Paper Citation Record · LEDGER

Generalizing vision-language models to novel domains: A comprehensive survey

As of 19 August 2026, this Paper Citation Record lists 100 of 297 outbound references and 4 inbound Pith citation observations for arXiv:2506.18504.

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

pith.paper-citation-record.v1
2506.18504 v2

Coverage vector

measured 100 of 297 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:20:46.460566Z

measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:43:52.041500Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T03:39:30.504452Z

Reference resolution

100 of 297 outbound references displayed

verified exact3
verified fuzzy0
unresolved97
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d349e17f-f0d9-4bb0-bc33-eb9c1bf5e579 · outbound

This paper cites Imagenet classi- fication with deep convolutional neural networks,.

Generalizing vision-language models to novel domains: A comprehensive survey Imagenet classi- fication with deep convolutional neural networks,

Reference 1

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Observation d3b0ba3d-e8ab-4040-a287-fe8a03f0855f · outbound

This paper cites Deep residual learning for image recognition,.

Generalizing vision-language models to novel domains: A comprehensive survey Deep residual learning for image recognition,

Reference 2

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Observation ba61cf84-176d-4400-b4ab-8178fcdfea9f · outbound

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

Generalizing vision-language models to novel domains: A comprehensive survey An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 3

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Observation f12dea1e-5f6e-41a1-8860-1031f617dbcc · outbound

This paper cites A comprehensive survey on transfer learning,.

Generalizing vision-language models to novel domains: A comprehensive survey A comprehensive survey on transfer learning,

Reference 4

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Observation 3876ab1a-2bd5-4771-b2bb-d36fcf9cced0 · outbound

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

Generalizing vision-language models to novel domains: A comprehensive survey Imagenet: A large-scale hierarchical image database,

Reference 5

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This paper cites Object detection in 20 years: A survey,.

Generalizing vision-language models to novel domains: A comprehensive survey Object detection in 20 years: A survey,

Reference 6

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Observation aedf8c83-349a-4996-b862-63a63a9d321d · outbound

This paper cites Attention is all you need,.

Generalizing vision-language models to novel domains: A comprehensive survey Attention is all you need,

Reference 7

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Observation dbcdc464-19e9-4d8a-9b0b-c18d010853ac · outbound

This paper cites Bert: Pre- training of deep bidirectional transformers for language under- standing,.

Generalizing vision-language models to novel domains: A comprehensive survey Bert: Pre- training of deep bidirectional transformers for language under- standing,

Reference 8

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This paper cites Language models are unsupervised multitask learners,.

Generalizing vision-language models to novel domains: A comprehensive survey Language models are unsupervised multitask learners,

Reference 9

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This paper cites Language models are few-shot learners,.

Generalizing vision-language models to novel domains: A comprehensive survey Language models are few-shot learners,

Reference 10

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

Generalizing vision-language models to novel domains: A comprehensive survey Learning transferable visual models from natural language supervision,

Reference 11

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Observation d597bd5f-77a3-4426-bd1d-deedc5fdbfab · outbound

This paper cites Regionclip: Region-based language- image pretraining,.

Generalizing vision-language models to novel domains: A comprehensive survey Regionclip: Region-based language- image pretraining,

Reference 12

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Observation a9a26ee2-9374-491a-a429-00b17ef22230 · outbound

This paper cites Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning,.

Generalizing vision-language models to novel domains: A comprehensive survey Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning,

Reference 13

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Observation 8020ac47-9234-4198-bd06-f90e5ccff2f5 · outbound

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

Generalizing vision-language models to novel domains: A comprehensive survey Scaling up visual and vision- language representation learning with noisy text supervision,

Reference 14

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This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

Generalizing vision-language models to novel domains: A comprehensive survey CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 15

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This paper cites Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation,.

Generalizing vision-language models to novel domains: A comprehensive survey Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation,

Reference 16

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Observation 3d2264ba-a644-4b04-be28-b12959a837c8 · outbound

This paper cites InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning.

Generalizing vision-language models to novel domains: A comprehensive survey InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

Reference 17

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Observation 437b529e-cbe6-4528-94fa-b0499530abcb · outbound

This paper cites Visual instruction tuning,.

