Paper Citation Record · LEDGER

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation

As of 13 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 2 inbound Pith citation observations for arXiv:2502.00896.

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

pith.paper-citation-record.v1
2502.00896 v3

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:23:26.427221Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T00:19:10.202472Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T22:36:16.613779Z

Reference resolution

55 of 55 outbound references displayed

verified exact2
verified fuzzy25
unresolved28
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 02aa90e1-0676-42ac-83c5-c465a32a2b03 · outbound

This paper cites Can SGD learn recurrent neural networks with provable generalization? In Advances in Neural Information Processing Systems (NeurIPS), pp.\ 10310--10320, 2019.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Can SGD learn recurrent neural networks with provable generalization? In Advances in Neural Information Processing Systems (NeurIPS), pp.\ 10310--10320, 2019

Reference 1

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Observation ad8d6fc8-f043-43e5-ac72-2d6e473d82da · outbound

This paper cites On the backward stability of SGD and its use for asymptotic model selection in neural networks.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation On the backward stability of SGD and its use for asymptotic model selection in neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:23:28.176574Z

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Observation 5f0ea8cf-8cf1-46bb-bec6-20187ffbf566 · outbound

This paper cites On the convergence rate of training recurrent neural networks.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation On the convergence rate of training recurrent neural networks

Reference 3

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Observation eb38e87a-0108-437c-ade5-4e208a6b54fa · outbound

This paper cites Backward feature correction: How deep learning performs deep learning.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Backward feature correction: How deep learning performs deep learning

Reference 4

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Observation 509017f4-7d0c-47fd-8829-f6d09806e771 · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Exploring Visual Prompts for Adapting Large-Scale Models

Reference 5

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Observation 9cdc031d-29cd-48bb-b3e1-f83d062264d3 · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 6

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Observation 01e9a9df-5b2d-47be-9ff2-c442482d8a91 · outbound

This paper cites Language models are few-shot learners.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Language models are few-shot learners

Reference 7

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Observation 2bde678e-c7d4-4964-ae89-0ca99d5039b0 · outbound

This paper cites Sample-specific masks for visual reprogramming-based prompting.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Sample-specific masks for visual reprogramming-based prompting

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:23:28.117827Z

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Observation 75e7413d-95f2-4850-a41f-4f8e367f9631 · outbound

This paper cites Singular value thresholding algorithm for matrix completion.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Singular value thresholding algorithm for matrix completion

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:23:28.099666Z

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Observation 46617e12-0318-4d80-acd3-527f442bc196 · outbound

This paper cites Understanding and improving visual prompting: A label-mapping perspective.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Understanding and improving visual prompting: A label-mapping perspective

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:23:28.082796Z

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Observation 4b25d2e7-fd79-4ec0-aac0-2c0302a223e0 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 11

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Observation 5f12d1ab-5db6-4629-91a9-b087212a4387 · outbound

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

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Imagenet: A large-scale hierarchical image database

Reference 12

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Observation 05ef099a-18ae-4272-ba75-6d8132029b65 · outbound

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

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation An image is worth 16x16 words: Transformers for image recognition at scale

Reference 13

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Observation d93aa4b1-bb98-4353-981a-5190432e2d6a · outbound

This paper cites Neural networks are more expressive than kernel methods: A representational perspective.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Neural networks are more expressive than kernel methods: A representational perspective

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:23:28.032736Z

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Observation cf0cc017-6bf9-448b-9a75-d17b0d9261c8 · outbound

This paper cites Low-rank tensor approximation techniques.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Low-rank tensor approximation techniques

Reference 15

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Observation b8be0e50-bc54-4b6e-bd71-679274a9654c · outbound

This paper cites Svdiff: Compact parameter space for diffusion fine-tuning.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Svdiff: Compact parameter space for diffusion fine-tuning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:23:27.973984Z

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Observation 8537da5e-4eaa-4a75-8dfb-b8c33a094859 · outbound

This paper cites Proxedit: Improving tuning-free real image editing with proximal guidance.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Proxedit: Improving tuning-free real image editing with proximal guidance

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:23:27.956943Z

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Observation 8d3b917d-ede3-4426-a0e6-ccecd19f7207 · outbound

This paper cites Deep residual learning for image recognition.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Deep residual learning for image recognition

Reference 18

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Observation a9f2b3d7-7d7a-40fa-babb-49962d4fb7d2 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Momentum contrast for unsupervised visual representation learning

Reference 19

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Observation 309988ed-3f30-44dd-8c60-76658aae8932 · outbound

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

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 20

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Observation 8b4a386c-f5e8-4cfb-bcd6-86df202553ad · outbound

This paper cites Natural adversarial examples.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Natural adversarial examples

Reference 21

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Observation 34e13be7-622f-4f6f-8148-ef079d1c121f · outbound

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

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation LoRA: Low-Rank Adaptation of Large Language Models

