Paper Citation Record · LEDGER

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation

As of 7 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 4 inbound Pith citation observations for arXiv:2506.02677.

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

pith.paper-citation-record.v1
2506.02677 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:24:21.130265Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:21:33.687395Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:09:56.167161Z

Reference resolution

53 of 53 outbound references displayed

verified exact1
verified fuzzy34
unresolved18
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fb2e0e26-af15-4eb4-808a-744f460f81c8 · outbound

This paper cites I., Piantanida, P., Ben Ayed, I., and Dolz, J.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation I., Piantanida, P., Ben Ayed, I., and Dolz, J

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:27.452730Z

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Observation ac7b7076-9687-4e64-92f2-4ddaaefb9774 · outbound

This paper cites P., Singh, R.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation P., Singh, R

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:27.266516Z

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Observation 51204491-2696-4535-b023-231582a16ba9 · outbound

This paper cites MMFuser: Multimodal Multi-Layer Feature Fuser for Fine-Grained Vision-Language Understanding.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation MMFuser: Multimodal Multi-Layer Feature Fuser for Fine-Grained Vision-Language Understanding

Reference 3

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Observation fa24f820-8e1b-4bb5-a323-c8f439bb2165 · outbound

This paper cites F., and Huang, J.-B.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation F., and Huang, J.-B

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:27.091964Z

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Observation eddcd4f5-d78e-4fbd-bed2-22bc26afcd4c · outbound

This paper cites Infogan: Interpretable representation learning by information maximizing generative adversarial nets.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Infogan: Interpretable representation learning by information maximizing generative adversarial nets

Reference 5

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Source-reported events for the cited work

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Observation 2d53bf81-3e95-4ab9-b577-f0987a0a59d8 · outbound

This paper cites C., and Wen, B.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation C., and Wen, B

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:26.894620Z

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Observation 4607e00b-d3a8-4848-98d5-0b1c990fcacb · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T11:24:17.309537Z

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Observation d1bb7133-e81b-40dc-994a-e296d8f3e991 · outbound

This paper cites Deepglobe 2018: A challenge to parse the earth through satellite images.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Deepglobe 2018: A challenge to parse the earth through satellite images

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:26.724782Z

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Observation ea4a4174-864d-45be-8555-c0551fb1b95f · outbound

This paper cites and Xing, E.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation and Xing, E

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:26.552247Z

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Observation a660cbb6-0deb-418b-ab31-ef93cb358988 · outbound

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

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:24:17.539653Z

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Observation 9bf2cbba-afd8-47b2-84eb-74aa6956e3ac · outbound

This paper cites K., Winn, J., and Zisserman, A.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation K., Winn, J., and Zisserman, A

Reference 11

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Observation 39cc3189-eafb-45af-b6d8-69fea060de70 · outbound

This paper cites Self-support few-shot semantic segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Self-support few-shot semantic segmentation

Reference 12

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Observation fab884e6-b382-45bf-94e7-f1ad0f7e590f · outbound

This paper cites A., and Steinhardt, J.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation A., and Steinhardt, J

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:26.156442Z

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Observation 0040c06f-bf72-4dff-a4bb-5c1849ce9455 · outbound

This paper cites Semantic contours from inverse detectors.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Semantic contours from inverse detectors

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:25.998243Z

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Observation b5895e66-808a-43b6-a407-eded288cbfe2 · outbound

This paper cites Apseg: Auto-prompt network for cross-domain few-shot semantic segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Apseg: Auto-prompt network for cross-domain few-shot semantic segmentation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:25.836887Z

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Observation 7f18b958-e070-4595-b672-40bf9e348735 · outbound

This paper cites Adapt before comparison: A new perspective on cross-domain few-shot segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Adapt before comparison: A new perspective on cross-domain few-shot segmentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:25.598438Z

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Observation 4c443e37-3ed4-45e8-a3cf-756aae67bbbd · outbound

This paper cites K., Antani, S., et al.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation K., Antani, S., et al

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:25.367251Z

Source-reported events for the cited work

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Observation a09c183e-fd37-4d20-aec5-5682008bc023 · outbound

This paper cites C., Lo, W.-Y., et al.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation C., Lo, W.-Y., et al