Generalizing vision-language models to novel domains: A comprehensive survey Visual instruction tuning,

Reference 18

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Observation f585e1b3-eaed-43b8-bdac-242b4234c635 · outbound

This paper cites PaLI: A Jointly-Scaled Multilingual Language-Image Model.

Generalizing vision-language models to novel domains: A comprehensive survey PaLI: A Jointly-Scaled Multilingual Language-Image Model

Reference 19

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Observation 0a7f94c8-6ee1-423e-ba2f-ce2f7422eb18 · outbound

This paper cites PaLI-X: On Scaling up a Multilingual Vision and Language Model.

Generalizing vision-language models to novel domains: A comprehensive survey PaLI-X: On Scaling up a Multilingual Vision and Language Model

Reference 20

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Observation 9646b2fc-6620-41fc-94dd-1f971f9595ba · outbound

This paper cites Multilingual diversity improves vision-language representations,.

Generalizing vision-language models to novel domains: A comprehensive survey Multilingual diversity improves vision-language representations,

Reference 21

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This paper cites Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.

Generalizing vision-language models to novel domains: A comprehensive survey Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

Reference 25

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This paper cites Clip-adapter: Better vision-language models with feature adapters,.

Generalizing vision-language models to novel domains: A comprehensive survey Clip-adapter: Better vision-language models with feature adapters,

Reference 26

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Observation ac9fdebd-6e1b-430a-912d-b6a22e662725 · outbound

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

Generalizing vision-language models to novel domains: A comprehensive survey Tip-adapter: Training-free adaption of clip for few-shot classification,

Reference 27

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Observation cc1deee2-edaa-44f6-abee-aa17980f440b · outbound

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

Generalizing vision-language models to novel domains: A comprehensive survey The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 28

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Generalizing vision-language models to novel domains: A comprehensive survey Parameter- efficient transfer learning for nlp,

Reference 29

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This paper cites Knowledge distillation: A survey,.

Generalizing vision-language models to novel domains: A comprehensive survey Knowledge distillation: A survey,

Reference 30

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Generalizing vision-language models to novel domains: A comprehensive survey Learning to prompt for vision-language models,

Reference 31

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This paper cites Conditional prompt learning for vision-language models,.

Generalizing vision-language models to novel domains: A comprehensive survey Conditional prompt learning for vision-language models,

Reference 32

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This paper cites Distilling large vision-language model with out-of-distribution generalizability,.

Generalizing vision-language models to novel domains: A comprehensive survey Distilling large vision-language model with out-of-distribution generalizability,

Reference 33

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Generalizing vision-language models to novel domains: A comprehensive survey Domain adaptation via prompt learning,

Reference 34

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Generalizing vision-language models to novel domains: A comprehensive survey Split to merge: Unifying separated modalities for unsupervised domain adaptation,

Reference 35

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This paper cites Domain-agnostic mutual prompting for unsupervised domain adaptation,.

Generalizing vision-language models to novel domains: A comprehensive survey Domain-agnostic mutual prompting for unsupervised domain adaptation,

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This paper cites Soft prompt generation for domain generalization,.

Generalizing vision-language models to novel domains: A comprehensive survey Soft prompt generation for domain generalization,

Reference 37

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This paper cites Leverag- ing vision-language models for improving domain generalization in image classification,.

Generalizing vision-language models to novel domains: A comprehensive survey Leverag- ing vision-language models for improving domain generalization in image classification,

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This paper cites Practicaldg: Perturbation distillation on vision-language models for hybrid domain generalization,.

Generalizing vision-language models to novel domains: A comprehensive survey Practicaldg: Perturbation distillation on vision-language models for hybrid domain generalization,

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Generalizing vision-language models to novel domains: A comprehensive survey Test-time prompt tuning for zero-shot generalization in vision-language models,

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Generalizing vision-language models to novel domains: A comprehensive survey Efficient test-time adaptation of vision-language models,

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Generalizing vision-language models to novel domains: A comprehensive survey Diverse data augmentation with diffusions for effective test-time prompt tun- ing,

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This paper cites Vision-language models for vision tasks: A survey,.

Generalizing vision-language models to novel domains: A comprehensive survey Vision-language models for vision tasks: A survey,

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Generalizing vision-language models to novel domains: A comprehensive survey A Survey of Vision-Language Pre-Trained Models

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This paper cites Ex- ploring the frontier of vision-language models: A survey of current methodologies and future directions,.

