Paper Citation Record · LEDGER

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning

As of 15 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2506.03110.

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

pith.paper-citation-record.v1
2506.03110 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:14:04.914055Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:46:52.751334Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T22:34:01.801877Z

Reference resolution

47 of 47 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0860f862-eeef-480d-9465-25cd51d83215 · outbound

This paper cites write newline.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning write newline

Reference 1

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Source-reported events for the cited work

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Observation d191090f-1d31-497b-ae3c-4a2dd1581e98 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Emerging properties in self-supervised vision transformers

Reference 2

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Observation 123fc64d-6d3e-465b-8aed-5bd4702b5b68 · outbound

This paper cites Amplitude-phase recombination: Rethinking robustness of convolutional neural networks in frequency domain.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Amplitude-phase recombination: Rethinking robustness of convolutional neural networks in frequency domain

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:11.601781Z

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Observation e2edecaa-8f24-4629-a487-e9938c556fa6 · outbound

This paper cites Accumulated trivial attention matters in vision transformers on small datasets.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Accumulated trivial attention matters in vision transformers on small datasets

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:11.380505Z

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Observation 2849dda0-aaaa-4426-8844-c9eb5ed6b04a · outbound

This paper cites Conditional Positional Encodings for Vision Transformers.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Conditional Positional Encodings for Vision Transformers

Reference 5

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Observation 8eb8dd11-50dc-417b-a53e-d9790ec8acd5 · outbound

This paper cites E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., Kittler, H., and Halpern, A.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., Kittler, H., and Halpern, A

Reference 6

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Observation cc91f5c8-fea9-4462-a407-7b0b5a90b27e · outbound

This paper cites Confess: A framework for single source cross-domain few-shot learning.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Confess: A framework for single source cross-domain few-shot learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:10.863802Z

Source-reported events for the cited work

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Observation 7adbb38e-a31d-4e41-9248-80da1e4c09d0 · outbound

This paper cites Reliability of cka as a similarity measure in deep learning, 2022.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Reliability of cka as a similarity measure in deep learning, 2022

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:10.577446Z

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Observation 4d3ff255-fec7-4641-a7a0-cfe3b20254af · outbound

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

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Imagenet: A large-scale hierarchical image database

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:10.379710Z

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Observation 16c84ea4-c069-4c9b-b4af-46be9a9eab8f · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning An image is worth 16x16 words: Transformers for image recognition at scale, 2021

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:10.168407Z

Source-reported events for the cited work

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Observation 5dbf6ee6-5962-46db-a561-eb068747a896 · outbound

This paper cites Meta-fdmixup: Cross-domain few-shot learning guided by labeled target data.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Meta-fdmixup: Cross-domain few-shot learning guided by labeled target data

Reference 11

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Observation b685869a-e9a8-4c43-943b-46a4eed2f752 · outbound

This paper cites Wave-san: Wavelet based style augmentation network for cross-domain few-shot learning, 2022.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Wave-san: Wavelet based style augmentation network for cross-domain few-shot learning, 2022

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:09.881208Z

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Observation a09d69f1-7b5d-4411-a96f-ffae9a84e346 · outbound

This paper cites Styleadv: Meta style adversarial training for cross-domain few-shot learning, 2023.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Styleadv: Meta style adversarial training for cross-domain few-shot learning, 2023

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:09.695574Z

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Observation bcbdc8ef-d8d5-4de8-a7be-63e8b116691b · outbound

This paper cites C., Karlinsky, L., Codella, J.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning C., Karlinsky, L., Codella, J

Reference 14

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Observation 1e0b14c0-993a-43f4-b8e7-c9f60bbc22e7 · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification, 2019.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification, 2019

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:09.353746Z

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Observation 737670e3-f099-4130-b144-d60ee50ac77e · outbound

This paper cites and Ma, A.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning and Ma, A

Reference 16

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Observation e426340d-4f26-4be8-81db-efee1f2d1916 · outbound

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Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Unresolved cited work

Reference 17

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Observation dfcf6ed4-ca14-41d9-9862-9a0c4b391f85 · outbound

This paper cites Similarity of neural network representations revisited.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Similarity of neural network representations revisited

Reference 18

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Observation 0fb84ce5-517c-46b8-9c55-b52e31de97a0 · outbound

This paper cites Ranking distance calibration for cross-domain few-shot learning, 2022.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Ranking distance calibration for cross-domain few-shot learning, 2022

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:08.818912Z

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Observation 72551a7f-4b15-4621-a6f3-f6413fbfab76 · outbound

This paper cites Revisiting local descriptor based image-to-class measure for few-shot learning.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Revisiting local descriptor based image-to-class measure for few-shot learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:08.637703Z

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Observation fc1d5fe7-f0d0-4be3-bd0e-e13f81c71fc3 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Swin transformer: Hierarchical vision transformer using shifted windows

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:08.485410Z

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Observation 4103f902-4876-496a-b02c-ec632b8d8aac · outbound

This paper cites Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning

Reference 22

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Observation 487f9b91-7811-45d7-8670-b57c0587a5da · outbound

This paper cites Using deep learning for image-based plant disease detection.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Using deep learning for image-based plant disease detection

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:14:01.280197Z

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Observation cb16ba89-d9b7-47ec-979b-a6126f1cc226 · outbound

This paper cites M., Ranasinghe, K., Khan, S.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning M., Ranasinghe, K., Khan, S

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:08.294497Z

Source-reported events for the cited work

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Observation bf493897-6362-4b68-b02b-0665f55117e3 · outbound