Reference 18

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Observation 83d42586-86ac-4873-a7f5-686c46f130e3 · outbound

This paper cites Similarity of neural network representations revisited.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Similarity of neural network representations revisited

Reference 19

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Observation 4011728c-1350-4226-a0e4-98bb5f28f336 · outbound

This paper cites Cross-domain few-shot semantic segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Cross-domain few-shot semantic segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:25.164484Z

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Observation d7c4d1d0-9c4b-44ba-a5b2-fe3db8bedd62 · outbound

This paper cites Adaptive prototype learning and allocation for few-shot segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Adaptive prototype learning and allocation for few-shot segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:25.060183Z

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Observation ff0e9d87-b74c-43a1-8cbb-a56da745288e · outbound

This paper cites P., Tai, Y.-W., and Tang, C.-K.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation P., Tai, Y.-W., and Tang, C.-K

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:24.879901Z

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Observation 5de6d913-9861-44b4-a42d-bb8fdb3295dd · outbound

This paper cites Generalized zero-shot learning via disentangled representation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Generalized zero-shot learning via disentangled representation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:24.750401Z

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Observation a5c277e0-b13f-43ee-ac44-c80db3a0b33e · outbound

This paper cites Feature pyramid networks for object detection.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Feature pyramid networks for object detection

Reference 24

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Observation 4a743b10-ad01-4290-901b-487649ba72eb · outbound

This paper cites The Devil is in Low-Level Features for Cross-Domain Few-Shot Segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation The Devil is in Low-Level Features for Cross-Domain Few-Shot Segmentation

Reference 25

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Observation 9d4732e6-2bbd-4155-8898-8a9315a9fe14 · outbound

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

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 26

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Observation 6de02152-f582-4a2c-8bfc-a5854aafcc26 · outbound

This paper cites Object-centric learning with slot attention.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Object-centric learning with slot attention

Reference 27

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Observation e00f44ec-ec54-43de-825d-9434bb53c9e8 · outbound

This paper cites Fully convolutional networks for semantic segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Fully convolutional networks for semantic segmentation

Reference 28

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Observation b97cc460-e679-4e8c-a374-de0a13df3053 · outbound

This paper cites Hypercorrelation squeeze for few-shot segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Hypercorrelation squeeze for few-shot segmentation

Reference 29

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This paper cites C., and Lu, S.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation C., and Lu, S

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:24.042811Z

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Observation c46b2e39-5c11-4a52-adb1-7fff530516d5 · outbound

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Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation and Kim, S

Reference 31

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Observation bda96597-4287-4c2f-a33c-3c07e72889ec · outbound

This paper cites Imagenet large scale visual recognition challenge.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Imagenet large scale visual recognition challenge

Reference 32

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Observation 8b0dcb98-956f-49db-bbd7-1d48a384240a · outbound

This paper cites Bridging the Gap to Real-World Object-Centric Learning.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Bridging the Gap to Real-World Object-Centric Learning

Reference 33

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Observation 289eb451-c8c6-4748-abc3-57c5dade09b4 · outbound

This paper cites One-Shot Learning for Semantic Segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation One-Shot Learning for Semantic Segmentation

Reference 34

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Observation 8361ebda-006a-4c4f-82b1-6406461cc9c7 · outbound

This paper cites Prototypical networks for few-shot learning.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Prototypical networks for few-shot learning

Reference 35

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Observation 7cd9478a-3269-4e1e-99f0-4bb438c06fb1 · outbound

This paper cites Domain-rectifying adapter for cross-domain few-shot segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Domain-rectifying adapter for cross-domain few-shot segmentation

Reference 36

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Observation 776369e5-3586-4728-ad2c-0d9b90aef593 · outbound

This paper cites Prior guided feature enrichment network for few-shot segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Prior guided feature enrichment network for few-shot segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:23.502147Z

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Observation d10924fc-e5e0-4ab9-a497-94d024b41e60 · outbound

This paper cites Lightweight frequency masker for cross-domain few-shot semantic segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Lightweight frequency masker for cross-domain few-shot semantic segmentation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:23.300705Z

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Observation b0c4a297-32bf-4ec8-ac95-9e82082cb039 · outbound

