Paper Citation Record · LEDGER

Adapting Vision-Language Models Without Labels: A Comprehensive Survey

As of 18 August 2026, this Paper Citation Record lists 100 of 299 outbound references and 4 inbound Pith citation observations for arXiv:2508.05547.

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

pith.paper-citation-record.v1
2508.05547 v1

Coverage vector

measured 100 of 299 reference resolution

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measured 104 of 104 standing notices

One-hop event checks from named stored sources.

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measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T12:54:40.377437Z

Reference resolution

100 of 299 outbound references displayed

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External citation measurements

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Outbound references

Observation c7b6bc77-500a-472d-865f-2f872bda4b73 · outbound

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

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Learning transferable visual models from natural language supervision,

Reference 1

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Observation b31b2cf6-d9fc-4828-b62b-048077f931a8 · outbound

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

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 2

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Observation c25732c9-0c30-4640-bae1-4ef40b5f49f0 · outbound

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

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Flamingo: a visual language model for few-shot learning,

Reference 3

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Observation a6d4c706-f83b-4876-9841-aa92642aeb6c · outbound

This paper cites Visual instruction tuning,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Visual instruction tuning,

Reference 4

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Observation 871f2069-6a30-41ea-8e04-b01f027af56a · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Laion-5b: An open large-scale dataset for training next generation image-text models,

Reference 5

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Observation f656fd03-df30-4533-8938-90be9d1ffa37 · outbound

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

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Clip2scene: Towards label-efficient 3d scene understanding by clip,

Reference 6

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Observation 3beeec1f-bdac-4ce2-877c-6bee9c981e19 · outbound

This paper cites CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory

Reference 7

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Observation a2af32f6-8260-4f30-8822-38d51e270b87 · outbound

This paper cites Unseen visual anomaly generation,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Unseen visual anomaly generation,

Reference 8

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Observation fd09b1c1-6566-4185-b79b-c82440621154 · outbound

This paper cites Probabilistic embeddings for cross-modal retrieval,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Probabilistic embeddings for cross-modal retrieval,

Reference 9

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Observation ba6eecd3-805d-46a9-87f9-170a1bbb4d0b · outbound

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

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Learning to prompt for vision-language models,

Reference 10

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Observation a204632c-839e-4b13-b0f0-d6ad72687abb · outbound

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

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Conditional prompt learning for vision-language models,

Reference 11

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Observation dc17dc4d-4f79-4b3b-be30-068c5e9da5a6 · outbound

This paper cites MaPLe: Multi-modal Prompt Learning.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey MaPLe: Multi-modal Prompt Learning

Reference 12

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Observation 37b296ab-d061-46b0-af81-05101b73d4aa · outbound

This paper cites A hard-to- beat baseline for training-free clip-based adaptation,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey A hard-to- beat baseline for training-free clip-based adaptation,

Reference 13

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Observation c7d5e9f4-b8b5-48a1-b6f1-03b734620888 · outbound

This paper cites Adapting visual category models to new domains,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Adapting visual category models to new domains,

Reference 14

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Observation ae56fbcf-d5fb-4397-b0b8-c5b88ba57c63 · outbound

This paper cites Visual classification via description from large language models,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Visual classification via description from large language models,

Reference 15

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This paper cites Extract free dense labels from clip,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Extract free dense labels from clip,

Reference 16

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Observation c3942aff-a4f6-4c99-945b-09a255845dd3 · outbound

This paper cites Unsupervised Prompt Learning for Vision-Language Models.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Unsupervised Prompt Learning for Vision-Language Models

Reference 17

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Observation 68b5ba6b-c2c9-496f-b81f-0d203c35a7ca · outbound

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

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Test-time prompt tuning for zero-shot generalization in vision-language models,

Reference 18

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Observation 788dd14e-fd13-4fbc-ad9d-ba36c89c061c · outbound

This paper cites Swapprompt: Test-time prompt adaptation for vision-language models,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Swapprompt: Test-time prompt adaptation for vision-language models,

Reference 19

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Observation c3c3c35f-78cd-46df-b823-32edca0210c0 · outbound

This paper cites The illusion of progress? a critical look at test-time adaptation for vision-language models,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey The illusion of progress? a critical look at test-time adaptation for vision-language models,

Reference 20

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Observation e124257a-b3a7-41f6-8f27-cb4a0b3476b3 · outbound

This paper cites What does a platypus look like? generating customized prompts for zero-shot image classi- fication,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey What does a platypus look like? generating customized prompts for zero-shot image classi- fication,

Reference 21

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Observation 7fca3fa5-e0cd-4f0b-a89b-0fdc766993b2 · outbound

This paper cites Improving Zero-Shot Models with Label Distribution Priors.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Improving Zero-Shot Models with Label Distribution Priors

Reference 22

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Observation 447f5e3e-7f2c-4d92-aa47-da94c1dec0c3 · outbound

This paper cites Online zero-shot classification with clip,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Online zero-shot classification with clip,

Reference 23

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Observation 0d32d8bf-997f-4b63-b1ed-f92733283f9a · outbound

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

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Align your prompts: Test- time prompting with distribution alignment for zero-shot generaliza- tion,

Reference 24

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Observation 40ceada1-b4e4-47a8-9dcc-76ad2c947cd3 · outbound

This paper cites Efficient test-time adaptation of vision-language models,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Efficient test-time adaptation of vision-language models,

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This paper cites Masked unsupervised self-training for label-free image classification,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Masked unsupervised self-training for label-free image classification,

Reference 26

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Realistic unsupervised clip fine-tuning with universal entropy optimization,

Reference 27

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Reco: Retrieve and co-segment for zero-shot transfer,

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This paper cites Pay attention to your neighbours: Training-free open-vocabulary semantic segmentation,.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Pay attention to your neighbours: Training-free open-vocabulary semantic segmentation,

Reference 29

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This paper cites Can language-guided unsupervised adaptation improve medical image classification using unpaired images and texts?.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Can language-guided unsupervised adaptation improve medical image classification using unpaired images and texts?

Reference 30

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey A chatgpt aided explainable framework for zero-shot medical image diagnosis,

Reference 31

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Text-enhanced zero-shot action recognition: A training-free approach,

Reference 32

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Dts-tpt: dual temporal-sync test-time prompt tuning for zero-shot activity recogni- tion,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Pouf: Prompt- oriented unsupervised fine-tuning for large pre-trained models,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Label propagation for zero-shot classification with vision-language models,

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This paper cites On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?

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

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Vision-language models for vision tasks: A survey,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Advances in multimodal adaptation and generalization: From traditional approaches to foundation models,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Generalizing vision-language models to novel domains: A comprehensive survey

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey A comprehensive survey on test-time adaptation under distribution shifts,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey In search of lost online test-time adaptation: A survey,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Beyond Model Adaptation at Test Time: A Survey

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey FILIP: Fine-grained Interactive Language-Image Pre-Training

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Attention is all you need,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey An image is worth 16x16 words: Transformers for image recognition at scale,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Microsoft COCO Captions: Data Collection and Evaluation Server

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Image captioning with semantic attention,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Visual question answering: A survey of methods and datasets,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey High- resolution image synthesis with latent diffusion models,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Deep supervised cross-modal retrieval,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey A comprehensive survey on pretrained foundation models: A history from bert to chatgpt,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Learning to detect unseen object classes by between-class attribute transfer,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Describing objects by their attributes,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Devise: A deep visual-semantic embedding model,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey An embarrassingly simple approach to zero-shot learning,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Generalized zero-shot learning via synthesized examples,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Multi-modal cycle-consistent generalized zero-shot learning,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey f-vaegan-d2: A feature generating framework for any-shot learning,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey An empirical study and analysis of generalized zero-shot learning for object recognition in the wild,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Zero-shot learning-the good, the bad and the ugly,

