Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting | Paper Citation Record

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This paper cites write newline.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting write newline

Reference 1

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Observation e26b359a-d5cd-4228-ba4f-18e6ed417266 · outbound

This paper cites NoCaps : novel object captioning at scale.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting NoCaps : novel object captioning at scale

Reference 2

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Observation 59a78959-9507-4218-8715-280c3792382a · outbound

This paper cites and Fusi, N.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting and Fusi, N

Reference 3

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Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Unresolved cited work

Reference 4

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Observation 9706094f-a387-48aa-b5ca-b6dd472ef362 · outbound

This paper cites Darkrank: Accelerating deep metric learning via cross sample similarities transfer.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Darkrank: Accelerating deep metric learning via cross sample similarities transfer

Reference 5

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Observation da4731e9-bb8f-4176-8088-a52f583e2280 · outbound

This paper cites Remote sensing image scene classification: Benchmark and state of the art.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Remote sensing image scene classification: Benchmark and state of the art

Reference 6

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Observation 1c842028-ecf3-48e3-a09b-578885e414a7 · outbound

This paper cites Describing textures in the wild.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Describing textures in the wild

Reference 7

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Observation 287b4fdd-5a90-48ad-84b7-8a2b74d8b9ec · outbound

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

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting ImageNet : A large-scale hierarchical image database

Reference 8

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Observation edda84b2-0223-468d-8e1d-265e19828077 · outbound

This paper cites Vos: Learning what you don’t know by virtual outlier synthesis.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Vos: Learning what you don’t know by virtual outlier synthesis

Reference 9

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Observation f564ee2f-4b87-4122-932e-b41492ce3be7 · outbound

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

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

Reference 10

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Observation 8d07c900-70c3-42d7-ae87-1fd1be7b6793 · outbound

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

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting CLIP-Adapter: Better Vision-Language Models with Feature Adapters

Reference 11

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Observation 3649c51c-b44c-4803-9ce3-59d7132165be · outbound

This paper cites Finetune like you pretrain: Improved finetuning of zero-shot vision models.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Finetune like you pretrain: Improved finetuning of zero-shot vision models

Reference 12

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Observation f530cfb5-4791-42e9-a993-a07ee4b85eff · outbound

This paper cites Making the V in VQA matter: Elevating the role of image understanding in V isual Q uestion A nswering.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Making the V in VQA matter: Elevating the role of image understanding in V isual Q uestion A nswering

Reference 13

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Observation e98895a3-fda8-466f-b0fb-f1c7557db779 · outbound

This paper cites Deep residual learning for image recognition.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Deep residual learning for image recognition

Reference 14

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This paper cites Masked autoencoders are scalable vision learners.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Masked autoencoders are scalable vision learners

Reference 15

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Observation aab42691-fb53-423f-9988-42988b7bd30d · outbound

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

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 16

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Observation 29228a95-5cf8-47e0-93f5-a78ede28b23a · outbound

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

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 17

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Observation 9baa2ed2-ce54-49b2-b4d9-db885f0835d5 · outbound

This paper cites Natural adversarial examples.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Natural adversarial examples

Reference 18

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Observation 1cbd5155-b54e-4b65-9588-4d8eef17e74a · outbound

This paper cites CLIPS core: A reference-free evaluation metric for image captioning.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting CLIPS core: A reference-free evaluation metric for image captioning

Reference 19

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Observation 4ff2fcd3-11bb-4502-a282-4969a7a1e4d3 · outbound

This paper cites Lifelong learning via progressive distillation and retrospection.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Lifelong learning via progressive distillation and retrospection

Reference 20

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Observation 36cfe6dd-9872-4046-a288-9847b1d7a6c6 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 21

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Observation 662814a8-a3b5-4c4c-a14c-c060923a4c75 · outbound

This paper cites Knowledge distillation from a stronger teacher.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Knowledge distillation from a stronger teacher

