Paper Citation Record · LEDGER

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves

As of 14 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2412.11509.

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

pith.paper-citation-record.v1
2412.11509 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:55:41.073787Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:26:34.671641Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:26:35.642817Z

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation 50196bb7-1941-4002-848c-039290e6c9a1 · outbound

This paper cites GPT-4 Technical Report.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves GPT-4 Technical Report

Reference 1

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Observation a5a51f63-df43-42ec-aba2-7c1ea5438fa4 · outbound

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Food-101–mining discriminative components with random forests

Reference 2

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Observation f1d5b6ac-5348-4101-a2ea-f9d5e6efdd64 · outbound

This paper cites Describing textures in the wild.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Describing textures in the wild

Reference 3

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Observation 12676e42-8686-4727-899c-c51b06e29ad6 · outbound

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Imagenet: A large-scale hierarchical image database

Reference 4

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Observation fc7cf552-8be5-4f6f-ac23-fd1b29b84956 · outbound

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 5

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Observation 0f96c8ec-4c78-4462-81ce-30069592af70 · outbound

This paper cites Prompt- det: Towards open-vocabulary detection using uncurated im- ages.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Prompt- det: Towards open-vocabulary detection using uncurated im- ages

Reference 6

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Observation c4eb3178-c79a-496d-a013-837de9c261ec · outbound

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Generalized meta-fdmixup: Cross-domain few-shot learning guided by labeled target data

Reference 7

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Observation 36f32ad2-6175-4f97-90b5-2f35af77f568 · outbound

This paper cites Styleadv: Meta style adversarial training for cross-domain few-shot learning.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Styleadv: Meta style adversarial training for cross-domain few-shot learning

Reference 8

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Observation 69625c8a-da00-4531-82f3-b721d9cf621f · outbound

This paper cites Cross-domain few-shot object detection via enhanced open-set object detector.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Cross-domain few-shot object detection via enhanced open-set object detector

Reference 9

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Observation 39a99d8c-286a-44e7-a172-e881a96deafd · outbound

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Clip-adapter: Better vision-language models with feature adapters

Reference 10

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Observation 038038e9-71d8-4733-8b9f-3a472b58f70f · outbound

This paper cites Open-vocabulary Object Detection via Vision and Language Knowledge Distillation.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Open-vocabulary Object Detection via Vision and Language Knowledge Distillation

Reference 11

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Observation 31dd6fd7-3d72-4954-83e2-f2c83fff7ddc · outbound

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 12

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Observation 8e503ee4-dba8-447f-8507-3be414a1399c · outbound

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves The many faces of robust- ness: A critical analysis of out-of-distribution generalization

Reference 13

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Observation 08e6694f-6f22-4b42-abc0-b67bd7c2c8eb · outbound

This paper cites Natural adversarial examples.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Natural adversarial examples

Reference 14

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Observation d7d6ad56-eb48-4648-be15-67e927630919 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves LoRA: Low-Rank Adaptation of Large Language Models

Reference 15

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Observation c7bd8b7d-e88d-42fa-b6d5-1e4c6f88fcd4 · outbound

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 16

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Observation 218717cc-5862-437d-8a66-23f6df721752 · outbound

This paper cites Maple: Multi-modal prompt learning.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Maple: Multi-modal prompt learning

Reference 17

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Self-regulating prompts: Foundational model adaptation without forgetting

Reference 18

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Observation cae21571-0a4d-46a5-9316-9ff8c2c0746c · outbound

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves 3d object representations for fine-grained categorization

Reference 19

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Observation c4dedc22-9636-4ae1-8f2c-ddd769184ec5 · outbound

This paper cites Image segmenta- tion using text and image prompts.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Image segmenta- tion using text and image prompts

Reference 20

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Observation fffef978-e826-4180-9f1e-bddedd282271 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Fine-Grained Visual Classification of Aircraft

Reference 21

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Automated flower classification over a large number of classes

Reference 22

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This paper cites Cats and dogs.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Cats and dogs

Reference 23

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Learning transferable visual models from natural language supervi- sion

Reference 24

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Observation 6c0aa5d0-9c55-4141-a48f-25ba5ede9a99 · outbound

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Denseclip: Language-guided dense prediction with context- aware prompting

Reference 25

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Observation 28f4a049-da1a-453c-be0c-ac38c72a895e · outbound

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Do imagenet classifiers generalize to im- agenet? In International conference on machine learning , pages 5389–5400

Reference 26

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This paper cites Consistency-guided Prompt Learning for Vision-Language Models.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Consistency-guided Prompt Learning for Vision-Language Models

Reference 27

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 28

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves LLaMA: Open and Efficient Foundation Language Models

Reference 29

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This paper cites Learning robust global representations by penalizing local predictive power.Advances in Neural Information Pro- cessing Systems, 32, 2019.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Learning robust global representations by penalizing local predictive power.Advances in Neural Information Pro- cessing Systems, 32, 2019

Reference 30

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Sun database: Large-scale scene recognition from abbey to zoo

Reference 31

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This paper cites Visual- language prompt tuning with knowledge-guided context op- timization.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Visual- language prompt tuning with knowledge-guided context op- timization

Reference 32

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This paper cites Tcp: Textual- based class-aware prompt tuning for visual-language model.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Tcp: Textual- based class-aware prompt tuning for visual-language model

Reference 33

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves FILIP: Fine-grained Interactive Language-Image Pre-Training

Reference 34

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This paper cites Open-vocabulary detr with conditional matching.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Open-vocabulary detr with conditional matching

Reference 35

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

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Lit: Zero-shot transfer with locked-image text tuning

Reference 36

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This paper cites Free-lunch for cross-domain few-shot learning: Style-aware episodic training with robust contrastive learning.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Free-lunch for cross-domain few-shot learning: Style-aware episodic training with robust contrastive learning

Reference 37

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This paper cites Deta: Denoised task adaptation for few-shot learning.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Deta: Denoised task adaptation for few-shot learning

Reference 38

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Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Dept: Decoupled prompt tuning

Reference 39

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This paper cites Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Reference 40

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This paper cites Conditional prompt learning for vision-language mod- els.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Conditional prompt learning for vision-language mod- els

Reference 41

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Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Learning to prompt for vision-language models

Reference 42

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This paper cites Detecting twenty-thousand classes using image-level supervision.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Detecting twenty-thousand classes using image-level supervision

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:55:41.269575Z

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This paper cites Prompt-aligned gradient for prompt tuning.

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves Prompt-aligned gradient for prompt tuning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:55:41.253467Z

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Pith citing papers

Observation a41e6341-a21e-4246-aefd-e225f5a2fa8e · inbound

Dynamic Rank Adaptation for Vision-Language Models cites this paper.

Dynamic Rank Adaptation for Vision-Language Models Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves

Reference 33

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