This paper cites The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

Reference 39

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Observation 52e8e1a5-b370-4440-ba8e-edcadb60c1c2 · outbound

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Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Matching networks for one shot learning

Reference 40

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Observation 4b3cb234-9833-4ffd-86fb-dc63248f52f9 · outbound

This paper cites H., Zou, Y., Zhou, D., and Feng, J.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation H., Zou, Y., Zhou, D., and Feng, J

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:23.100706Z

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Observation 3c2f9ecf-2674-472f-bf26-90971f93f248 · outbound

This paper cites All you need is beyond a good init: Exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation All you need is beyond a good init: Exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:22.838138Z

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Observation a5328841-f6b2-42bf-9550-9f42c2d55817 · outbound

This paper cites Prototype mixture models for few-shot semantic segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Prototype mixture models for few-shot semantic segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:22.582807Z

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Observation 62379dfa-dcdf-4431-b5f4-5c8e0a5154af · outbound

This paper cites Prototype mixture models for few-shot semantic segmentation.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Prototype mixture models for few-shot semantic segmentation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:22.324687Z

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Observation c6a7e4b0-21e5-469f-be6c-8656ab9ce487 · outbound

This paper cites Deepemd: Few-shot image classification with differentiable earth mover's distance and structured classifiers.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Deepemd: Few-shot image classification with differentiable earth mover's distance and structured classifiers

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:22.223543Z

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Observation 77ee5bf5-9563-45a2-9c4b-ea921580f53b · outbound

This paper cites Personalize segment anything model with one shot.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Personalize segment anything model with one shot

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:22.088848Z

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Observation 1a3877dc-45ad-4874-8e2c-750c01c2975c · outbound

This paper cites an unresolved cited work.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:24:21.959933Z

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Observation 74ed9c7f-bc90-4956-b1e0-d04528f57ddb · outbound

This paper cites Pyramid scene parsing network.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Pyramid scene parsing network

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:21.832574Z

Source-reported events for the cited work

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Observation 7b6ed115-581a-43bc-a642-39bbd24d06a6 · outbound

This paper cites Multi-modal large language model enhanced pseudo 3d perception framework for visual commonsense reasoning.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Multi-modal large language model enhanced pseudo 3d perception framework for visual commonsense reasoning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:21.669376Z

Source-reported events for the cited work

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Observation 6ebd074d-4f97-4cb7-b668-11f8ff94604a · outbound

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

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Margin-based few-shot class-incremental learning with class-level overfitting mitigation

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T11:24:20.978209Z

Source-reported events for the cited work

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Observation 5e3531fa-1e31-43ce-aed3-715a09dafbe7 · outbound

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

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Flatten long-range loss landscapes for cross-domain few-shot learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:21.543063Z

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Observation ea9c6c1d-f42c-4682-85d5-225ea47ff7fd · outbound

This paper cites Compositional few-shot class-incremental learning.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation Compositional few-shot class-incremental learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:21.435513Z

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Observation 1b8a31f4-63bd-405f-81d2-3a307da0f98c · outbound

This paper cites write newline.

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation write newline

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T11:24:21.130265Z

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Pith citing papers

Observation 7847456d-3bbd-450f-9c9b-6fa6ebf57c22 · inbound

Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation cites this paper.

Cross-Domain Few-Shot Segmentation via Multi-view Progressive Adaptation Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:25.106198Z

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Observation b27c444d-e178-40f7-8e8b-3d7ea25d92a7 · inbound

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation cites this paper.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-20T06:48:05.866531Z

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Observation b1671688-cd56-418b-b9bd-c395cf104fbc · inbound

Hierarchical Spatial and Channel Aggregation for Cross-domain Few-shot Segmentation cites this paper.

Hierarchical Spatial and Channel Aggregation for Cross-domain Few-shot Segmentation Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T16:09:56.169230Z

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Observation c805a8b5-3871-4a37-9f79-faaf4d7ac764 · inbound

The First EgoCross Challenge at EgoVis 2026: Cross-Domain Egocentric Video Question Answering cites this paper.

The First EgoCross Challenge at EgoVis 2026: Cross-Domain Egocentric Video Question Answering Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T21:21:33.687395Z

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