Reference 22

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Observation d20aaef1-d8f3-449c-a02b-9885cefefb7e · outbound

This paper cites Visual prompt tuning.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Visual prompt tuning

Reference 23

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Observation 2c9737ca-b72e-4ec6-a686-db36d4d94155 · outbound

This paper cites Less-forgetting Learning in Deep Neural Networks.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Less-forgetting Learning in Deep Neural Networks

Reference 24

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This paper cites U., Rasheed, H., Maaz, M., Khan, S., and Khan, F.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting U., Rasheed, H., Maaz, M., Khan, S., and Khan, F

Reference 25

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Observation ab0b4819-8ff5-4176-a351-6e55f20c7600 · outbound

This paper cites U., Wasim, S.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting U., Wasim, S

Reference 26

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Observation f3be455e-7f9c-420e-bd4e-cf8364c5effb · outbound

This paper cites Learning to Prompt with Text Only Supervision for Vision-Language Models.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Learning to Prompt with Text Only Supervision for Vision-Language Models

Reference 27

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Observation e08f47b9-847b-4fa4-aee4-d41762181810 · outbound

This paper cites Proxy anchor loss for deep metric learning.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Proxy anchor loss for deep metric learning

Reference 28

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

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting 3d object representations for fine-grained categorization

Reference 29

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

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Learning multiple layers of features from tiny images

Reference 30

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This paper cites M., Ma, T., and Liang, P.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting M., Ma, T., and Liang, P

Reference 31

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This paper cites Modeling Caption Diversity in Contrastive Vision-Language Pretraining.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Modeling Caption Diversity in Contrastive Vision-Language Pretraining

Reference 32

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Observation adca173d-3a32-4336-9662-494e871f9b79 · outbound

This paper cites Mnist handwritten digit database.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Mnist handwritten digit database

Reference 33

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Observation dfbdc072-f5ca-41e3-a789-bd5876a21bba · outbound

This paper cites Explicit inductive bias for transfer learning with convolutional networks.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Explicit inductive bias for transfer learning with convolutional networks

Reference 34

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Observation ef7af593-18fd-4f1d-bdee-c3ef20d81d32 · outbound

This paper cites and Hoiem, D.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting and Hoiem, D

Reference 35

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This paper cites J., Hays, J., Perona, P., Ramanan, D., Doll \' a r, P., and Zitnick, C.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting J., Hays, J., Perona, P., Ramanan, D., Doll \' a r, P., and Zitnick, C

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:42:00.509922Z

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Observation 657cfea5-8f6d-45e1-8866-73bdbd1287a6 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Fine-Grained Visual Classification of Aircraft

Reference 37

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Observation d7ecdc7c-d3d6-4337-ba99-a1c8f41c20f1 · outbound

This paper cites Linearly mapping from image to text space.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Linearly mapping from image to text space

Reference 38

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Observation 7fdc9862-36e8-4fa0-b1bd-ef6d00f506d6 · outbound

This paper cites Information theoretic representation distillation.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Information theoretic representation distillation

Reference 39

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Observation 3093b431-6783-4466-af36-116e7e275100 · outbound

This paper cites No fuss distance metric learning using proxies.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting No fuss distance metric learning using proxies

Reference 40

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Observation d1730945-ae97-4d4b-b124-85e9ec007942 · outbound

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Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Unresolved cited work

Reference 41

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Observation 599ffcac-260a-448a-8e8a-09d72c029058 · outbound

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Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Unresolved cited work

Reference 42

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Observation 04202a3d-6449-4eae-a80a-4252b4e889b6 · outbound

This paper cites and Zisserman, A.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting and Zisserman, A

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:59.518541Z

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Observation 3679e935-770d-4c1b-ae60-07c69959ae6b · outbound

This paper cites an unresolved cited work.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Unresolved cited work

Reference 44

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Observation 251cf7ca-dcec-402b-9ffb-1e76733e8326 · outbound

This paper cites Relational knowledge distillation.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Relational knowledge distillation

