FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models | Paper Citation Record

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This paper cites Minimization of functions having lipschitz continuous first partial derivatives.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Minimization of functions having lipschitz continuous first partial derivatives

Reference 1

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This paper cites D., and Li, Y.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models D., and Li, Y

Reference 2

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This paper cites Diprompt: Disentangled prompt tuning for multiple latent domain generalization in federated learning.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Diprompt: Disentangled prompt tuning for multiple latent domain generalization in federated learning

Reference 3

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Id-like prompt learning for few-shot out-of-distribution detection

Reference 4

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Food-101--mining discriminative components with random forests

Reference 5

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This paper cites A Unified Wasserstein Distributional Robustness Framework for Adversarial Training.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models A Unified Wasserstein Distributional Robustness Framework for Adversarial Training

Reference 6

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This paper cites and Chao, W.-L.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models and Chao, W.-L

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Describing textures in the wild

Reference 8

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This paper cites Describing textures in the wild.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Describing textures in the wild

Reference 9

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Unresolved cited work

Reference 10

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This paper cites Harmonizing generalization and personalization in federated prompt learning.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Harmonizing generalization and personalization in federated prompt learning

Reference 11

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This paper cites Q., Li, A., and Kung, H.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Q., Li, A., and Kung, H

Reference 12

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

Reference 14

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This paper cites Wordnet: An electronic lexical database.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Wordnet: An electronic lexical database

Reference 15

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This paper cites Learning federated visual prompt in null space for mri reconstruction.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Learning federated visual prompt in null space for mri reconstruction

Reference 16

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This paper cites Geodesic flow kernel for unsupervised domain adaptation.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Geodesic flow kernel for unsupervised domain adaptation

Reference 17

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Observation 42a2ccc1-90e1-465c-a71f-a03ea7bde502 · outbound

This paper cites Pfedprompt: Learning personalized prompt for vision-language models in federated learning.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Pfedprompt: Learning personalized prompt for vision-language models in federated learning

Reference 18

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This paper cites Promptfl: Let federated participants cooperatively learn prompts instead of models-federated learning in age of foundation model.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Promptfl: Let federated participants cooperatively learn prompts instead of models-federated learning in age of foundation model

Reference 19

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This paper cites Out-of-distribution generalization of federated learning via implicit invariant relationships.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Out-of-distribution generalization of federated learning via implicit invariant relationships

Reference 20

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Deep residual learning for image recognition

Reference 21

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This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

Reference 22

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models and Gimpel, K

Reference 23

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Federated learning for generalization, robustness, fairness: A survey and benchmark

Reference 24

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Revisiting frank-wolfe: Projection-free sparse convex optimization

Reference 25

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

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Scaling up visual and vision-language representation learning with noisy text supervision

Reference 26

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models and Lin, T

Reference 27

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This paper cites Negative label guided ood detection with pretrained vision-language models.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Negative label guided ood detection with pretrained vision-language models

Reference 28

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models P., Kale, S., Mohri, M., Reddi, S., Stich, S., and Suresh, A

Reference 29

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Learning multiple layers of features from tiny images

Reference 30

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

Reference 31

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Gallop: Learning global and local prompts for vision-language models

Reference 32

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models and Yang, X

Reference 33

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Unresolved cited work

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Global and local prompts cooperation via optimal transport for federated learning

Reference 35

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Model-contrastive federated learning

Reference 36

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V

Reference 37

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models and Wang, J

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Hyperfed: hyperbolic prototypes exploration with consistent aggregation for non-iid data in federated learning

Reference 39

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Rethinking the representation in federated unsupervised learning with non-iid data

Reference 40

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space

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This paper cites Reducing item discrepancy via differentially private robust embedding alignment for privacy-preserving cross domain recommendation.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Reducing item discrepancy via differentially private robust embedding alignment for privacy-preserving cross domain recommendation

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Hyperbolic variational graph auto-encoder for next poi recommendation

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?

