Paper Citation Record · LEDGER

A Closer Look at Conditional Prompt Tuning for Vision-Language Models

As of 14 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 1 inbound Pith citation observation for arXiv:2506.23856.

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

pith.paper-citation-record.v1
2506.23856 v1

Coverage vector

measured 67 of 67 reference resolution

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measured 68 of 68 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T00:32:35.119143Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:29:57.636038Z

Reference resolution

67 of 67 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 42a143cf-56cf-44eb-9e66-373d11fc3027 · outbound

This paper cites Dept: Decoupled prompt tuning.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Dept: Decoupled prompt tuning

Reference 1

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Observation 10c8f469-62c6-48aa-b788-e2d006debfc9 · outbound

This paper cites Learning trans- ferable visual models from natural language supervision.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Learning trans- ferable visual models from natural language supervision

Reference 2

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Observation e88ac3bc-595d-44ca-a87d-cb3cfa98fab0 · outbound

This paper cites A closer look at few-shot classification again.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models A closer look at few-shot classification again

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:31.336067Z

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Observation ee9246f5-154e-415d-bccf-3430d116f005 · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Bayesian prompt learning for image- language model generalization

Reference 4

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Observation 1bf7febd-f5f0-42b3-86db-42c1ae529c13 · outbound

This paper cites Maple: Multi-modal prompt learning.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Maple: Multi-modal prompt learning

Reference 5

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Observation 36ef0ed2-b19e-4c7b-89fb-e0c761633f5a · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Conditional prompt learning for vision-language models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:31.289366Z

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Observation 51dcf074-8db9-47ef-baee-2a403d94a32a · outbound

This paper cites Visual-language prompt tuning with knowledge-guided con- text optimization.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Visual-language prompt tuning with knowledge-guided con- text optimization

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:31.258623Z

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Observation 078bdf5b-3200-48c4-af67-2de18b03526b · outbound

This paper cites Prompt-aligned gradient for prompt tuning.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Prompt-aligned gradient for prompt tuning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:31.243884Z

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Observation d558fa0b-5c62-48de-8d20-a3bf83cfcfde · outbound

This paper cites Distribution-aware prompt tuning for vision-language models.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Distribution-aware prompt tuning for vision-language models

Reference 10

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Observation bc2cdb00-bc92-494b-98bf-7e3c56bc0edc · outbound

This paper cites Context-aware Alignment and Mutual Mask- ing for 3D-Language Pre-training.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Context-aware Alignment and Mutual Mask- ing for 3D-Language Pre-training

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:31.211644Z

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Observation 02749759-2fa5-4cd4-af91-d8f9b3fc15ba · outbound

This paper cites Recent advances in natural language processing via large pre- trained language models: A survey.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Recent advances in natural language processing via large pre- trained language models: A survey

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:31.194747Z

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Observation 331cfc70-9bf6-4f40-b869-43c69c894a17 · outbound

This paper cites Nat- ural language processing: State of the art, current trends and challenges.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Nat- ural language processing: State of the art, current trends and challenges

Reference 13

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Observation db735bfb-72d4-4aba-9778-9b0b3d7c90b9 · outbound

This paper cites Vilt: Vision-and- language transformer without convolution or region supervision.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Vilt: Vision-and- language transformer without convolution or region supervision

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:31.161326Z

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Observation a8938f1c-a23c-4734-b879-22de8c45f1a0 · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Scaling up visual and vision- language representation learning with noisy text supervision

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:31.147022Z

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Observation b5d0aceb-a5b8-4986-9948-5b846aa4f352 · outbound

This paper cites WenLan: Bridging vision and language by large-scale multi-modal pre- training.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models WenLan: Bridging vision and language by large-scale multi-modal pre- training

Reference 16

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Observation 93208d3e-066f-41ca-bb24-9498c922d608 · outbound

This paper cites Align before fuse: Vision and language representation learn- ing with momentum distillation.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Align before fuse: Vision and language representation learn- ing with momentum distillation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:31.118090Z

