TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models | Paper Citation Record

Observation a67bfdc0-bb18-47cf-b57c-f74cd3ee028e · outbound

This paper cites Align your prompts: Test-time prompting with distribution align- ment for zero-shot generalization.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Align your prompts: Test-time prompting with distribution align- ment for zero-shot generalization

Reference 1

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Observation 518df696-ba5f-48be-82fe-8123e754af2d · outbound

This paper cites GPT-4 Technical Report.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models GPT-4 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T16:52:27.606350Z

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Observation 651d3ff6-fbbf-44bb-9dc3-3c9d669a8112 · outbound

This paper cites Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 3

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Observation 6a910d48-3f98-4d7b-ae50-3cabe55e4bc7 · outbound

This paper cites Agreement-on-the-line: Predicting the performance of neural networks under distribution shift.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Agreement-on-the-line: Predicting the performance of neural networks under distribution shift

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.598294Z

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Observation 8d7aa66b-5300-4825-8282-f41aa7eb22ca · outbound

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Exploring Visual Prompts for Adapting Large-Scale Models

Reference 5

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Observation b26e17d1-b382-418a-9f38-0b174356dffd · outbound

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Food-101–mining discriminative components with random forests

Reference 6

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Observation 574c6f45-62c4-4bf5-8e74-ce4e1fea7467 · outbound

This paper cites Describing textures in the wild.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Describing textures in the wild

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.580641Z

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Observation 6ee214d9-ad4f-401e-b09c-3c6bdd443200 · outbound

This paper cites Reliable evalua- tion of adversarial robustness with an ensemble of diverse parameter-free attacks.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Reliable evalua- tion of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.569826Z

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Observation 0b4d5f23-90b0-4e33-a785-5a265b30c801 · outbound

This paper cites Boosting adversarial at- tacks with momentum.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Boosting adversarial at- tacks with momentum

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.558233Z

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Observation 81fd44ab-0bef-4235-a7bd-c303fa34ffcf · outbound

This paper cites One perturbation is enough: On generating universal adversarial perturbations against vision- language pre-training models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models One perturbation is enough: On generating universal adversarial perturbations against vision- language pre-training models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T16:52:27.638672Z

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Observation 349303b4-a001-43b0-9ebe-95f81653ba83 · outbound

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.546801Z

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Observation 78b00c95-053d-41f0-a8ca-e158440afe2f · outbound

This paper cites Large-scale adversarial training for vision- and-language representation learning.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Large-scale adversarial training for vision- and-language representation learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.535430Z

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Observation 8dedbda0-6fba-4535-acf3-d9cf26e09085 · outbound

This paper cites SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T16:52:27.650257Z

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Observation 13ec3537-cef7-4828-bd1d-65f5b106dadb · outbound

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.524362Z

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Observation 29a8e459-3192-42de-b62e-bbf335eb3ab5 · outbound

This paper cites A visual–language foundation model for pathology image analysis using medical twitter.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models A visual–language foundation model for pathology image analysis using medical twitter

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.512561Z

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Observation 0fcf6700-6d89-424a-a80b-78b7c1792702 · outbound

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Scaling up visual and vision-language representation learning with noisy text supervision

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T16:52:27.661569Z

Source-reported events for the cited work

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Observation 3e864907-8af8-4236-930d-fe36aa388c06 · outbound

This paper cites Simple but effective: Clip embed- dings for embodied ai.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Simple but effective: Clip embed- dings for embodied ai

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.494306Z

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Observation e4c18c12-1230-482f-b5fb-de29ca7e3b9d · outbound

This paper cites Maple: Multi-modal prompt learning.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Maple: Multi-modal prompt learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.483450Z

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Observation 3af7214f-5933-4239-84e5-58bef45385b5 · outbound

This paper cites Test-time adaptation induces stronger accuracy and agreement-on-the-line.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Test-time adaptation induces stronger accuracy and agreement-on-the-line

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.473031Z

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Observation 4b90cd3b-b27b-416b-bb0e-734cc5e92eb6 · outbound

This paper cites Torchattacks: A PyTorch Repository for Adversarial Attacks.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Torchattacks: A PyTorch Repository for Adversarial Attacks

Reference 20

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Observation da76e5b1-5d8c-4c01-884f-4b77136b21bb · outbound

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models 3d object representations for fine-grained categorization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.461508Z

Source-reported events for the cited work

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Observation c39837d6-b2f8-4a78-8c6f-b82a8e18a410 · outbound

This paper cites One prompt word is enough to boost adversarial robustness for pre-trained vision-language models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models One prompt word is enough to boost adversarial robustness for pre-trained vision-language models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.449188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0a9c9bd7-3b95-4b63-9e85-4fa6c0761bb3 · outbound

