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Paper Citation Record · LEDGER

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation

As of 15 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 2 inbound Pith citation observations for arXiv:2506.07376.

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

pith.paper-citation-record.v1
2506.07376 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:41:47.242778Z

measured 25 of 25 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 2026-06-26T01:03:42.423515Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:09:56.183113Z

Reference resolution

23 of 23 outbound references displayed

  • verified exact2
  • verified fuzzy8
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8088668d-b5bb-4e65-8a0a-ec02e95544ac · outbound

This paper cites Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation

Reference 1

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Unavailable: canonical work link unavailable.

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Observation 47228dbd-a547-4452-afd6-de6359daeb77 · outbound

This paper cites Deep- globe 2018: A challenge to parse the earth through satel- lite images.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Deep- globe 2018: A challenge to parse the earth through satel- lite images

Reference 4

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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.

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Observation c10374e2-266a-425d-badc-9800263624e2 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation LoRA: Low-Rank Adaptation of Large Language Models

Reference 9

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Observation 16e39911-fac0-45cb-8d9a-f47f3e448fa7 · outbound

This paper cites Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks

Reference 13

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Observation eecd66e8-9d6b-4252-b7c2-ee305252f6d5 · outbound

This paper cites Normalization Layers Are All That Sharpness-Aware Minimization Needs.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Normalization Layers Are All That Sharpness-Aware Minimization Needs

Reference 14

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local_arxiv, observed 2026-08-07T05:41:47.553546Z

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.

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Observation 7f5ce249-9c68-40aa-8fee-56767eec9e16 · outbound

This paper cites One-Shot Learning for Semantic Segmentation.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation One-Shot Learning for Semantic Segmentation

Reference 15

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Unavailable: canonical work link unavailable.

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Observation ea53a251-bf00-413e-a382-61a467c9ab61 · outbound

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

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Prototype mixture models for few-shot semantic segmentation

Reference 16

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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.

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Observation 0a183056-903e-474c-a5d4-8900fe96afb0 · outbound

This paper cites Object-contextual rep- resentations for semantic segmentation.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Object-contextual rep- resentations for semantic segmentation

Reference 17

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raw_fallback, observed 2026-08-07T05:41:48.047606Z

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.

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Observation 80108416-15b6-4bf2-a9b2-fe8ff332d4da · outbound

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

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Compositional Few-Shot Class-Incremental Learning

Reference 18

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Observation 79b39742-83f5-4fa5-9821-7fc8e8880acd · outbound

This paper cites When domain shift occurs, the weights learned by the DFN become misaligned on the target domain.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation When domain shift occurs, the weights learned by the DFN become misaligned on the target domain

Reference 19

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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.

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Observation 803fdf6b-16fa-4554-b967-cb607d223631 · outbound

This paper cites We employ PASCAL- 5i as our source domain for training.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation We employ PASCAL- 5i as our source domain for training

Reference 21

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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.

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Observation 96ba35ba-6c33-4a62-a6a4-a76ede257aec · outbound

This paper cites As ground-truth labels are only provided in the training set, we rely on the official training dataset, consist- ing of 803 images, to present our results.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation As ground-truth labels are only provided in the training set, we rely on the official training dataset, consist- ing of 803 images, to present our results

Reference 22

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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.

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Observation ce68087a-4b38-4534-b437-deceb5188318 · outbound

This paper cites The dataset is processed and utilized in accordance with the standards set by PATNet.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation The dataset is processed and utilized in accordance with the standards set by PATNet

Reference 23

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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.

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Observation b0d9af93-4e42-41e3-849a-54e36c0229f9 · outbound

This paper cites Related Work Few-shot learningFew-shot learning focuses on developing robust representations for novel concepts with limited anno- tated samples (An et al., 2024a;b).

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Related Work Few-shot learningFew-shot learning focuses on developing robust representations for novel concepts with limited anno- tated samples (An et al., 2024a;b)

Reference 96

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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.

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Observation 7d7e57aa-b634-41f5-9ea9-2a2244cceb02 · outbound

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

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation C., Karlinsky, L., Codella, J

Reference 2012

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raw_fallback, observed 2026-08-07T05:41:48.125639Z

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.

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Observation ad4d173e-73e2-4034-8034-a347bfb736be · outbound

This paper cites Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Reference 2013

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Observation ab242fc6-0770-45e3-91a9-54ba7cbb1a45 · outbound

This paper cites Domain-invariant Feature Exploration for Domain Generalization.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Domain-invariant Feature Exploration for Domain Generalization

Reference 2015

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Observation d9637bb6-023f-4e22-9cce-ad2b97e41110 · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 2017

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Observation 5a5dc113-5af3-40e4-9bab-7192f57f3e45 · outbound

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

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2018

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Observation 6ada0db8-438a-4379-a63f-8f54175b366f · outbound

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

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 2019

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Unavailable: canonical work link unavailable.

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Observation e7441727-80c5-4014-900c-63925a8147fa · outbound

This paper cites Few-Shot Learning with Graph Neural Networks.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Few-Shot Learning with Graph Neural Networks

Reference 2020

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source=pdf_text observed=2026-08-07T05:41:47.103028Z digest=sha256:bca3df83e02ebfb6a60f44ade73e8f50d4f3d20e2cfcd860c22a6b54b3aab268

Observation 0278ee8f-76d2-4acf-a178-a6afec002254 · outbound

This paper cites RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network

Reference 2021

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Observation a5d75a10-45a0-4177-ab5d-a2f60cb3e70d · outbound

This paper cites Decoupling Representation and Classifier for Long-Tailed Recognition.

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation Decoupling Representation and Classifier for Long-Tailed Recognition

Reference 2022

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Unavailable: canonical work link unavailable.

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Pith citing papers

Observation 4b365a9a-8ffa-4d84-a146-385ecb193faa · 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 Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation

Reference 50

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arxiv_id, observed 2026-05-20T06:48:05.843824Z

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.

source=pdf_text observed=2026-05-20T06:45:35.591459Z digest=sha256:21f0ecf90624a822202fa37bea3745df9f32445532f17a091c322523370c6278

Observation cc6bc072-18b0-432f-944a-0288854f10e4 · 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 Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation

Reference 13

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arxiv_id, observed 2026-07-04T16:09:56.185017Z

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.

source=arxiv_source observed=2026-06-26T01:03:42.423515Z digest=sha256:61221ca80f5157285a88fc7d36464376c4e9cf10f4c6052571f0b6f3f7549348