Robust Adaptation of Foundation Models with Black-Box Visual Prompting | Paper Citation Record

Observation 9815c1da-3673-48b9-8982-5fc79a80a248 · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Learning transferable visual models from natural language supervision

Reference 1

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Observation d74773c1-663c-45b3-ba97-d0eef71bfc57 · outbound

This paper cites GPT-4 Technical Report.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting GPT-4 Technical Report

Reference 2

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This paper cites Visual instruction tuning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Visual instruction tuning

Reference 3

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Observation f0c70fed-e008-4798-b0ec-4b4d69dda79b · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 4

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Observation bbf15973-a76c-4ba2-acc2-9f8a241577bf · outbound

This paper cites Visual Prompt Tuning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Visual Prompt Tuning

Reference 5

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Observation fcea3cc7-99b8-40a1-b527-79e7f7f54ad0 · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Exploring Visual Prompts for Adapting Large-Scale Models

Reference 6

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Observation 8fceb714-42c4-4058-8963-a974af054534 · outbound

This paper cites Maple: Multi-modal prompt learning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Maple: Multi-modal prompt learning

Reference 7

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Observation 9c1f1cb3-90c8-4b7c-b569-792c0574a71a · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting An image is worth 16x16 words: Transformers for image recognition at scale

Reference 8

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Observation 6ddec918-2bed-4452-b567-9ca60a587345 · outbound

This paper cites Prompting Visual-Language Models for Efficient Video Understanding.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Prompting Visual-Language Models for Efficient Video Understanding

Reference 9

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Observation 686fa36c-baa9-4f52-88f4-ade643adf1af · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Learning to prompt for vision-language models

Reference 10

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Observation b6a382cb-4e0f-4b87-8e28-da966b1513fc · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Conditional prompt learning for vision-language models

Reference 11

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Observation 9b391797-2dda-4b4e-bf9b-be54cf8708e6 · outbound

This paper cites Unified Vision and Language Prompt Learning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Unified Vision and Language Prompt Learning

Reference 12

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Observation 3fb4d998-0e8a-479e-9c4d-64798ed0101f · outbound

This paper cites Multivariate stochastic approximation using a simultaneous perturbation gradient approximation.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Multivariate stochastic approximation using a simultaneous perturbation gradient approximation

Reference 13

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Observation e5d9fdb1-7fe6-4407-b67a-acbb3467d859 · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Blackvip: Black-box visual prompting for robust transfer learning

Reference 14

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Observation 3dd14387-60ef-4b7d-9b96-28b3eceae55d · outbound

This paper cites AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition

Reference 15

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Observation 54d83ce4-0a2a-4768-a0e0-26c6bc57140e · outbound

This paper cites Vision transformer adapter for dense predictions.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Vision transformer adapter for dense predictions

Reference 16

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Observation a2026741-05e0-45bf-b428-d6a4a0b0eb3f · outbound

This paper cites CLIP-Adapter: Better Vision-Language Models with Feature Adapters.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting CLIP-Adapter: Better Vision-Language Models with Feature Adapters

Reference 17

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Observation 8975f9b4-2c23-4a3e-b649-f7af4a2c10e4 · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Tip-adapter: Training-free adaption of clip for few-shot classification

Reference 18

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Observation f6acf763-a375-4d01-ad9b-d35c4d058dd7 · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Black box few-shot adaptation for vision-language models

Reference 19

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Observation 2ea1aace-7824-47e6-b7b7-caf6266cfd98 · outbound

This paper cites Contrastive adapters for foundation model group robustness.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Contrastive adapters for foundation model group robustness

Reference 20

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Observation 1afc1a40-68f8-437d-a9a9-53b8bf15fcbe · outbound

This paper cites Attention is all you need.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Attention is all you need

Reference 21

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Observation 843c7f2a-97d7-4dd7-a184-8c1453290a1b · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Learning to prompt for vision-language models

Reference 22

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Observation 6d577f7e-7ffa-4a1b-ac15-56bf00945434 · outbound

This paper cites Prompt-aligned gradient for prompt tuning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Prompt-aligned gradient for prompt tuning

Reference 23

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Observation 67ec0bac-6d7b-450a-98f4-4409915fd58f · outbound

This paper cites Prompt pre-training with twenty-thousand classes for open-vocabulary visual recognition.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Prompt pre-training with twenty-thousand classes for open-vocabulary visual recognition

Reference 24

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Observation 89bca49d-a246-4296-8733-2d3c7dcddb83 · outbound

This paper cites Diversity-aware meta visual prompting.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Diversity-aware meta visual prompting