Generalizing vision-language models to novel domains: A comprehensive survey Ex- ploring the frontier of vision-language models: A survey of current methodologies and future directions,

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Generalizing vision-language models to novel domains: A comprehensive survey Domain generalization by mutual-information regularization with pre-trained models,

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Generalizing vision-language models to novel domains: A comprehensive survey Clipood: Generalizing clip to out-of-distributions,

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Generalizing vision-language models to novel domains: A comprehensive survey Towards calibrated robust fine-tuning of vision-language models,

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

Generalizing vision-language models to novel domains: A comprehensive survey Dual memory networks: A versatile adaptation approach for vision- language models,

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Generalizing vision-language models to novel domains: A comprehensive survey A survey of transfer learning,

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Generalizing vision-language models to novel domains: A comprehensive survey A survey on deep transfer learning,

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Generalizing vision-language models to novel domains: A comprehensive survey DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

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Generalizing vision-language models to novel domains: A comprehensive survey Qwen2 Technical Report

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Generalizing vision-language models to novel domains: A comprehensive survey Introducing qwen-7b: Open foundation and human- aligned models (of the state-of-the-arts),

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Generalizing vision-language models to novel domains: A comprehensive survey GPT-4 Technical Report

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Generalizing vision-language models to novel domains: A comprehensive survey DeepSeek-VL: Towards Real-World Vision-Language Understanding

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Generalizing vision-language models to novel domains: A comprehensive survey Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

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Generalizing vision-language models to novel domains: A comprehensive survey MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

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Generalizing vision-language models to novel domains: A comprehensive survey Contrastive learning of medical visual representations from paired images and text,

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Generalizing vision-language models to novel domains: A comprehensive survey Transfer learning,

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Generalizing vision-language models to novel domains: A comprehensive survey Conditional adversarial domain adaptation,

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Generalizing vision-language models to novel domains: A comprehensive survey Transfer independently together: A generalized framework for domain adaptation,

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This paper cites Maximum density divergence for domain adaptation,.

Generalizing vision-language models to novel domains: A comprehensive survey Maximum density divergence for domain adaptation,

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Generalizing vision-language models to novel domains: A comprehensive survey Divergence- agnostic unsupervised domain adaptation by adversarial at- tacks,

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Observation b4f3661c-3d7a-4f3b-9a49-456804a11052 · outbound

This paper cites Deep transfer metric learning,.

Generalizing vision-language models to novel domains: A comprehensive survey Deep transfer metric learning,

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This paper cites Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation,.

Generalizing vision-language models to novel domains: A comprehensive survey Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation,

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This paper cites Unsupervised domain adaptation by backpropagation,.

Generalizing vision-language models to novel domains: A comprehensive survey Unsupervised domain adaptation by backpropagation,

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This paper cites Domain generalization: A survey,.

Generalizing vision-language models to novel domains: A comprehensive survey Domain generalization: A survey,

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This paper cites Learning transferrable and interpretable representations for domain generalization,.

Generalizing vision-language models to novel domains: A comprehensive survey Learning transferrable and interpretable representations for domain generalization,

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This paper cites Domain gener- alization via invariant feature representation,.

Generalizing vision-language models to novel domains: A comprehensive survey Domain gener- alization via invariant feature representation,

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This paper cites Energy-based domain generalization for face anti-spoofing,.

Generalizing vision-language models to novel domains: A comprehensive survey Energy-based domain generalization for face anti-spoofing,

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This paper cites Deep domain generalization via conditional invariant adversar- ial networks,.

Generalizing vision-language models to novel domains: A comprehensive survey Deep domain generalization via conditional invariant adversar- ial networks,

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This paper cites Domain general- ization via entropy regularization,.

Generalizing vision-language models to novel domains: A comprehensive survey Domain general- ization via entropy regularization,

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This paper cites Learning to gen- eralize: Meta-learning for domain generalization,.

Generalizing vision-language models to novel domains: A comprehensive survey Learning to gen- eralize: Meta-learning for domain generalization,

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This paper cites Metareg: Towards domain generalization using meta-regularization,.

Generalizing vision-language models to novel domains: A comprehensive survey Metareg: Towards domain generalization using meta-regularization,

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This paper cites A comprehensive survey on test- time adaptation under distribution shifts,.

