervi- sion.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Learning transferable visual models from natural language supervi- sion

Reference 26

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Language models are unsu- pervised multitask learners

Reference 27

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This paper cites Do imagenet classifiers generalize to im- agenet? In International conference on machine learning , pages 5389–5400.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Do imagenet classifiers generalize to im- agenet? In International conference on machine learning , pages 5389–5400

Reference 28

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

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Test- time prompt tuning for zero-shot generalization in vision- language models

Reference 29

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

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

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This paper cites Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models

Reference 31

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Attention is all you need

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This paper cites Learning robust global representations by penalizing local predictive power.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Learning robust global representations by penalizing local predictive power

Reference 33

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Robust fine-tuning of zero-shot models

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

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Sun database: Large-scale scene recognition from abbey to zoo

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Visual- language prompt tuning with knowledge-guided context op- timization

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

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling FILIP: Fine-grained Interactive Language-Image Pre-Training

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This paper cites Boosting continual learning of vision-language models via mixture-of-experts adapters.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Boosting continual learning of vision-language models via mixture-of-experts adapters

Reference 38

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This paper cites Florence: A New Foundation Model for Computer Vision.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Florence: A New Foundation Model for Computer Vision

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Barlow twins: Self-supervised learning via redundancy reduction

Reference 40

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This paper cites Lit: Zero-shot transfer with locked-image text tuning.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Lit: Zero-shot transfer with locked-image text tuning

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Preventing zero-shot transfer degradation in continual learning of vision-language models

Reference 42

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Conditional prompt learning for vision-language mod- els

Reference 43

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Learning to prompt for vision-language models

Reference 44

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Prompt-aligned gradient for prompt tuning

Reference 45

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