Reference 45

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Observation 344b25b4-95f1-4323-adf2-57f9ea63b484 · outbound

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

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting M., Vedaldi, A., Zisserman, A., and Jawahar, C

Reference 46

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Source-reported events for the cited work

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Observation 60d69cbc-8f63-408a-ad80-7a55809f94c9 · outbound

This paper cites and Tefas, A.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting and Tefas, A

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:58.951058Z

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Observation be9071a5-f7de-4049-9c25-ed8f0d94da70 · outbound

This paper cites Correlation congruence for knowledge distillation.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Correlation congruence for knowledge distillation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:58.770510Z

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 a5fb7565-607c-465e-b4ca-cba42247d2b8 · outbound

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

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 49

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This paper cites an unresolved cited work.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:41:58.637734Z

Source-reported events for the cited work

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Observation d3345421-bbc4-49ed-91c2-2c2c31a468f5 · outbound

This paper cites Do imagenet classifiers generalize to imagenet? In ICML, 2019.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Do imagenet classifiers generalize to imagenet? In ICML, 2019

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:58.414665Z

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 01a0441d-53ce-48c5-ae0c-ff1b4ae29673 · outbound

This paper cites Non-isotropy regularization for proxy-based deep metric learning.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Non-isotropy regularization for proxy-based deep metric learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:58.221049Z

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 1d5fd000-e4f4-49bb-bc9b-381a80002435 · outbound

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

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting CLIPood : Generalizing clip to out-of-distributions

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:58.044270Z

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 6039e370-22cf-4357-a1b4-2f89ea8a85ee · outbound

This paper cites FLAVA: A foundational language and vision alignment model.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting FLAVA: A foundational language and vision alignment model

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:57.861916Z

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 de530fe4-fe0b-46dd-8d97-2439a3e60b97 · outbound

This paper cites S., Karlinsky, L., Gutta, V., Cascante-Bonilla, P., Kim, D., Arbelle, A., Panda, R., Feris, R., and Kira, Z.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting S., Karlinsky, L., Gutta, V., Cascante-Bonilla, P., Kim, D., Arbelle, A., Panda, R., Feris, R., and Kira, Z

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:57.724372Z

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 f84a5fa9-f141-43e2-80e4-861adfd68ed4 · outbound

This paper cites FD -align: Feature discrimination alignment for fine-tuning pre-trained models in few-shot learning.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting FD -align: Feature discrimination alignment for fine-tuning pre-trained models in few-shot learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:57.549684Z

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 ddc517f0-173c-4089-b488-c9aaafb08463 · outbound

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

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T12:41:51.589050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3981e3a-db23-48e3-b8b3-18caf38ed1f2 · outbound

This paper cites an unresolved cited work.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:41:57.391763Z

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 47212be3-be2f-4104-bc70-404b9161a9a5 · outbound

This paper cites Non-parametric outlier synthesis.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Non-parametric outlier synthesis

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:57.199023Z

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 3f9e5698-fcfd-40e3-8ce8-461a82a9e039 · outbound

This paper cites CLIP model is an Efficient Continual Learner.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting CLIP model is an Efficient Continual Learner

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T12:41:51.815702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c62e1d3a-654a-4000-a51c-c8982f3c62fe · outbound

This paper cites ArGue: Attribute-Guided Prompt Tuning for Vision-Language Models.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting ArGue: Attribute-Guided Prompt Tuning for Vision-Language Models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:57.045200Z

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 40affa11-8ae4-4d0a-b6ca-550aca9b2a12 · outbound

This paper cites and Mori, G.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting and Mori, G

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:56.915231Z

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 644126fd-2bc6-443a-a2ae-0e26c39e8124 · outbound

This paper cites L., and Parikh, D.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting L., and Parikh, D

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:56.762656Z

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 6a1ddff8-da58-4e40-b8c1-bf3dfbe66a14 · outbound