Generalizing vision-language models to novel domains: A comprehensive survey A comprehensive survey on test- time adaptation under distribution shifts,

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This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

Generalizing vision-language models to novel domains: A comprehensive survey Tent: Fully Test-time Adaptation by Entropy Minimization

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This paper cites A comprehensive survey on source-free domain adaptation,.

Generalizing vision-language models to novel domains: A comprehensive survey A comprehensive survey on source-free domain adaptation,

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This paper cites Source-free active domain adaptation via energy-based locality preserving transfer,.

Generalizing vision-language models to novel domains: A comprehensive survey Source-free active domain adaptation via energy-based locality preserving transfer,

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This paper cites Generalizing from a few examples: A survey on few-shot learning,.

Generalizing vision-language models to novel domains: A comprehensive survey Generalizing from a few examples: A survey on few-shot learning,

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This paper cites Leveraging the invariant side of generative zero-shot learning,.

Generalizing vision-language models to novel domains: A comprehensive survey Leveraging the invariant side of generative zero-shot learning,

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This paper cites Visual prompt tuning,.

Generalizing vision-language models to novel domains: A comprehensive survey Visual prompt tuning,

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This paper cites Maple: Multi-modal prompt learning,.

Generalizing vision-language models to novel domains: A comprehensive survey Maple: Multi-modal prompt learning,

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This paper cites Unified Vision and Language Prompt Learning.

Generalizing vision-language models to novel domains: A comprehensive survey Unified Vision and Language Prompt Learning

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Generalizing vision-language models to novel domains: A comprehensive survey Dual modality prompt tuning for vision-language pre-trained model,

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This paper cites Distribution-aware prompt tun- ing for vision-language models,.

Generalizing vision-language models to novel domains: A comprehensive survey Distribution-aware prompt tun- ing for vision-language models,

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Generalizing vision-language models to novel domains: A comprehensive survey Exploring Visual Prompts for Adapting Large-Scale Models

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This paper cites Unadversarial examples: Designing objects for ro- bust vision,.

Generalizing vision-language models to novel domains: A comprehensive survey Unadversarial examples: Designing objects for ro- bust vision,

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This paper cites Dept: Decoupled prompt tuning,.

Generalizing vision-language models to novel domains: A comprehensive survey Dept: Decoupled prompt tuning,

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This paper cites DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models.

Generalizing vision-language models to novel domains: A comprehensive survey DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models

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Observation e900055a-1ca2-4d6f-b83a-2b74489640d6 · outbound

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Generalizing vision-language models to novel domains: A comprehensive survey DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection

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This paper cites Robust fine-tuning of zero-shot models,.

Generalizing vision-language models to novel domains: A comprehensive survey Robust fine-tuning of zero-shot models,

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This paper cites Visual-language prompt tuning with knowledge-guided context optimization,.

Generalizing vision-language models to novel domains: A comprehensive survey Visual-language prompt tuning with knowledge-guided context optimization,

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Generalizing vision-language models to novel domains: A comprehensive survey Prompt-aligned gradient for prompt tuning,

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Generalizing vision-language models to novel domains: A comprehensive survey Self-regulating prompts: Foundational model adaptation without forgetting,

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Generalizing vision-language models to novel domains: A comprehensive survey What does a platypus look like? generating customized prompts for zero-shot image classification,

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This paper cites Learning to prompt with text only supervision for vision-language models,.

Generalizing vision-language models to novel domains: A comprehensive survey Learning to prompt with text only supervision for vision-language models,

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Generalizing vision-language models to novel domains: A comprehensive survey Ad-clip: Adapting do- mains in prompt space using clip,

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Generalizing vision-language models to novel domains: A comprehensive survey Stylip: Multi-scale style-conditioned prompt learning for clip- based domain generalization,

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Generalizing vision-language models to novel domains: A comprehensive survey Enhancing domain adaptation through prompt gradient alignment,

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Generalizing vision-language models to novel domains: A comprehensive survey Enhancing Vision-Language Models Generalization via Diversity-Driven Novel Feature Synthesis

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Observation 78be2ee6-8305-4a8a-bba6-ceb9c187d523 · outbound

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Generalizing vision-language models to novel domains: A comprehensive survey Dis- entangled prompt representation for domain generalization,

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Generalizing vision-language models to novel domains: A comprehensive survey Unknown prompt the only lacuna: Unveiling clip’s potential for open domain generalization,

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