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Observation e669899b-a506-4ba9-b199-cada8ba773c7 · outbound

This paper cites Disentangled Multiplex Graph Represen- tation Learning.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Disentangled Multiplex Graph Represen- tation Learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:31.102803Z

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Observation 443e8130-b339-4d0e-a49b-96c56f8ffb51 · outbound

This paper cites Vilbert: Pretraining task-agnostic visiolinguistic rep- resentations for vision-and-language tasks.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Vilbert: Pretraining task-agnostic visiolinguistic rep- resentations for vision-and-language tasks

Reference 19

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Observation cb02161e-0026-44d3-953b-af180183361d · outbound

This paper cites X-clip: End-to-end multi-grained contrastive learning for video-text retrieval.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models X-clip: End-to-end multi-grained contrastive learning for video-text retrieval

Reference 20

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Observation 8f69235b-9b31-48ea-967e-4e982dd4542d · outbound

This paper cites Complementarity-aware space learning for video-text retrieval.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Complementarity-aware space learning for video-text retrieval

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:31.058055Z

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Observation b1b0de1f-ac36-4a21-9a62-6a395843b727 · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Denseclip: Language- guided dense prediction with context-aware prompting

Reference 22

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Observation 8f864528-e0aa-4af5-8cb6-e74c775337d3 · outbound

This paper cites Extract free dense labels from clip.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Extract free dense labels from clip

Reference 23

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Observation bed7ca1b-cf45-4380-9772-cc5b841bc717 · outbound

This paper cites Clip-nerf: Text-and-image driven manipula- tion of neural radiance fields.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Clip-nerf: Text-and-image driven manipula- tion of neural radiance fields

Reference 24

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Observation a4cb55e4-90d6-4568-b21f-eb9ee2d7d48e · outbound

This paper cites Styleclip: Text-driven manipulation of stylegan imagery.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Styleclip: Text-driven manipulation of stylegan imagery

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.998539Z

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Observation 705724cd-72a8-49c2-b018-56e5ca3ffd8a · outbound

This paper cites Parameter-efficient transfer learning for NLP.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Parameter-efficient transfer learning for NLP

Reference 26

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Observation d5674e47-e6c9-4c04-942d-a74b5670c08c · outbound

This paper cites Learning a universal tem- plate for few-shot dataset generalization.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Learning a universal tem- plate for few-shot dataset generalization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.968078Z

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Observation 480c1647-b762-425d-a5cf-994d7e30d472 · outbound

This paper cites Reliable Few-shot Learning under Dual Noises.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Reliable Few-shot Learning under Dual Noises

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.953004Z

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Observation ee68335c-474d-41d7-b3c2-006ad3b17063 · outbound

This paper cites Prefix-Tuning: Optimiz- ing Continuous Prompts for Generation.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Prefix-Tuning: Optimiz- ing Continuous Prompts for Generation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.939262Z

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Observation f1598afd-3a49-40a5-9ed7-b39261f22984 · outbound

This paper cites Skip Tuning: Pre-trained Vision- Language Models are Effective and Efficient Adapters Themselves.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Skip Tuning: Pre-trained Vision- Language Models are Effective and Efficient Adapters Themselves

Reference 30

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Observation 60f427b5-a497-4c2f-bcab-f211532013d6 · outbound

This paper cites Lora: Low-rank adapta- tion of large language models.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Lora: Low-rank adapta- tion of large language models

Reference 31

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Observation e8ce2f53-e69d-4b28-b94d-4f21f944307d · outbound

This paper cites Learning to decompose visual features with latent textual prompts.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Learning to decompose visual features with latent textual prompts

Reference 32

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Observation 4478a318-60e6-48e8-b33f-3fae6c334823 · outbound

This paper cites Prompt learning with optimal transport for vision-language models.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Prompt learning with optimal transport for vision-language models

Reference 33

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Observation af539285-740e-42f7-b81f-4020ddae8e05 · outbound