This paper cites Understanding cross-domain few-shot learning based on domain similarity and few-shot difficulty, 2022.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Understanding cross-domain few-shot learning based on domain similarity and few-shot difficulty, 2022

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:08.098351Z

Source-reported events for the cited work

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Observation 7bc17d4d-4b77-49ec-a2a4-cfb09eaaf001 · outbound

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Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Unresolved cited work

Reference 26

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Observation 4069ce9a-d697-4ec2-9e0e-d301111fc7d4 · outbound

This paper cites Rapid learning or feature reuse? towards understanding the effectiveness of maml.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Rapid learning or feature reuse? towards understanding the effectiveness of maml

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:07.715562Z

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Observation ff8aaa9a-5ae4-4e83-986b-7726149db78c · outbound

This paper cites Espt: A self-supervised episodic spatial pretext task for improving few-shot learning.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Espt: A self-supervised episodic spatial pretext task for improving few-shot learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:07.574170Z

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Observation 8b516666-5ba9-4668-85e2-21092bea6676 · outbound

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Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Unresolved cited work

Reference 29

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Observation 3c1e52b3-e47e-4f8d-8b1e-7b8771bbab9f · outbound

This paper cites Explanation-guided training for cross-domain few-shot classification.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Explanation-guided training for cross-domain few-shot classification

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:07.331898Z

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Observation 9becccc7-cca4-472a-be94-ef96ab61e1fb · outbound

This paper cites Cross-domain few-shot classification via learned feature-wise transformation.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Cross-domain few-shot classification via learned feature-wise transformation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:07.180650Z

Source-reported events for the cited work

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Observation 606d3ddd-64c1-4c82-a765-d0d03a2dc397 · outbound

This paper cites Matching networks for one shot learning.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Matching networks for one shot learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:07.029903Z

Source-reported events for the cited work

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Observation ba7937aa-41ee-4d6a-8696-c136875664b7 · outbound

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Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Unresolved cited work

Reference 33

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Observation e19058e9-48dd-4f60-9356-61926dbbd8a3 · outbound

This paper cites and Deng, Z.-H.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning and Deng, Z.-H

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:06.888606Z

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Observation 5f86bed0-d96d-46d4-985c-7ea8e0d6ddf9 · outbound

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Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Unresolved cited work

Reference 35

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Observation 54dc1d4a-3907-4afb-bca4-33eeb89c5b99 · outbound

This paper cites Few-shot classification with feature map reconstruction networks.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Few-shot classification with feature map reconstruction networks

Reference 36

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Observation 345f0b68-ecef-4887-aaa4-88820053242b · outbound

This paper cites Tinyvit: Fast pretraining distillation for small vision transformers.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Tinyvit: Fast pretraining distillation for small vision transformers

Reference 37

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Observation 71dfc9f0-87a5-4d54-825f-8d5e7c983bf2 · outbound

This paper cites Enhancing information maximization with distance-aware contrastive learning for source-free cross-domain few-shot learning.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Enhancing information maximization with distance-aware contrastive learning for source-free cross-domain few-shot learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:06.739123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 912a66f6-0e71-47a0-b394-0166043ffa4a · outbound

This paper cites E., Feng, J., and Yan, S.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning E., Feng, J., and Yan, S

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:06.603541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c6d1bab9-f129-4d1b-a56d-de5b30f74e88 · outbound

This paper cites M., and Shum, H.-Y.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning M., and Shum, H.-Y

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:06.447542Z

Source-reported events for the cited work

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Observation 71675eb8-9e2d-4399-aa4a-e925299b9b11 · outbound

This paper cites Metagan: An adversarial approach to few-shot learning.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Metagan: An adversarial approach to few-shot learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:06.268996Z

Source-reported events for the cited work

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Observation 4c32348f-0556-4f1c-8741-45a46d2401a5 · outbound

This paper cites Revisiting prototypical network for cross domain few-shot learning.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Revisiting prototypical network for cross domain few-shot learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:06.093314Z

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Observation 95de0e5f-1660-48e9-954f-7aefcb2109b5 · outbound

This paper cites Attention temperature matters in vit-based cross-domain few-shot learning.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Attention temperature matters in vit-based cross-domain few-shot learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:05.970004Z

Source-reported events for the cited work

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Observation 46f2d802-816c-4452-8d1d-352a81651c13 · outbound

This paper cites A closer look at the cls token for cross-domain few-shot learning.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning A closer look at the cls token for cross-domain few-shot learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:05.810113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b34a78b2-9506-4c68-a23e-54463a6eda0b · outbound

This paper cites Margin-based few-shot class-incremental learning with class-level overfitting mitigation.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Margin-based few-shot class-incremental learning with class-level overfitting mitigation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:05.685603Z

Source-reported events for the cited work

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Observation 064d89a4-6ae5-4747-8a10-0023e66c7232 · outbound

This paper cites Flatten long-range loss landscapes for cross-domain few-shot learning, 2024 a.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Flatten long-range loss landscapes for cross-domain few-shot learning, 2024 a

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:14:05.536256Z

Source-reported events for the cited work

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Observation 526577dc-7206-4960-bc74-bf96dfeea23d · outbound

This paper cites Compositional Few-Shot Class-Incremental Learning.

Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning Compositional Few-Shot Class-Incremental Learning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T11:14:04.914055Z

Source-reported events for the cited work

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Pith citing papers

Observation 9d71c73f-52cc-4771-9d5b-779f9f98ae9c · inbound

Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning cites this paper.

Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:34:01.804650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fe4567aa-a676-45b9-a593-720937a78c42 · inbound

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning cites this paper.

When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:52.751334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.