Reference 46

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Unresolved cited work

Reference 47

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This paper cites Delving into out-of-distribution detection with vision-language representations.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Delving into out-of-distribution detection with vision-language representations

Reference 48

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This paper cites Locoop: Few-shot out-of-distribution detection via prompt learning.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Locoop: Few-shot out-of-distribution detection via prompt learning

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This paper cites T., Torr, P., and Lim, S.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models T., Torr, P., and Lim, S

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This paper cites T., Torr, P., and Lim, S.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models T., Torr, P., and Lim, S

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This paper cites and Zisserman, A.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models and Zisserman, A

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.597315Z

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This paper cites Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:51:03.045958Z

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Observation c53066ee-cc70-4d1f-a9bd-cbecf1608126 · outbound

This paper cites M., Vedaldi, A., Zisserman, A., and Jawahar, C.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models M., Vedaldi, A., Zisserman, A., and Jawahar, C

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.580620Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5b6f1111-7041-48b0-9754-d12bde906ea4 · outbound

This paper cites Moment matching for multi-source domain adaptation.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Moment matching for multi-source domain adaptation

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T23:50:59.555609Z

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Observation 4eb8bb89-1013-4ac9-893a-ab4e17126512 · outbound

This paper cites Counterfactual user sequence synthesis augmented with continuous time dynamic preference modeling for sequential poi recommendation.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Counterfactual user sequence synthesis augmented with continuous time dynamic preference modeling for sequential poi recommendation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.554885Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dcb18ad4-3eb9-4627-b7d0-de77cc79b829 · outbound

This paper cites K., Ganesh, M.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models K., Ganesh, M

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.538659Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 663354db-1747-4db8-a7c7-3f5f14e36e1d · outbound

This paper cites Generalized federated learning via sharpness aware minimization.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Generalized federated learning via sharpness aware minimization

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.522034Z

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Observation 22cb6fd0-1cab-48d0-b0ed-924ee769b24c · outbound

This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 59

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Observation 0afdd6ff-10fa-4fdb-b799-1d9dc4404c1e · outbound

This paper cites Clip-guided federated learning on heterogeneity and long-tailed data.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Clip-guided federated learning on heterogeneity and long-tailed data

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.494432Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f9bcc6af-2503-480e-8c5e-bd694b3c8213 · outbound

This paper cites Clipood: Generalizing clip to out-of-distributions.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Clipood: Generalizing clip to out-of-distributions

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.476796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 872eb3c9-cdd6-46f7-824d-762c067ef757 · outbound

This paper cites Certifying Some Distributional Robustness with Principled Adversarial Training.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T23:51:00.164072Z

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Observation 05a4c352-cbe2-4a6e-a2e6-21af2699ef5f · outbound

This paper cites Is heterogeneity notorious? taming heterogeneity to handle test-time shift in federated learning.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Is heterogeneity notorious? taming heterogeneity to handle test-time shift in federated learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.459460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 46119f15-c77d-4a18-97e4-a5d95122949a · outbound

This paper cites Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:51:02.768886Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a61a198e-8306-45a8-8133-3dc2c494b691 · outbound

This paper cites The inaturalist species classification and detection dataset.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models The inaturalist species classification and detection dataset

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T23:51:00.409863Z

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Observation f5de8ccc-7195-44ef-85d2-96da07ab0b3e · outbound

This paper cites Ce-rcfr: Robust counterfactual regression for consensus-enabled treatment effect estimation.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Ce-rcfr: Robust counterfactual regression for consensus-enabled treatment effect estimation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.433425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 25180944-d4ba-4713-88da-7aad3616d9b2 · outbound

This paper cites Inter- and intra- similarity preserved counterfactual incentive effect estimation for recommendation systems.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Inter- and intra- similarity preserved counterfactual incentive effect estimation for recommendation systems

Reference 67

Resolution: verified exact; doi, observed 2026-08-06T23:51:02.558361Z

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Observation d4f596d5-bb6f-4574-b22a-68182409cc35 · outbound

