Paper Citation Record · LEDGER

Noise is an Efficient Learner for Zero-Shot Vision-Language Models

As of 9 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 3 inbound Pith citation observations for arXiv:2502.06019.

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

pith.paper-citation-record.v1
2502.06019 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:05:17.765360Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:21:19.132444Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T23:17:14.256179Z

Reference resolution

42 of 42 outbound references displayed

verified exact1
verified fuzzy27
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 06add40f-66f4-42d6-9916-000b795e8480 · outbound

This paper cites Badclip: Trigger-aware prompt learning for backdoor attacks on clip.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Badclip: Trigger-aware prompt learning for backdoor attacks on clip

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.256617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f07f2dd5-021a-4a56-8102-604449ed287f · outbound

This paper cites Rep- resentation learning: A review and new perspectives.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Rep- resentation learning: A review and new perspectives

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.245615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 58f45380-b6fe-4947-b7a0-2c9731914bf6 · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Food-101–mining discriminative components with random forests

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.235143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f92dc26e-13e5-4479-86f4-9e9c2ea667c6 · outbound

This paper cites Describing textures in the wild.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Describing textures in the wild

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.224533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9a262bfe-d48a-4ce7-9ce2-b3287af5125b · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.213625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d1cbf18a-2011-493b-af89-b70492c2a15b · outbound

This paper cites Generative adversarial nets.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Generative adversarial nets

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.629208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6506260d-375d-49ec-a1fc-becf15612e03 · outbound

This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Goodfellow, Jonathon Shlens, and Christian Szegedy

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.195603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5f774dd8-677d-41db-97df-a6ab7963265c · outbound

This paper cites Calip: Zero-shot en- hancement of clip with parameter-free attention.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Calip: Zero-shot en- hancement of clip with parameter-free attention

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.185277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3dd6c251-ac40-4255-9fc8-e3e227f84e77 · outbound

This paper cites Baple: Back- door attacks on medical foundational models using prompt learning.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Baple: Back- door attacks on medical foundational models using prompt learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.174414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 50f59c35-4782-4e30-a9a3-775c195b84e6 · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.164120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 91729fae-8f58-4761-a827-5fe7eddb93b1 · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models The many faces of robust- ness: A critical analysis of out-of-distribution generalization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.650651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1c7c4d6-3b59-4325-8711-803fada7123c · outbound

This paper cites Natural adversarial examples.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Natural adversarial examples

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.147552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 17ab93fe-1d0a-466f-a894-6cf4adad2ace · outbound

This paper cites Denoising dif- fusion probabilistic models.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Denoising dif- fusion probabilistic models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.658597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c50ac094-ffd3-4cf7-b319-653bef6dbb26 · outbound

This paper cites Test-time low rank adaptation via confi- dence maximization for zero-shot generalization of vision- language models, 2024.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Test-time low rank adaptation via confi- dence maximization for zero-shot generalization of vision- language models, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.130266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c88849e1-8c76-454e-8287-1c50d3b8fdd9 · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.666164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e9c4ffd-78b3-498a-8036-6bc085ce7bcf · outbound

This paper cites Auto-Encoding Variational Bayes.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Auto-Encoding Variational Bayes

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.670166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 264673d6-341d-45d5-9aff-9ff3e742f47e · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models 3d object representations for fine-grained categorization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.110815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 179c0051-3388-42ca-9553-27b220a32694 · outbound

This paper cites Revisiting batch normalization for practical do- main adaptation, 2016.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Revisiting batch normalization for practical do- main adaptation, 2016

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.098597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation da1e8594-af99-4280-a083-9310f247a11b · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Fine-Grained Visual Classification of Aircraft

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.679456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7222ee9a-f2e7-469f-a478-442fccc78cf1 · outbound

This paper cites Robust Calibration of Large Vision-Language Adapters.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Robust Calibration of Large Vision-Language Adapters

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-08T17:05:17.877440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 960b5afc-9bd8-4dc9-8541-81638a56c30b · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Automated flower classification over a large number of classes

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.085955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a94864d8-69c5-4e42-8a97-ed0f3a4b60ee · outbound

This paper cites Cats and dogs.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Cats and dogs

