Pith. sign in

Paper Citation Record · LEDGER

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

As of 8 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 2026-08-08T06:32:00.761636+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

Resolution
unresolved
no resolver link, observed 2026-08-07T05:41:47.047720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:47.047720Z digest=sha256:d89b515146649946f0119621f24793f92ddcef09662ff4c9e5445e95ee6fc814

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:41:48.167092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:41:47.073432Z digest=sha256:e5b245e5612dca5110722d7870ed23498f53c684ce474c6c083f3cf1584135ae

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

Resolution
unresolved
no resolver link, observed 2026-08-07T05:41:47.122399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:47.122399Z digest=sha256:6142d7a970f84f5d2ec9ea8cdd49f651f717e5e7850c1e0c8090d92ef427b943

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

Resolution
unresolved
no resolver link, observed 2026-08-07T05:41:47.164466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:47.164466Z digest=sha256:214e61af9df05ea0616c564c72b9e1b157298b317f2079b1c84d9b6cdb95fc5b

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

Resolution
verified exact
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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:41:47.170601Z digest=sha256:4ee466c604a5e2c1d742a16b4a1ea9fd67966983e8f57bcc89a7f126e7d06c3c

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

Resolution
unresolved
no resolver link, observed 2026-08-07T05:41:47.177651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:47.177651Z digest=sha256:3e2855545b9c9de5d2760e12e889024d4c61521a4e63e29d63921e4839ec4e6f

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:41:48.083893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:41:47.186114Z digest=sha256:220e99b193893d89068377b2a985334d2bd618f2cd6c421e2187efa08101da97

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

Resolution
verified fuzzy
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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:41:47.192642Z digest=sha256:1321f6a7799e010975c55863cdba7b49c3aaf528aafc02c455e0dbe91783aff8

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

Resolution
unresolved
no resolver link, observed 2026-08-07T05:41:47.201523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:47.201523Z digest=sha256:ee2a5fe835bce7434c236c1891181f002f66198c0f9a55a78eadba20406213ef

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

Resolution
verified exact
raw_fallback, observed 2026-08-07T05:41:47.443019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:41:47.209068Z digest=sha256:09433518d37b0943da94c0ae930fc8c5f91dae32eb112e73212832039186d364

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:41:47.982492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:41:47.224451Z digest=sha256:24b26b74d72811155780144373b7d906c44c9afaa188de86206284d3252d9f9c

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:41:47.959603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:41:47.232051Z digest=sha256:ee58b826ada922b5d5159a070d6b681a18585606779867beb9037552ac1965a0

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:41:47.928668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:41:47.242778Z digest=sha256:5ec729cd9c7e5dbc75397fb297e4cec0cfc4a497058670d1c02edef56d6243d7

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:41:48.015763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:41:47.217543Z digest=sha256:6032565c7b3ce888cfeaf9ac47876f882ab2ee4fbd8598eb85eee49e9022eee8

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

Resolution
verified fuzzy
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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:41:47.114324Z digest=sha256:197dc21d5b46c667baeda3375780f9ef19bd61b975976601f53af684b09cf0f2

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

Resolution
unresolved
no resolver link, observed 2026-08-07T05:41:47.058047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:47.058047Z digest=sha256:a27deabe704c2b3d2b4db6568acf4dbcf539cf33ad1579ca9a320ffdbcc7fd29

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

Resolution
unresolved
no resolver link, observed 2026-08-07T05:41:47.155941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:47.155941Z digest=sha256:15330df4d9ef91193f326733b00919af09a4ee30e930a085210e659f956be01e

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

Resolution
unresolved
no resolver link, observed 2026-08-07T05:41:47.093374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:47.093374Z digest=sha256:872378aa227f74c97c2bd84baa726693e918ca3d75000b20cd80ff3c56815822

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

Resolution
unresolved
no resolver link, observed 2026-08-07T05:41:47.084498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:47.084498Z digest=sha256:bd571170f3c78c5d098b4e1ff1477d150ccdb20105048d101121528b32c0d34a

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

Resolution
unresolved
no resolver link, observed 2026-08-07T05:41:47.065310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:47.065310Z digest=sha256:d15c58b7a984317e6781a27f04985ecde09e4b06a39a1aff5ca7a29b6182199c

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

Resolution
unresolved
no resolver link, observed 2026-08-07T05:41:47.103028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:47.103028Z digest=sha256:301b1bbf7f174f54fcda2b5ed974ce99b8f2492a2e487631e309b7e116e48205

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

Resolution
unresolved
no resolver link, observed 2026-08-07T05:41:47.133044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:47.133044Z digest=sha256:4a6ba4e96626156db9e10eefc8a915a2a8f896a21d5acd754864a9437f521994

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

Resolution
unresolved
no resolver link, observed 2026-08-07T05:41:47.141499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:47.141499Z digest=sha256:19cafa40d9a7aced36a305e675ebcf0bf473f3c7f2c30f8e2b51f095c3675443

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

Resolution
verified exact
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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-20T06:45:35.591459Z digest=sha256:a30868b509a4a5d068e451d018b69668c9dbc7f37848d6e4bf92d50f0bf70366

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

Resolution
metadata mismatch
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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-26T01:03:42.423515Z digest=sha256:fe74c40698b22b2b875910a92c03e68bf3c80f95681b5635be49e4f5c675f887