This paper cites Manifold mixup: Better representations by interpolating hidden states.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Manifold mixup: Better representations by interpolating hidden states

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:56.626410Z

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 bef5fa0e-d92f-44fd-b462-b8df8cb29532 · outbound

This paper cites Optimal Transport: Old and New.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Optimal Transport: Old and New

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:56.495417Z

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 5b00de19-7245-41c5-8fac-2fa6ef3a1896 · outbound

This paper cites an unresolved cited work.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:41:56.345681Z

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 2988dbf2-c690-4cc9-bfd4-4ebbce0013c2 · outbound

This paper cites and Yoon, K.-J.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting and Yoon, K.-J

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:56.222064Z

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 9373467f-adf7-4533-8e74-c7910fbcd572 · outbound

This paper cites and Deng, W.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting and Deng, W

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:56.111803Z

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 1abe1d14-c4be-42a4-969f-08ffb360abf0 · outbound

This paper cites Improving Knowledge Distillation via Regularizing Feature Norm and Direction.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Improving Knowledge Distillation via Regularizing Feature Norm and Direction

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T12:41:52.606322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df462856-7b63-4efc-b5ea-5068b2c2b3ad · outbound

This paper cites Dualprompt: Complementary prompting for rehearsal-free continual learning.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:55.950392Z

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 6c6d41a8-e817-47a1-a2b2-b5c298642bab · outbound

This paper cites Learning to prompt for continual learning.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Learning to prompt for continual learning

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:55.848238Z

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 0f7524ad-1279-4b45-9347-8bc07203b8b9 · outbound

This paper cites Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:55.726627Z

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 5de5bc78-18a9-473a-8522-ef05b454a130 · outbound

This paper cites W., Li, M., Kornblith, S., Roelofs, R., Gontijo-Lopes, R., Hajishirzi, H., Farhadi, A., Namkoong, H., and Schmidt, L.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting W., Li, M., Kornblith, S., Roelofs, R., Gontijo-Lopes, R., Hajishirzi, H., Farhadi, A., Namkoong, H., and Schmidt, L

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:55.568758Z

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 ef289692-2eac-49c6-9228-8fcb9324d3de · outbound

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

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting A., Oliva, A., and Torralba, A

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T12:41:53.026389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcca67cb-9e05-4f84-acd5-77bd72fcf7bd · outbound

This paper cites M., and Huang, C.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting M., and Huang, C

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:55.448801Z

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 cb8f620e-7d6a-4613-a69b-0d674b69d1de · outbound

This paper cites Sigmoid loss for language image pre-training.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Sigmoid loss for language image pre-training

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T12:41:53.183293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 508e9817-55a8-45b5-b63d-ee3c9755d83d · outbound

This paper cites SLCA : Slow learner with classifier alignment for continual learning on a pre-trained model.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting SLCA : Slow learner with classifier alignment for continual learning on a pre-trained model

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:55.351924Z

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 9a7279ed-cce0-4dca-991e-85d7a28092f1 · outbound

This paper cites and Yang, E.-H.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting and Yang, E.-H

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:55.082699Z

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 44924050-f8ec-44a7-a12e-bae137b4cc5e · outbound

This paper cites Preventing zero-shot transfer degradation in continual learning of vision-language models.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Preventing zero-shot transfer degradation in continual learning of vision-language models

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:54.749373Z

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 dd70107c-b63b-465c-bb1e-6e4c6337eefa · outbound

This paper cites C., and Liu, Z.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting C., and Liu, Z

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:54.428982Z

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 237f0cd8-4f33-4b18-bcaf-852dc5cc3eb3 · outbound

This paper cites C., and Liu, Z.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting C., and Liu, Z

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:41:54.291395Z

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 0eb7d09e-16ba-4b63-9244-414c8bf639aa · outbound

This paper cites Contrastive Neighborhood Alignment.

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting Contrastive Neighborhood Alignment

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:41:53.947141Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers