Paper Citation Record · LEDGER

Few-Shot Semantic Segmentation Meets SAM3

As of 4 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 4 inbound Pith citation observations for arXiv:2604.05433.

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

pith.paper-citation-record.v1
2604.05433 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T19:01:45.435123Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T02:09:22.801958Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T00:27:29.781943Z

Reference resolution

24 of 24 outbound references displayed

verified exact1
verified fuzzy23
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e27988a3-c89a-420e-b0c9-7076b749676f · outbound

This paper cites SAM 3: Segment anything with concepts.International Conference on Learning Representations (ICLR).

Few-Shot Semantic Segmentation Meets SAM3 SAM 3: Segment anything with concepts.International Conference on Learning Representations (ICLR)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.011731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 116a5e9f-72a6-4390-a5f2-58a3031d47b6 · outbound

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

Few-Shot Semantic Segmentation Meets SAM3 Emerging properties in self-supervised vision transformers

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.014188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 53dceb75-927e-40c3-9bb0-f343bfff8761 · outbound

This paper cites SANSA: Unleashing the hidden semantics in SAM2 for few-shot segmentation.Advances in Neural Information Processing Systems (NeurIPS).

Few-Shot Semantic Segmentation Meets SAM3 SANSA: Unleashing the hidden semantics in SAM2 for few-shot segmentation.Advances in Neural Information Processing Systems (NeurIPS)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:41.986459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7d5ac3db-8f26-42a8-9976-ff0cc97fc695 · outbound

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

Few-Shot Semantic Segmentation Meets SAM3 Self-support few-shot semantic segmentation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.016391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0c5ce2b3-22c3-4857-bd60-45e5357fa58c · outbound

This paper cites Learning to Prompt Segment Anything Models.

Few-Shot Semantic Segmentation Meets SAM3 Learning to Prompt Segment Anything Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:35:50.504620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 31797694-76ee-4b43-a442-38c4ccdd217a · outbound

This paper cites Segment anything.

Few-Shot Semantic Segmentation Meets SAM3 Segment anything

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.009401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0db1ab3e-33be-4cb9-9542-999a0e435364 · outbound

This paper cites Learning what not to segment: A new perspective on few-shot segmentation.

Few-Shot Semantic Segmentation Meets SAM3 Learning what not to segment: A new perspective on few-shot segmentation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.007411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b875c097-2a81-4766-8336-eecb9b4352c3 · outbound

This paper cites Matcher: Segment anything with one shot using all-purpose feature matching.International Conference on Learning Representations (ICLR).

Few-Shot Semantic Segmentation Meets SAM3 Matcher: Segment anything with one shot using all-purpose feature matching.International Conference on Learning Representations (ICLR)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.004775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2c089798-53b2-4c44-abd3-ade4b1ad5bba · outbound

This paper cites Hypercorrelation squeeze for few-shot segmenta- tion.

Few-Shot Semantic Segmentation Meets SAM3 Hypercorrelation squeeze for few-shot segmenta- tion

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.043496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 743c0363-eb8d-429c-a04c-f13284384934 · outbound

This paper cites DINOv2: Learning robust visual features without supervision.International Conference on Learning Representations (ICLR).

Few-Shot Semantic Segmentation Meets SAM3 DINOv2: Learning robust visual features without supervision.International Conference on Learning Representations (ICLR)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.041325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3da36008-636d-4096-816f-71d4928c0fc3 · outbound

This paper cites Hierarchical dense correlation distillation for few-shot segmentation.

Few-Shot Semantic Segmentation Meets SAM3 Hierarchical dense correlation distillation for few-shot segmentation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.050466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 04e6919e-1531-4fed-9964-08ea573796f9 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Few-Shot Semantic Segmentation Meets SAM3 Learning transferable visual models from natural language supervision

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.045622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9087ce3b-c3be-4f94-9ce4-9f8cdbe172bd · outbound

This paper cites SAM 2: Segment anything in images and videos.International Conference on Learning Representations (ICLR).

Few-Shot Semantic Segmentation Meets SAM3 SAM 2: Segment anything in images and videos.International Conference on Learning Representations (ICLR)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.036925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9022c69c-25f6-4709-8c3f-0649e1856b24 · outbound

This paper cites VRP-SAM: SAM with visual reference prompt.

Few-Shot Semantic Segmentation Meets SAM3 VRP-SAM: SAM with visual reference prompt

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.032712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1dbf5743-dca2-4393-a072-84b666dae767 · outbound

This paper cites Prior guided feature enrichment network for few-shot segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 44(2):1050–1065.