This paper cites Consistent Prompt Tuning for Generalized Category Discovery.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Consistent Prompt Tuning for Generalized Category Discovery

Reference 34

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Observation 0f1fca09-a50e-4fac-94c8-cd61ccb67226 · outbound

This paper cites Progressive visual prompt learn- ing with contrastive feature re-formation.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Progressive visual prompt learn- ing with contrastive feature re-formation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.850748Z

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Observation bd326d7c-1da3-498a-bd16-a395faa968e7 · outbound

This paper cites HybridPrompt: Domain-Aware Prompting for Cross-Domain Few-Shot Learning.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models HybridPrompt: Domain-Aware Prompting for Cross-Domain Few-Shot Learning

Reference 36

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Observation f3770958-619b-4d86-9596-660fe212a0f4 · outbound

This paper cites DETA: Denoised Task Adaptation for Few- Shot Learning.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models DETA: Denoised Task Adaptation for Few- Shot Learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.821226Z

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Observation f5c2de13-800a-4f7c-95ba-73f8579489c8 · outbound

This paper cites Meta-fdmixup: Cross- domain few-shot learning guided by labeled target data.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Meta-fdmixup: Cross- domain few-shot learning guided by labeled target data

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.806719Z

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Observation 5f6c5ee2-111f-43c9-afdb-6bb457702b20 · outbound

This paper cites StyleAdv: Meta Style Adversarial Training for Cross- Domain Few-Shot Learning.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models StyleAdv: Meta Style Adversarial Training for Cross- Domain Few-Shot Learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.791554Z

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Observation 1c6bea8b-0247-452b-b50d-04995c25ba88 · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Tip-adapter: Training-free adaption of clip for few-shot classification

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.775458Z

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Observation c63402ce-65f7-428e-88c5-d2c1a37ad210 · outbound

This paper cites Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.761137Z

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Observation c5ce0667-06f8-4374-8ad7-d835396504fa · outbound

This paper cites Visual prompt tuning.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Visual prompt tuning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.745982Z

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Observation 4a4e4f27-5f3a-44b5-99cc-26804a2d49d9 · outbound

This paper cites Diversity-Aware Meta Visual Prompting.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Diversity-Aware Meta Visual Prompting

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.732034Z

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Observation 79a15588-09d9-4db5-b08e-f4e22987960e · outbound

This paper cites Self-regulating Prompts: Foundational Model Adaptation without For- getting.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Self-regulating Prompts: Foundational Model Adaptation without For- getting

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.716086Z

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Observation abf237a6-6657-46ff-8161-eaa9f78ebafb · outbound

This paper cites Learn- ing to prompt for vision-language models.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Learn- ing to prompt for vision-language models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:31.273491Z

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Observation 0fee4f5e-cf6d-415c-a3da-d8332fc03501 · outbound

This paper cites FastText.zip: Compress- ing text classification models.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models FastText.zip: Compress- ing text classification models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.699715Z

Source-reported events for the cited work

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Observation 350e753a-4400-484b-ac1a-5eb67e9b7f73 · outbound

This paper cites Wikipedia2Vec: An Efficient Toolkit for Learning and Visual- izing the Embeddings of Words and Entities from Wikipedia.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Wikipedia2Vec: An Efficient Toolkit for Learning and Visual- izing the Embeddings of Words and Entities from Wikipedia

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.684776Z

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Observation c260c1fe-87de-4dca-9927-95eb05589038 · outbound

This paper cites Glove: Global vectors for word representation.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Glove: Global vectors for word representation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.669294Z

Source-reported events for the cited work

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Observation 57ce1808-9b63-465f-b022-496190804d30 · outbound

This paper cites Clip-adapter: Bet- ter vision-language models with feature adapters.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Clip-adapter: Bet- ter vision-language models with feature adapters

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.654120Z

Source-reported events for the cited work

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Observation 6f27647e-a206-418b-8e45-9bf4f2bf7371 · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Imagenet: A large-scale hierarchical image database