This paper cites Clipn for zero-shot ood detection: Teaching clip to say no.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Clipn for zero-shot ood detection: Teaching clip to say no

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.417613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e44d9660-25b5-4255-a8dd-716b6900a4fd · outbound

This paper cites Outlier-robust distributionally robust optimization via unbalanced optimal transport.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Outlier-robust distributionally robust optimization via unbalanced optimal transport

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.403224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 24bb3fb1-7282-4f2c-a026-808458687c55 · outbound

This paper cites Flora: Federated fine-tuning large language models with heterogeneous low-rank adaptations.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Flora: Federated fine-tuning large language models with heterogeneous low-rank adaptations

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.389233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1779131-5287-433b-861d-c679af725e04 · outbound

This paper cites M., and Johansson, K.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models M., and Johansson, K

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.376362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 996b2fd2-45b9-4c03-8d7e-f06ef1b45a93 · outbound

This paper cites T., Weng, L., and Hoang, N.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models T., Weng, L., and Hoang, N

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.362512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 52db1717-a8af-4eff-ab1e-ca06de7c3e8c · outbound

This paper cites A., Oliva, A., and Torralba, A.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models A., Oliva, A., and Torralba, A

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.349060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e6ca2677-266a-4bbe-9c41-d3b97945e9aa · outbound

This paper cites TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T23:51:01.286253Z

Source-reported events for the cited work

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Observation 57100172-6ae3-49e5-b8f6-a58bdc880e18 · outbound

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FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:51:05.334947Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bcf695bf-bd53-43d2-99a6-984ad731c735 · outbound

This paper cites Generalized out-of-distribution detection: A survey.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Generalized out-of-distribution detection: A survey

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.320610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 10722bf6-da3e-464c-8915-5fb603f9bd12 · outbound

This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T23:51:01.575628Z

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Observation 26a74b9b-4a53-4ecb-a243-193d019fe40d · outbound

This paper cites Turning the curse of heterogeneity in federated learning into a blessing for out-of-distribution detection.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Turning the curse of heterogeneity in federated learning into a blessing for out-of-distribution detection

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.305083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6f13315e-6220-42da-8c9a-1fe09ce46cc6 · outbound

This paper cites R., Ning, L., and Singhal, K.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models R., Ning, L., and Singhal, K

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:05.148685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4476d16e-48e6-4b6c-a6de-d4e8a715fd8b · outbound

This paper cites Rethinking misalignment in vision-language model adaptation from a causal perspective.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Rethinking misalignment in vision-language model adaptation from a causal perspective

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:04.877223Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 82f7351e-3e90-45ff-8db2-09ee2a519e87 · outbound

This paper cites Lapt: Label-driven automated prompt tuning for ood detection with vision-language models.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Lapt: Label-driven automated prompt tuning for ood detection with vision-language models

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:04.682241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 441c22f1-1ee1-4b93-9f0e-c20d2c602e72 · outbound

This paper cites Places: A 10 million image database for scene recognition.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Places: A 10 million image database for scene recognition

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:04.481413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 45be89c3-f153-4f52-9086-ff7a2d14c013 · outbound

This paper cites C., and Liu, Z.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models C., and Liu, Z

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:04.303313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5b429523-f8bd-4dde-9999-05ae9b1c86d0 · outbound

This paper cites Fedgog: Federated graph out-of-distribution generalization with diffusion data exploration and latent embedding decorrelation.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Fedgog: Federated graph out-of-distribution generalization with diffusion data exploration and latent embedding decorrelation

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:04.118934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1d583dac-bd1d-4bc4-a1b2-8729f57f4b4f · outbound

This paper cites Fedgf: Enhancing structural knowledge via graph factorization for federated graph learning.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Fedgf: Enhancing structural knowledge via graph factorization for federated graph learning

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:51:03.900568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d6b37b5a-4371-48f7-90b5-b3d3d07db450 · outbound

This paper cites write newline.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models write newline

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T23:51:02.376943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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