Paper Citation Record · LEDGER

Learning to Prompt Segment Anything Models

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2401.04651.

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

pith.paper-citation-record.v1
2401.04651 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:12:07.571798Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T06:56:44.585158Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3748d4fa-59db-4eba-ada1-10b25cc8887f · inbound

fabSAM: A Farmland Boundary Delineation Method Based on the Segment Anything Model cites this paper.

fabSAM: A Farmland Boundary Delineation Method Based on the Segment Anything Model Learning to Prompt Segment Anything Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T17:12:07.571798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bd7e265-ed15-431b-ae97-208c07fc9d15 · inbound

SCING:Towards More Efficient and Robust Person Re-Identification through Selective Cross-modal Prompt Tuning cites this paper.

SCING:Towards More Efficient and Robust Person Re-Identification through Selective Cross-modal Prompt Tuning Learning to Prompt Segment Anything Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T21:20:06.631070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcaae867-d249-48a2-99f8-088839477017 · inbound

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges cites this paper.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Learning to Prompt Segment Anything Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.598319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8732d449-392c-4f15-b99a-83f4b9ff2588 · inbound

SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM cites this paper.

SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM Learning to Prompt Segment Anything Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T20:27:24.719140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Few-Shot Semantic Segmentation Meets SAM3 cites this paper.

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 2026-08-20T06:33:59.587034+00:00.

Observation b3d82dab-ed74-4ff7-8be5-b60902853c9e · inbound

Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM cites this paper.

Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM Learning to Prompt Segment Anything Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:31:14.688043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9cdad336-c1bc-4e3b-b550-6f8714061562 · inbound

Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation cites this paper.

Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation Learning to Prompt Segment Anything Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-02T06:56:44.586874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cbf39464-1cca-4b6e-9150-64fac5ac011a · inbound

Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation cites this paper.

Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation Learning to Prompt Segment Anything Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T12:27:07.660587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.