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.638243Z

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Observation 1b176023-3367-4a47-8ab1-0c7f44b3df28 · outbound

This paper cites Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.623077Z

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Observation 9a042d4f-8691-4172-b24d-3b4633719ce5 · outbound

This paper cites Cats and dogs.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Cats and dogs

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.608315Z

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Observation f7430805-a9f0-488d-8c1e-a8628d17988b · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models 3d object representations for fine-grained cate- gorization

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.593568Z

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Observation 02f2ed06-dc1d-4303-9342-39a78b69ddd3 · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Automated flower classification over a large number of classes

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.578974Z

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Observation 832a59e4-bc31-4c18-80db-fdf0f360847b · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Food- 101–mining discriminative components with random forests

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.565191Z

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Observation 015b16ee-8ea4-4553-b6ba-443c9a3e154b · outbound

This paper cites Fine-grained visual classification of aircraft.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Fine-grained visual classification of aircraft

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.551062Z

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Observation 2d800825-dc08-47ca-a7ad-ff67b1ac93c0 · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Eurosat: A novel dataset and deep learning benchmark for land use and land cover clas- sification

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.537081Z

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Observation f6a0116f-583d-40f5-bfe6-51c21753d908 · outbound

This paper cites UCF101: A dataset of 101 human actions classes from videos in the wild.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models UCF101: A dataset of 101 human actions classes from videos in the wild

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.522367Z

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Observation 4f69ec35-4baf-462d-bb9e-77979fcc612c · outbound

This paper cites Describing textures in the wild.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Describing textures in the wild

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.507329Z

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Observation 8cc07c26-2837-415e-a9fc-2cb61bfa1372 · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Sun database: Large-scale scene recognition from abbey to zoo

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.493351Z

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Observation 3c50391b-a103-4d08-95b0-bca52642ed36 · outbound

This paper cites Do imagenet classifiers generalize to ima- genet? In: ICML.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Do imagenet classifiers generalize to ima- genet? In: ICML

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.478310Z

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Observation a86bfedb-ea4f-4ecd-adf1-076560e3ea95 · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Learning robust global representations by penalizing local predictive power

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.463982Z

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Observation d38aec91-3c1b-440e-a579-6897d1ddcc06 · outbound

This paper cites Generating natural adversarial examples with universal perturbations for text classifi- cation.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Generating natural adversarial examples with universal perturbations for text classifi- cation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.449439Z

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Observation 50480c85-a5d9-43cb-bb90-4759a3a55d65 · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models The many faces of robustness: A critical analysis of out-of- distribution generalization

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.434073Z

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Observation cc3ac732-1f26-4196-9457-1185694f221f · outbound

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

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Self-regulating prompts: Foundational model adaptation without for- getting

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.419746Z

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Observation d2dddb92-a4f7-4687-8abe-2f0122007eba · outbound

This paper cites Prompt distribution learning.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Prompt distribution learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.403407Z

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Observation 12256246-c546-4514-bd14-aa92e610ee22 · outbound

This paper cites Black box few-shot adaptation for vision- language models.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Black box few-shot adaptation for vision- language models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:37:30.388579Z

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Observation 34bbdeb4-00de-4106-bb4a-63bd56f8f5e9 · outbound

This paper cites Read-only prompt optimization for vision- language few-shot learning.

A Closer Look at Conditional Prompt Tuning for Vision-Language Models Read-only prompt optimization for vision- language few-shot learning

Reference 68

Resolution: verified exact; raw_fallback, observed 2026-08-06T21:37:30.372993Z

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Pith citing papers

Observation a9dd6569-f00e-439d-a9a9-0f6aff9a755a · inbound

M^2C-EvDet: Multi-Domain Multi-Order Cross-Modal Knowledge Distillation for Event-based Object Detection cites this paper.

M^2C-EvDet: Multi-Domain Multi-Order Cross-Modal Knowledge Distillation for Event-based Object Detection A Closer Look at Conditional Prompt Tuning for Vision-Language Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:29:57.637515Z

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