This paper cites Set-level guidance at- tack: Boosting adversarial transferability of vision-language pre-training models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Set-level guidance at- tack: Boosting adversarial transferability of vision-language pre-training models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.437381Z

Source-reported events for the cited work

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Observation 3c92c15d-06cd-4cef-bc1c-e32fe4662192 · outbound

This paper cites Imbalanced gradients: a sub- tle cause of overestimated adversarial robustness.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Imbalanced gradients: a sub- tle cause of overestimated adversarial robustness

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.425163Z

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Observation dde244a9-6eb2-41f6-957f-c4d04065347b · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Towards deep learning models resistant to adversarial attacks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.413171Z

Source-reported events for the cited work

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Observation 976d04d3-ca59-4659-86a6-534baca1fdb4 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Fine-Grained Visual Classification of Aircraft

Reference 26

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Observation d48d05f9-ef81-4059-8835-d8954c02519c · outbound

This paper cites Understanding zero-shot adversarial robust- ness for large-scale models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Understanding zero-shot adversarial robust- ness for large-scale models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.401839Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 734dd0c1-8ae3-4277-9553-56e12181d32a · outbound

This paper cites Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T16:52:27.705166Z

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Observation 74803c23-95d0-424b-988a-2fbcaf4b4bbe · outbound

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Automated flower classification over a large number of classes

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T16:52:27.709031Z

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Observation 5cbe0811-ccd0-43d1-8bcc-a57544227ace · outbound

This paper cites Efficient test- 9 time model adaptation without forgetting.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Efficient test- 9 time model adaptation without forgetting

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.384878Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3a57427d-82db-4c47-bbc7-9c72bd868fa5 · outbound

This paper cites Cats and dogs.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Cats and dogs

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.374117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 22cdc8ba-4300-4ff0-b6e3-6315966857c4 · outbound

This paper cites On the rela- tionship between generalization and robustness to adversar- ial examples.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models On the rela- tionship between generalization and robustness to adversar- ial examples

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.363427Z

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Observation 43604da4-d9d7-4a04-8996-552f49b19418 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Learn- ing transferable visual models from natural language super- vision

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.352421Z

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Observation f548a372-ab7e-4317-999b-1e0273013abe · outbound

This paper cites Imagenet large scale visual recognition challenge.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Imagenet large scale visual recognition challenge

Reference 34

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Observation b6504b37-73b6-46fd-9f79-2056d1ff285d · outbound

This paper cites Robust CLIP: Unsupervised ad- versarial fine-tuning of vision embeddings for robust large vision-language models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Robust CLIP: Unsupervised ad- versarial fine-tuning of vision embeddings for robust large vision-language models

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.334837Z

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Observation d46bad2f-ee0e-4dee-8d29-f05fa39cb2e1 · outbound

This paper cites Improving robustness against common corruptions by covariate shift adaptation.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Improving robustness against common corruptions by covariate shift adaptation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.323880Z

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Observation 2e7e8c93-0ad0-4efb-a9c7-d2dd199e27eb · outbound

This paper cites The Cost of Training NLP Models: A Concise Overview.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models The Cost of Training NLP Models: A Concise Overview

Reference 37

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Observation 73432cff-c52a-4268-abfe-7a665328ef6f · outbound

This paper cites Cliport: What and where pathways for robotic manipulation.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Cliport: What and where pathways for robotic manipulation

Reference 38

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Observation c18ba89a-0142-4ea8-9d00-a3b4c0d57d9c · outbound

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Test- time prompt tuning for zero-shot generalization in vision- language models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.307010Z

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Observation 83f64589-886e-4de6-8068-7227558c7434 · outbound

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 40

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Observation 334da2de-a4af-4867-b6f3-d7a060623bf1 · outbound

This paper cites Disentan- gling adversarial robustness and generalization.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Disentan- gling adversarial robustness and generalization

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.296181Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f1640690-67b7-4b13-8c26-549577e77a2f · outbound

This paper cites Is robustness the cost of accuracy?– a comprehensive study on the robustness of 18 deep image classification models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Is robustness the cost of accuracy?– a comprehensive study on the robustness of 18 deep image classification models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.286114Z

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Observation 07564bcb-9fd9-4ca7-8d0b-933c46156d7d · outbound

This paper cites In- triguing properties of neural networks.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models In- triguing properties of neural networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.275668Z

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Observation 48bfb5d2-56e9-4f83-9a16-a975a45e32c1 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Gemini: A Family of Highly Capable Multimodal Models

Reference 44

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Observation 618d51b5-ad1a-4909-a400-6fb13dbb1db5 · outbound