Few-Shot Semantic Segmentation Meets SAM3 Prior guided feature enrichment network for few-shot segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 44(2):1050–1065

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.028134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cc153bd4-4da4-43ce-8caf-50dff331d4ca · outbound

This paper cites Adaptive FSS: a novel few-shot segmentation framework via prototype enhancement.

Few-Shot Semantic Segmentation Meets SAM3 Adaptive FSS: a novel few-shot segmentation framework via prototype enhancement

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.025460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0cd865e5-52c1-4295-a9b7-cdd6f6cb090f · outbound

This paper cites Focus on query: Adversarial mining transformer for few-shot segmentation.Advances in Neural Information Processing Systems (NeurIPS), 36:31524–31542.

Few-Shot Semantic Segmentation Meets SAM3 Focus on query: Adversarial mining transformer for few-shot segmentation.Advances in Neural Information Processing Systems (NeurIPS), 36:31524–31542

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.030450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6bd76f02-aa4d-44e0-9728-7a90c7c07cb8 · outbound

This paper cites Eliminating feature ambiguity for few-shot segmentation.

Few-Shot Semantic Segmentation Meets SAM3 Eliminating feature ambiguity for few-shot segmentation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.034823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6ceff908-6825-42d6-a7bd-ef4905163fa8 · outbound

This paper cites Hybrid mamba for few-shot segmentation.Advances in Neural Information Processing Systems (NeurIPS), 37:73858–73883.

Few-Shot Semantic Segmentation Meets SAM3 Hybrid mamba for few-shot segmentation.Advances in Neural Information Processing Systems (NeurIPS), 37:73858–73883

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.039102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 77d792f3-4817-4429-b27b-469cdf1af0f0 · outbound

This paper cites Self-calibrated cross attention network for few-shot segmentation.

Few-Shot Semantic Segmentation Meets SAM3 Self-calibrated cross attention network for few-shot segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.047968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cd828f27-ace2-4372-8ee0-247be6594d24 · outbound

This paper cites Unlocking the power of SAM 2 for few-shot segmentation.

Few-Shot Semantic Segmentation Meets SAM3 Unlocking the power of SAM 2 for few-shot segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.020867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d49e5c5c-3a91-4f14-beb8-d93ca59e478a · outbound

This paper cites Bridge the points: Graph- based few-shot segment anything semantically.Advances in Neural Information Processing Systems (NeurIPS), 37:33232–33261.

Few-Shot Semantic Segmentation Meets SAM3 Bridge the points: Graph- based few-shot segment anything semantically.Advances in Neural Information Processing Systems (NeurIPS), 37:33232–33261

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.023102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 03f1d57c-2179-4848-8154-05b50c11829f · outbound

This paper cites Feature-proxy transformer for few-shot segmentation.Advances in Neural Information Processing Systems (NeurIPS), 35:6575–6588.

Few-Shot Semantic Segmentation Meets SAM3 Feature-proxy transformer for few-shot segmentation.Advances in Neural Information Processing Systems (NeurIPS), 35:6575–6588

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.052754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 05aee42c-e72a-498d-84ec-731c72b81701 · outbound

This paper cites Personalize segment anything model with one shot.International Conference on Learning Representations (ICLR).

Few-Shot Semantic Segmentation Meets SAM3 Personalize segment anything model with one shot.International Conference on Learning Representations (ICLR)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:32:42.018699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation 8120efaf-07ac-42df-b542-e166658d4d43 · inbound

Example-Based Object Detection cites this paper.

Example-Based Object Detection Few-Shot Semantic Segmentation Meets SAM3

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-09T06:50:41.200521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ac925caf-0e55-4994-8758-2e8c95a89fd8 · inbound

Training-Free Generalized Few-Shot Segmentation through Open-Vocabulary Semantic Arbitration cites this paper.

Training-Free Generalized Few-Shot Segmentation through Open-Vocabulary Semantic Arbitration Few-Shot Semantic Segmentation Meets SAM3

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-07-03T00:27:29.783084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 710d3adf-56fd-462c-ba59-c969c8dfa74c · inbound

SADe: Sparse-Atom Support Decontamination for Few-Shot Segmentation with Weak Support Annotations cites this paper.

SADe: Sparse-Atom Support Decontamination for Few-Shot Segmentation with Weak Support Annotations Few-Shot Semantic Segmentation Meets SAM3

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-31T07:22:46.274403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 040e6dfc-20ba-4217-808b-cfecf1e2b20e · inbound

Few-Shot Open-Vocabulary Remote Sensing Segmentation via Textual Inversion cites this paper.

Few-Shot Open-Vocabulary Remote Sensing Segmentation via Textual Inversion Few-Shot Semantic Segmentation Meets SAM3

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T02:09:22.801958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.