Reference 60

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey A review of generalized zero-shot learning meth- ods,

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey A survey of zero-shot learning: Settings, methods, and applications,

Reference 62

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models

Reference 63

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Clip-adapter: Better vision-language models with feature adapters,

Reference 64

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Reference 65

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Low-rank few-shot adaptation of vision- language models,

Reference 66

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Visual Classification via Description from Large Language Models

Reference 67

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Denseclip: Language-guided dense prediction with context- aware prompting,

Reference 68

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models

Reference 69

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Recall and Refine: A Simple but Effective Source-free Open-set Domain Adaptation Framework

Reference 70

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,

Reference 71

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Exploiting local feature patterns for unsupervised domain adaptation,

Reference 72

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Contrastive test-time adaptation,

Reference 73

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Universal source-free domain adaptation,

Reference 74

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data,

Reference 75

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Domain adaptation without source data,

Reference 76

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey A comprehensive survey on source-free domain adaptation,

Reference 77

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Source-free unsu- pervised domain adaptation: A survey,

Reference 78

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Tent: Fully test-time adaptation by entropy minimization,

Reference 79

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Towards robust multimodal open-set test-time adaptation via adaptive entropy-aware optimization,

Reference 80

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Efficient test-time model adaptation without forgetting,

Reference 81

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Sotta: Robust test-time adaptation on noisy data streams,

Reference 82

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Continual test-time domain adaptation,

Reference 83

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Ecotta: Memory- efficient continual test-time adaptation via self-distilled regularization,

Reference 84

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Diverse data augmentation with diffusions for effective test-time prompt tuning,

Reference 85

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 86

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Sigmoid loss for language image pre-training,

Reference 87

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Chils: Zero-shot image classification with hierarchical label sets,

Reference 88

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Sus-x: Training-free name- only transfer of vision-language models,

Reference 90

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Neural priming for sample- efficient adaptation,

Reference 91

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Just say the name: Online continual learning with category names only via data generation,

Reference 92

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Calip: Zero-shot enhancement of clip with parameter-free attention,

Reference 93

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Sclip: Rethinking self-attention for dense vision-language inference,

Reference 94

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Proxyclip: Proxy attention improves clip for open-vocabulary segmentation,

Reference 95

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Language models are few-shot learners,

Reference 96

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Meta-prompting for automating zero- shot visual recognition with llms,

Reference 97

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey The neglected tails in vision-language models,

Reference 98

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Introducing chatgpt,

Reference 99

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Prompting scientific names for zero-shot species recognition,

Reference 100

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Adapting Vision-Language Models Without Labels: A Comprehensive Survey Waffling around for performance: Visual classification with random words and broad concepts,

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Pith citing papers

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VIP: Visual-guided Prompt Evolution for Efficient Dense Vision-Language Inference cites this paper.

VIP: Visual-guided Prompt Evolution for Efficient Dense Vision-Language Inference Adapting Vision-Language Models Without Labels: A Comprehensive Survey

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VIP: Visual-guided Prompt Evolution for Efficient Dense Vision-Language Inference cites this paper.

VIP: Visual-guided Prompt Evolution for Efficient Dense Vision-Language Inference Adapting Vision-Language Models Without Labels: A Comprehensive Survey

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ComMem: Complementary Memory Systems for Test-Time Adaptation of Vision-Language Models cites this paper.

ComMem: Complementary Memory Systems for Test-Time Adaptation of Vision-Language Models Adapting Vision-Language Models Without Labels: A Comprehensive Survey

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USE: A Unified Self-Ensembling Framework for Test-Time Prompt Tuning cites this paper.

USE: A Unified Self-Ensembling Framework for Test-Time Prompt Tuning Adapting Vision-Language Models Without Labels: A Comprehensive Survey

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