Reference 22

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Observation b309a9b8-1bcd-46b5-9ba4-0b7765dbb617 · outbound

This paper cites Visual Prompt Tuning.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Visual Prompt Tuning

Reference 23

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Observation 3af2ceda-7e30-4bb7-8ad5-ef6d5569f530 · outbound

This paper cites Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective

Reference 24

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Observation 0ece593b-d7fe-4af2-b377-d32e7f25ac9a · outbound

This paper cites Initialization and regularization of factorized neural layers.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Initialization and regularization of factorized neural layers

Reference 25

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Observation fd4b4217-1ef1-4da1-8c78-1d2013efa88d · outbound

This paper cites Learning multiple layers of features from tiny images.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Learning multiple layers of features from tiny images

Reference 26

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Observation 26ade53f-45c0-448c-abc7-f2f4495a7acb · outbound

This paper cites Tiny imagenet visual recognition challenge.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Tiny imagenet visual recognition challenge

Reference 27

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Observation ba9cecf7-cd2a-44bb-9416-58e723d84ce1 · outbound

This paper cites Low-rank adaptation of large neural networks.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Low-rank adaptation of large neural networks

Reference 28

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Observation bbbbd946-7f71-44f9-885c-1e5c0dfb71b0 · outbound

This paper cites Low-rank matrix recovery.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Low-rank matrix recovery

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:23:27.751741Z

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Observation a7bae84d-b14d-4617-8ff2-1e7daf199ab8 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Prefix-tuning: Optimizing continuous prompts for generation

Reference 30

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Observation 4ce8d604-5c3a-4b75-a3a1-75fa1431c5d3 · outbound

This paper cites Learning overparameterized neural networks via stochastic gradient descent on structured data.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Learning overparameterized neural networks via stochastic gradient descent on structured data

Reference 31

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Observation 1cd4b782-440d-4f96-988f-615b4fc7dbcd · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning

Reference 32

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Observation 18780cce-4223-4f90-b5a3-f95792e1ab52 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 33

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Observation 5b18365f-4320-4220-8bba-cf65265a25c2 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 34

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Observation 8c4768ca-62f3-4f41-893b-546881253984 · outbound

This paper cites Blackvip: Black-box visual prompting for robust transfer learning.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Blackvip: Black-box visual prompting for robust transfer learning

Reference 35

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Observation 69923694-7cff-4a2d-be13-822165708db8 · outbound

This paper cites Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian

Reference 36

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Maxk-gnn: Extremely fast gpu kernel design for accelerating graph neural networks training

Reference 37

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Observation d77938bf-e318-48cc-9cb7-fc2e42e55ba3 · outbound

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

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Learning transferable visual models from natural language supervision

Reference 38

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This paper cites Do imagenet classifiers generalize to imagenet? In International conference on machine learning, pp.\ 5389--5400.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Do imagenet classifiers generalize to imagenet? In International conference on machine learning, pp.\ 5389--5400

Reference 39

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Imagenet-21k pretraining for the masses

Reference 40

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Observation beff1e54-f5f2-4f2b-a51b-6357404617d6 · outbound

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Low-rank matrix factorization for deep neural network training with high-dimensional output targets

Reference 41

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Observation dee966a7-0dcc-47df-a048-c749b8968b4d · outbound

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Logan IV, Eric Wallace, and Sameer Singh

Reference 42

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources

Reference 43

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation AutoVP: An Automated Visual Prompting Framework and Benchmark

Reference 44

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

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Learning robust global representations by penalizing local predictive power

Reference 45

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Unresolved cited work

Reference 46

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Observation 9309e169-4883-4825-be12-e595ec6f1877 · outbound

This paper cites Prompt tuning for unified multimodal pretrained models.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Prompt tuning for unified multimodal pretrained models

Reference 47

Resolution: verified exact; doi, observed 2026-08-09T17:23:26.576839Z

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Observation 3b48010f-9e66-46d0-937b-5c5c57f78264 · outbound

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Dynamic Integration of Task-Specific Adapters for Class Incremental Learning

Reference 48

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression

Reference 49

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Facial landmark detection by deep multi-task learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:23:26.967238Z

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Observation 0c9cbd33-d23d-462b-984f-9d8110a6cd8b · outbound

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Energy-efficient image classification on low-power iot devices

Reference 51

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Observation 2742a1e9-221d-4c53-a0c2-f91068d03023 · outbound

This paper cites write newline.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation write newline

Reference 52

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation @esa (Ref

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Unresolved cited work

Reference 54

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LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Unresolved cited work

Reference 55

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Pith citing papers

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Beyond Low-Rank: Low-Rank Sparse Prompting via Spiking Neural Network and Prompt Factorization cites this paper.

Beyond Low-Rank: Low-Rank Sparse Prompting via Spiking Neural Network and Prompt Factorization LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-01T22:36:16.615782Z

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HeteroPROPMT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework cites this paper.

HeteroPROPMT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation

Reference 21

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