Reference 25

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Observation 61a00a38-9881-4fc7-ac9d-f36098c6e5e9 · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Understanding and improving visual prompting: A label-mapping perspective

Reference 26

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This paper cites Fine-grained vi- sual prompting.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Fine-grained vi- sual prompting

Reference 27

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Observation 92b18e97-e59a-4f7d-b6c1-4004d6835150 · outbound

This paper cites Lst: Ladder side-tuning for parameter and memory efficient transfer learning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Lst: Ladder side-tuning for parameter and memory efficient transfer learning

Reference 28

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This paper cites Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning

Reference 29

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This paper cites Memory-efficient fine-tuning of compressed large language models via sub-4-bit integer quantization.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Memory-efficient fine-tuning of compressed large language models via sub-4-bit integer quantization

Reference 30

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This paper cites Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources

Reference 31

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This paper cites Black-box tuning for language-model-as-a-service.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Black-box tuning for language-model-as-a-service

Reference 32

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This paper cites Bbtv2: Towards a gradient-free future with large language models.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Bbtv2: Towards a gradient-free future with large language models

Reference 33

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This paper cites RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 34

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This paper cites Completely derandomized self- adaptation in evolution strategies.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Completely derandomized self- adaptation in evolution strategies

Reference 35

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This paper cites Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es).

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es)

Reference 36

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This paper cites A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications

Reference 37

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This paper cites Analysis and improve- ment of policy gradient estimation.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Analysis and improve- ment of policy gradient estimation

Reference 38

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This paper cites An overview of the simultaneous perturbation method for efficient optimization.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting An overview of the simultaneous perturbation method for efficient optimization

Reference 39

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This paper cites Adversarial Reprogramming of Neural Networks.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Adversarial Reprogramming of Neural Networks

Reference 40

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This paper cites Explaining and Harnessing Adversarial Examples.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Explaining and Harnessing Adversarial Examples

Reference 41

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This paper cites The limitations of deep learning in adversarial settings.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting The limitations of deep learning in adversarial settings

Reference 42

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This paper cites Adversarial attacks and defenses in images, graphs and text: A review.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Adversarial attacks and defenses in images, graphs and text: A review

Reference 43

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This paper cites Cross-modal adversarial reprogramming.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Cross-modal adversarial reprogramming

Reference 44

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This paper cites Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning

Reference 45

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This paper cites Reprogramming Pretrained Language Models for Antibody Sequence Infilling.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Reprogramming Pretrained Language Models for Antibody Sequence Infilling

Reference 46

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This paper cites Emerging properties in self-supervised vision trans- formers.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Emerging properties in self-supervised vision trans- formers

Reference 47

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This paper cites Masked autoencoders are scalable vision learners.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Masked autoencoders are scalable vision learners

Reference 48

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This paper cites Benchmarking Detection Transfer Learning with Vision Transformers.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Benchmarking Detection Transfer Learning with Vision Transformers

Reference 49

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This paper cites Self-supervised learning is more robust to dataset imbalance.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Self-supervised learning is more robust to dataset imbalance

Reference 50

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Observation 7fb3c18e-e634-4e14-a5df-1113a8721a1a · outbound

This paper cites A survey of self-supervised and few-shot object detection.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting A survey of self-supervised and few-shot object detection

Reference 51

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Observation f17dfead-8750-4ebd-ba22-6bf660efe8de · outbound

This paper cites Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection

Reference 52

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This paper cites Towards Understanding Why Mask-Reconstruction Pretraining Helps in Downstream Tasks.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Towards Understanding Why Mask-Reconstruction Pretraining Helps in Downstream Tasks

Reference 53

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Observation bc83ef0f-e791-4fc9-9315-866ac3ad8d5b · outbound

This paper cites Pytorch image models.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Pytorch image models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.331122Z

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Observation 55a918da-24dd-48ee-aeb6-5567a51fd89a · outbound

This paper cites What is being transferred in transfer learning?.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting What is being transferred in transfer learning?

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.336015Z

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Observation b8f9c86d-fe49-417c-9984-e2bfdecb626c · outbound

This paper cites A one-measurement form of simultaneous perturbation stochastic approximation.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting A one-measurement form of simultaneous perturbation stochastic approximation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.418783Z

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Observation 6ce489e0-d09d-4464-a5e4-534a37657a2e · outbound

This paper cites Spall, Introduction to Stochastic Search and Optimization , 1st ed.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Spall, Introduction to Stochastic Search and Optimization , 1st ed

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.302357Z

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Observation a1f8be91-ddf2-4184-8caa-bdde63c51a9d · outbound

This paper cites Robust neural network tracking controller using simultaneous perturbation stochastic approx- imation.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Robust neural network tracking controller using simultaneous perturbation stochastic approx- imation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.183434Z