This paper cites Tent: Fully test-time adaptation by entropy minimization.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Tent: Fully test-time adaptation by entropy minimization

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.264209Z

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Observation cf0df288-2cfd-4897-b477-f59e7b92bad2 · outbound

This paper cites Trans- ferable multimodal attack on vision-language pre-training models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Trans- ferable multimodal attack on vision-language pre-training models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.253088Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2d5725b6-8f42-461b-a86e-79dea12622ed · outbound

This paper cites Con- tinual test-time domain adaptation.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Con- tinual test-time domain adaptation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.240908Z

Source-reported events for the cited work

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Observation 2899b49b-91f6-4f65-bd82-ddeb0104ea37 · outbound

This paper cites Pre- trained model guided fine-tuning for zero-shot adversarial robustness.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Pre- trained model guided fine-tuning for zero-shot adversarial robustness

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.229558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f06f41c9-ff90-41e2-8a1d-6b9953080415 · outbound

This paper cites AdvQDet: Detecting query-based adversarial attacks with adversarial contrastive prompt tun- ing.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models AdvQDet: Detecting query-based adversarial attacks with adversarial contrastive prompt tun- ing

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.218578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 86fbb80f-6ce3-4d11-bcd3-d281eb6728d3 · outbound

This paper cites Medclip: Contrastive learning from unpaired medical images and text.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Medclip: Contrastive learning from unpaired medical images and text

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.205260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 117c918d-4b18-4815-89c3-eae9ef280c0f · outbound

This paper cites Re- visiting adversarial training at scale.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Re- visiting adversarial training at scale

Reference 51

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Observation 66b99564-25ac-4121-9163-a1c9f6bc6e50 · outbound

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Sun database: Large-scale scene recognition from abbey to zoo

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.181483Z

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Observation 87066be0-5bbd-4476-855d-100b52867a59 · outbound

This paper cites Improving transferabil- ity of adversarial examples with input diversity.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Improving transferabil- ity of adversarial examples with input diversity

Reference 53

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Observation 0ac438f1-2d45-48fe-8d75-4735f07aee6b · outbound

This paper cites Vlattack: Multimodal adversarial attacks on vision-language tasks via pre-trained models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Vlattack: Multimodal adversarial attacks on vision-language tasks via pre-trained models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.162796Z

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Observation 7cea113f-6024-46a9-a37e-e4887bd8ab9a · outbound

This paper cites Multi-event video-text retrieval.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Multi-event video-text retrieval

Reference 55

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Observation 5383218d-6c5f-4f8c-8ece-fb10d5f9bc6a · outbound

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TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Theoretically principled trade-off between robustness and accuracy

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.142508Z

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Observation 802eae0a-60d1-478d-8b34-e4e9474bbb6a · outbound

This paper cites Towards adversarial attack on vision-language pre-training models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Towards adversarial attack on vision-language pre-training models

Reference 57

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Observation 6b4c7f66-c410-4302-965e-e9ee826b538d · outbound

This paper cites Adversarial prompt tuning for vision-language models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Adversarial prompt tuning for vision-language models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.123706Z

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Observation 85d99722-1b83-4ef8-a023-84616393d9c7 · outbound

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Memo: Test time robustness via adaptation and augmentation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.112918Z

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Observation 5025364b-d97a-40f5-ba13-f1dd194f1ff8 · outbound

This paper cites Univer- sal adversarial perturbations for vision-language pre-trained models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Univer- sal adversarial perturbations for vision-language pre-trained models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.101759Z

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Observation ceaf71e2-0ea8-4b4e-b96c-804f7497a86a · outbound

This paper cites On evaluating adversarial robustness of large vision-language models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models On evaluating adversarial robustness of large vision-language models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.090659Z

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Observation a4ac5ca2-dc9a-45f2-9ed8-158319245599 · outbound

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Conditional prompt learning for vision-language models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.079185Z

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Observation 54589e70-1270-4b66-9757-a3dc569a3c6d · outbound

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Learning to prompt for vision-language models

Reference 63

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Observation 6f453f80-fa02-48b3-b641-d3932e2de760 · outbound

This paper cites Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T16:52:27.836524Z

Source-reported events for the cited work

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Observation 2364e03f-d124-460b-a650-99d3b036c607 · outbound

This paper cites Few-shot adversarial prompt learning on vision-language models.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Few-shot adversarial prompt learning on vision-language models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.060689Z

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Observation e3a2ccde-b8ee-493e-9b63-d4179845281a · outbound

This paper cites Advclip: Downstream-agnostic adversarial examples in multimodal contrastive learning.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Advclip: Downstream-agnostic adversarial examples in multimodal contrastive learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:28.049547Z

Source-reported events for the cited work

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