Paper Citation Record · LEDGER

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation

As of 19 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2507.11994.

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

pith.paper-citation-record.v1
2507.11994 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:01:03.751176Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

verified exact1
verified fuzzy22
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bfedefd9-6cd7-42ab-b71b-c0bd7d41d5fc · outbound

This paper cites Weak-to-strong consistency learning for semisuper- vised image segmentation,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Weak-to-strong consistency learning for semisuper- vised image segmentation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:08.039786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2c4e6bfc-92f0-4d01-8a90-ec1e2538bf5e · outbound

This paper cites Classhyper: Classmix-based hybrid perturbations for deep semi-supervised semantic segmentation of remote sensing imagery,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Classhyper: Classmix-based hybrid perturbations for deep semi-supervised semantic segmentation of remote sensing imagery,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:07.888717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bf97f1c1-8b8c-41a5-8884-836ecc42be8a · outbound

This paper cites Semisupervised semantic segmentation of remote sensing images with consistency self- training,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Semisupervised semantic segmentation of remote sensing images with consistency self- training,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:07.752652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 96898513-2607-4587-b092-57f0aa7c55fb · outbound

This paper cites Picoco: Pixelwise contrast and consistency learning for semisupervised building footprint segmentation,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Picoco: Pixelwise contrast and consistency learning for semisupervised building footprint segmentation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:07.555949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2ad82a67-b85c-4481-9e0d-f943e9ba1bfb · outbound

This paper cites Virtual adversarial training: A regularization method for supervised and semi-supervised learning,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Virtual adversarial training: A regularization method for supervised and semi-supervised learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:07.390159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7f9a8203-477a-4532-b3e0-10a6ba18541b · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi- supervised deep learning results,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Mean teachers are better role models: Weight-averaged consistency targets improve semi- supervised deep learning results,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:07.206307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5fd3a512-eb7f-467d-818c-15cd1b4a55fd · outbound

This paper cites Dynamic and adaptive self-training for semi-supervised remote sensing image semantic segmentation,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Dynamic and adaptive self-training for semi-supervised remote sensing image semantic segmentation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:07.060407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 157d48b1-5e16-4ed3-84df-a142c8c13810 · outbound

This paper cites Region-aware contrastive learning for semi-supervised semantic segmentation of remote sensing images,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Region-aware contrastive learning for semi-supervised semantic segmentation of remote sensing images,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:06.835803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 29024b44-a99e-46cf-904b-ac547899f0b9 · outbound

This paper cites Semi- supervised remote sensing image semantic segmentation method based on deep learning,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Semi- supervised remote sensing image semantic segmentation method based on deep learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:06.639460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ee52540d-21c5-4da7-a6ca-59af970dfc01 · outbound

This paper cites Semi- supervised semantic segmentation of remote sensing images based on dual cross-entropy consistency,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Semi- supervised semantic segmentation of remote sensing images based on dual cross-entropy consistency,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:06.457853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f37ad517-ac26-428a-914b-687999fa3f70 · outbound

This paper cites St++: Make self- training work better for semi-supervised semantic segmenta- tion,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation St++: Make self- training work better for semi-supervised semantic segmenta- tion,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:06.259481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 251de21b-a85e-41cb-a024-4ad14ca5bd69 · outbound

This paper cites Simple and efficient: A semisupervised learning framework for remote sensing image semantic segmentation,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Simple and efficient: A semisupervised learning framework for remote sensing image semantic segmentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:06.083528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 588e3bf7-56be-4b4f-bb5e-49f49e4c1538 · outbound

This paper cites Semisupervised semantic segmentation of remote sensing images with consistency self- training,.

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:05.924217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 16bb4660-aefa-4dbb-813b-34bfb1f51832 · outbound

This paper cites Semi-supervised hyperspectral image classification via spatial- regulated self-training,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Semi-supervised hyperspectral image classification via spatial- regulated self-training,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:05.749933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 34fbab90-df52-4b46-b583-8aaa9c920512 · outbound

This paper cites Semi-supervised semantic segmentation with cross-consistency training,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Semi-supervised semantic segmentation with cross-consistency training,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:05.561897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aeba99b8-611c-4f8e-a166-662776e6e344 · outbound

This paper cites Semi-supervised semantic segmentation with high-and low-level consistency,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Semi-supervised semantic segmentation with high-and low-level consistency,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:05.345483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fbd9a9af-7707-405c-b0a1-ed327ccd4a2e · outbound

This paper cites Pseudo-label: The simple and efficient semi- supervised learning method for deep neural networks,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Pseudo-label: The simple and efficient semi- supervised learning method for deep neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:05.178920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation be617f74-0826-48af-b48c-fb5d025e1eed · outbound

This paper cites Segment anything,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Segment anything,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:03.140854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16354f4e-b6f4-4254-8590-8b1e97122d62 · outbound

This paper cites SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentation.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentation

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:01:03.962090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6ff6369a-e6ca-46ef-9da9-3d1075df1162 · outbound

This paper cites The isprs benchmark on urban object classification and 3d building reconstruction,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation The isprs benchmark on urban object classification and 3d building reconstruction,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:05.030226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7cb4fb22-38dd-410a-9e41-c11eac8dffdd · outbound

This paper cites What is a good evaluation measure for semantic segmentation?.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation What is a good evaluation measure for semantic segmentation?

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:04.762743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 92d831de-c51c-4992-8905-58cfed39ac18 · outbound

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

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:04.577461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dfbae095-1685-4329-93a6-9827c725d473 · outbound

This paper cites Unified perceptual parsing for scene understanding,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Unified perceptual parsing for scene understanding,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:04.408198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 62667e87-ee3c-4bf5-aa92-04f1fe7dd90c · outbound

This paper cites Context encoding for semantic segmentation,.

SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation Context encoding for semantic segmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:04.209193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.