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Observation 5702ff42-a43f-41b2-b1ef-755fdacd3156 · outbound

This paper cites Simultaneous per- turbation stochastic approximation for automatic speech recognition.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Simultaneous per- turbation stochastic approximation for automatic speech recognition

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.340486Z

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Observation 1a6134ab-a290-4538-ad9f-3dade083d4d5 · outbound

This paper cites Simultaneous perturba- tion stochastic approximation for few-shot learning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Simultaneous perturba- tion stochastic approximation for few-shot learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.410897Z

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Observation fab18edc-554f-4b69-abc2-4a5520642650 · outbound

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Robust Adaptation of Foundation Models with Black-Box Visual Prompting Adaptive stochastic approximation by the simultaneous per- turbation method

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.415022Z

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Observation 941e5ea5-a005-4fe0-80e8-e9c7bf5126ab · outbound

This paper cites Accelerated second-order stochastic optimization using only function measurements.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Accelerated second-order stochastic optimization using only function measurements

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.551340Z

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Observation e69d561d-0884-4097-8c0b-bab401cb7564 · outbound

This paper cites A method for solving the convex programming problem with convergence rate o(1/k2).

Robust Adaptation of Foundation Models with Black-Box Visual Prompting A method for solving the convex programming problem with convergence rate o(1/k2)

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.446629Z

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Observation 9b086fe3-6d0a-4001-aff4-752a9446a20d · outbound

This paper cites On the importance of initialization and momentum in deep learning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting On the importance of initialization and momentum in deep learning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.573226Z

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Observation 23ebb284-3fd4-4e91-8661-ce49c84aea9b · outbound

This paper cites Stochastic first-and zeroth-order methods for nonconvex stochastic programming.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Stochastic first-and zeroth-order methods for nonconvex stochastic programming

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.276963Z

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Observation b729ccdb-1017-4c29-a87b-75db6b91d5f5 · outbound

This paper cites Generative pretraining for black-box optimization.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Generative pretraining for black-box optimization

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.375212Z

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Observation 4cedf1ef-ed7e-43ed-9f00-0f5142be0191 · outbound

This paper cites ZIP: An efficient zeroth-order prompt tuning for black-box vision-language models.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting ZIP: An efficient zeroth-order prompt tuning for black-box vision-language models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.287815Z

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Observation cb0678b9-8b81-410e-9f29-04674ff87a1d · outbound

This paper cites Wilds: A benchmark of in-the-wild distribution shifts.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Wilds: A benchmark of in-the-wild distribution shifts

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.344948Z

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Observation 21f72f50-1faa-4b89-b9af-f3f9cb793c2a · outbound

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Robust Adaptation of Foundation Models with Black-Box Visual Prompting Zeroth-order stochastic variance reduction for nonconvex optimiza- tion

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.384085Z

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Observation d995fe9e-885f-43b2-a66f-d97a4eb89bb6 · outbound

This paper cites Learning de- biased representations with biased representations.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Learning de- biased representations with biased representations

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.391478Z

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Observation 28820b6c-2bb2-4035-bc1a-410165830337 · outbound

This paper cites Gradient-based learning applied to document recognition.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Gradient-based learning applied to document recognition

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.296644Z

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Observation 3677a953-2584-4110-ad47-feb1479b6083 · outbound

This paper cites De- scribing textures in the wild.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting De- scribing textures in the wild

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.268531Z

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Observation 18bea94e-1515-4305-b8d7-8f1b96bb5c42 · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.292051Z

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Observation 70479be9-330b-4c58-ae73-8f86979c310a · outbound

This paper cites Remote sensing image scene classifi- cation: Benchmark and state of the art.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Remote sensing image scene classifi- cation: Benchmark and state of the art

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.388021Z

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Observation 2409e1c4-fdb5-49bb-ac62-689f130c6985 · outbound

This paper cites Clevr: A diagnostic dataset for compositional language and elementary visual reasoning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Clevr: A diagnostic dataset for compositional language and elementary visual reasoning

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.370501Z

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Observation 644bb049-e29b-4792-bee3-e6d3fdcdca3c · outbound

This paper cites The intrinsic dimension of images and its impact on learning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting The intrinsic dimension of images and its impact on learning

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.218810Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b409c6e4-4d9b-4f95-b257-85e5f4acea49 · outbound

This paper cites Hastie, R.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Hastie, R

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.223178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5d46610e-f69b-4605-91f5-4d11511d72bb · outbound

This paper cites What does a platypus look like? generating customized prompts for zero-shot image classifi- cation.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting What does a platypus look like? generating customized prompts for zero-shot image classifi- cation