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.073805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ed2bacd1-f49f-41e8-b3cd-4632b765dceb · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.692177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fda3c33-d068-41c2-a62a-09ec13c87dcc · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Learning transferable visual models from natural language supervi- sion

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.061793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fb7dc0af-c750-453c-a975-579d05bcefc0 · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Do imagenet classifiers generalize to im- agenet? In International conference on machine learning , pages 5389–5400

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.050088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fe9ace95-0236-4850-8a85-cd49dd9e5197 · outbound

This paper cites Stochastic backpropagation and approximate inference in deep generative models.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Stochastic backpropagation and approximate inference in deep generative models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.038803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7a1b56f6-e1c8-4171-aa92-618e063b885e · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Test- time prompt tuning for zero-shot generalization in vision- language models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.026390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 97024579-8da8-4da3-a0f9-d1b2232a3047 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Generative modeling by estimating gradients of the data distribution

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:18.013645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 32ee7626-008e-48a3-8f2a-7a55156f752f · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.715629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fce2b5b-f784-4ee3-9ab4-671ba89e7cfb · outbound

This paper cites Test-time training with self- supervision for generalization under distribution shifts.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Test-time training with self- supervision for generalization under distribution shifts

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.719814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f369ef1-8440-464b-a744-e670db9486d7 · outbound

This paper cites An Empirical Study Into What Matters for Calibrating Vision-Language Models.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models An Empirical Study Into What Matters for Calibrating Vision-Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.723662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8bf3e70-11d7-4d42-8d23-8236de17512f · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.727647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c2a8112-4c83-481a-bb06-53dc18a40b11 · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Learning robust global representations by penalizing local predictive power.Advances in Neural Information Pro- cessing Systems, 32, 2019

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:17.992766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9841ecbe-6055-4ddb-8f3d-240d14a5b689 · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Sun database: Large-scale scene recognition from abbey to zoo

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:17.980458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 762629f4-98c0-4959-9644-2bf774d3b00a · outbound

This paper cites Beyond Model Adaptation at Test Time: A Survey.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Beyond Model Adaptation at Test Time: A Survey

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.738938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be71eeab-1276-4ece-a8b7-0c42f954c613 · outbound

This paper cites C-tpt: Calibrated test-time prompt tuning for vision-language mod- els via text feature dispersion.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models C-tpt: Calibrated test-time prompt tuning for vision-language mod- els via text feature dispersion

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:17.967668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 159a8db6-b8d8-4774-8431-1cf4f38a96b7 · outbound

This paper cites Memo: Test time robustness via adaptation and augmentation.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Memo: Test time robustness via adaptation and augmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:17.956515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b5611b0f-38a6-49c3-8d1f-1887a6de37ab · outbound

This paper cites Memo: Test time robustness via adaptation and augmentation, 2022.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Memo: Test time robustness via adaptation and augmentation, 2022

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:17.946308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1b332cba-2a91-46cb-90c3-b1ddf649b293 · outbound

This paper cites Test-time adaptation with clip reward for zero-shot gener- alization in vision-language models.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Test-time adaptation with clip reward for zero-shot gener- alization in vision-language models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:17.934752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 454c9955-0255-47fa-882a-2e9bea8ec9eb · outbound

This paper cites Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models.

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.757886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ea244e1-63b5-4aeb-a68f-a56e60dfad9c · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Learning to prompt for vision-language models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:05:17.921927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1ec35c67-ca7a-4d5e-936f-e46e1780b083 · outbound

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

Noise is an Efficient Learner for Zero-Shot Vision-Language Models Learning to prompt for vision-language models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:17.765360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation bd4549b0-3e2a-421f-bf14-7c5a85841c67 · inbound

On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable? cites this paper.

On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable? Noise is an Efficient Learner for Zero-Shot Vision-Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:19.132444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ea186bd-6a2d-4f2e-9755-61afd259452c · inbound

Adapting Vision-Language Models Without Labels: A Comprehensive Survey cites this paper.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Noise is an Efficient Learner for Zero-Shot Vision-Language Models

Reference 167

Resolution: verified exact; local_arxiv, observed 2026-08-05T23:17:14.261226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 75b0ab1b-a219-4e23-b106-cf629ad64b23 · inbound

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift cites this paper.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Noise is an Efficient Learner for Zero-Shot Vision-Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.548210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.