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.227669Z

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Observation b4b4fcbb-3adc-4a32-ac01-51331d704539 · outbound

This paper cites Language models as black-box optimizers for vision-language models.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Language models as black-box optimizers for vision-language models

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.423514Z

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Observation 457b0b16-cfa1-4cfe-a7bf-3d58c31d9ba1 · outbound

This paper cites Maximum likelihood estimation of intrinsic dimension.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Maximum likelihood estimation of intrinsic dimension

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.379084Z

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Observation 93e34142-1fd0-4971-b861-b7bf54980145 · outbound

This paper cites Available: https://proceedings.neurips.cc/paper files/ paper/2004/file/74934548253bcab8490ebd74afed7031-Paper.pdf.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Available: https://proceedings.neurips.cc/paper files/ paper/2004/file/74934548253bcab8490ebd74afed7031-Paper.pdf

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:23:36.287746Z

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Observation 53131d08-34a9-461b-887b-ef2f3c9d0355 · outbound

This paper cites Comments on ‘Maximum Likelihood Estimation of Intrinsic Dimension’ by e. levine and p. bickel (2004).

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Comments on ‘Maximum Likelihood Estimation of Intrinsic Dimension’ by e. levine and p. bickel (2004)

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.560331Z

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Observation c7cdd9e3-01e8-434c-b048-88d47968316b · outbound

This paper cites Scaling Laws for Neural Language Models.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Scaling Laws for Neural Language Models

Reference 83

Resolution: verified exact; local_arxiv, observed 2026-05-23T23:23:36.355388Z

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Observation cc7138fb-c30d-45d9-912e-284a81d180f7 · outbound

This paper cites Certified adversarial robustness via randomized smoothing.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Certified adversarial robustness via randomized smoothing

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.577556Z

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Observation 07fa19d8-af0d-4f05-8372-636b2e17f231 · outbound

This paper cites Certified Adversarial Robustness via Anisotropic Randomized Smoothing.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Certified Adversarial Robustness via Anisotropic Randomized Smoothing

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:23:36.281836Z

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Observation 6aa559ac-2b3d-49a0-a24e-1123fab897e0 · outbound

This paper cites Stochastic gradient de- scent as approximate bayesian inference.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Stochastic gradient de- scent as approximate bayesian inference

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.356500Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e623c773-63eb-4a0b-a277-d0e3e217c252 · outbound

This paper cites Revisiting the Characteristics of Stochastic Gradient Noise and Dynamics.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Revisiting the Characteristics of Stochastic Gradient Noise and Dynamics

Reference 87

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:23:36.294137Z

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Observation 1ca147f0-cca4-4edd-b39d-32caa956e96c · outbound

This paper cites an unresolved cited work.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Unresolved cited work

Reference 88

Resolution: unresolved; raw_fallback, observed 2026-05-23T23:23:37.259582Z

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Observation 43e9ebc4-2d6d-419d-a330-3706b29c9153 · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.361295Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 953f7e64-95ee-4e5c-8aaf-ff152fa41c5b · outbound

This paper cites Cats and dogs.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Cats and dogs

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.503972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6b1057ba-5e3a-4d34-bb29-ed310509e159 · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting 3d object representations for fine-grained categorization

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.201289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b3791c61-c56b-4fdd-8a09-1e0dcae5337d · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Automated flower classification over a large number of classes

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.206108Z

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Observation 9e880a7c-41b7-4217-83e4-d67c3231bd89 · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Food-101 – mining discriminative components with random forests

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.232001Z

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Observation 8ab4cab1-22ea-4228-a8c6-d3a947ce971f · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Fine-Grained Visual Classification of Aircraft

Reference 94

Resolution: verified exact; local_arxiv, observed 2026-05-23T23:23:36.430630Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d4217095-444d-4d77-bd06-77a3e8912530 · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Sun database: Large-scale scene recognition from abbey to zoo

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.395122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7ab46bbd-f8fa-4d9f-b388-f60d0b56b55a · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Reading digits in natural images with unsupervised feature learning

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.586206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9fbb50c2-fc7a-48cd-865f-30025b23ea79 · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 97

Resolution: verified exact; local_arxiv, observed 2026-05-23T23:23:36.380865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8c20cd0d-24fe-4bbc-ab74-caf37025e46d · outbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Imagenet: A large-scale hierarchical image database

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.263913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6f96f102-791c-4738-88ae-dbbf6052d1b2 · outbound

This paper cites From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.403387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e15da1e3-92d2-41b9-9103-b28c2c178182 · outbound

This paper cites Functional map of the world.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Functional map of the world

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T23:23:37.517651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Outbound references

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