Paper Citation Record · LEDGER

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery

As of 17 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2604.17920.

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

pith.paper-citation-record.v1
2604.17920 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T05:54:47.183674Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

19 of 19 outbound references displayed

verified exact4
verified fuzzy15
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 30e3b1fa-6c61-4cd4-b4d7-8d55bae693d5 · outbound

This paper cites Maritime Surveillance Finding Dark Ships with Satellites and Artificial Intelli- gence.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery Maritime Surveillance Finding Dark Ships with Satellites and Artificial Intelli- gence

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:29.009955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 37132778-eb37-4482-aa53-f7b191ae17f4 · outbound

This paper cites Automatic Ship Detection Based on RetinaNet Us- ing Multi-Resolution Gaofen-3 Imagery.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery Automatic Ship Detection Based on RetinaNet Us- ing Multi-Resolution Gaofen-3 Imagery

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:29.004029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9910eb5b-0795-493a-8b43-603591f8e340 · outbound

This paper cites Data-driven methods for detection of abnormal ship behavior: Progress and trends.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery Data-driven methods for detection of abnormal ship behavior: Progress and trends

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:28.993713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 18162607-d9dd-490b-a3df-6f22fc5a9d72 · outbound

This paper cites Ship detection in SAR im- ages based on an improved faster R-CNN.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery Ship detection in SAR im- ages based on an improved faster R-CNN

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:29.007120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 09db7e4a-f339-413c-9a0a-c5db5f86dc64 · outbound

This paper cites A Review of Deep-Learning-Based SAR Image Ship Interpreta- tion Technology: The Latest Advances.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery A Review of Deep-Learning-Based SAR Image Ship Interpreta- tion Technology: The Latest Advances

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:29.012735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 674afa82-e3c9-4e5e-af8b-bd411dc860b0 · outbound

This paper cites LS-SSDD-v1.0: A Deep Learn- ing Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery LS-SSDD-v1.0: A Deep Learn- ing Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:28.996701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e8606027-a18f-4ff5-bce9-174b38e12d02 · outbound

This paper cites SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:29.015672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ca07dcd2-514c-4bd4-947f-cea5c0a1ba79 · outbound

This paper cites Segment Anything.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery Segment Anything

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:29.000280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 05ac9f72-d7bd-4c66-ad53-122e57cc6ee5 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery SAM 2: Segment Anything in Images and Videos

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:56:25.474502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a9772402-601a-410f-855a-c11228a4aa83 · outbound

This paper cites On the Status of Foundation Mod- els for SAR Imagery.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery On the Status of Foundation Mod- els for SAR Imagery

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:56:11.067978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d6e5425f-d569-4214-986a-495e9ba6a637 · outbound

This paper cites SAMSAR: A modified SAM architecture for oceanic ship segmentation of satellite SAR images using CNN-based Cross- Fused Attention.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery SAMSAR: A modified SAM architecture for oceanic ship segmentation of satellite SAR images using CNN-based Cross- Fused Attention

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:28.987257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2444b90d-c22c-452b-ad42-19d8af831332 · outbound

This paper cites Tun- ing a SAM-Based Model With Multicognitive Vi- sual Adapter to Remote Sensing Instance Segmen- tation.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery Tun- ing a SAM-Based Model With Multicognitive Vi- sual Adapter to Remote Sensing Instance Segmen- tation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:28.990821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 771908f9-611b-4ac7-bbdd-263f39d020c0 · outbound

This paper cites Context-Aggregated and SAM-Guided Network for ViT-Based Instance Segmentation in Remote Sensing Images.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery Context-Aggregated and SAM-Guided Network for ViT-Based Instance Segmentation in Remote Sensing Images

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:28.979825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6c106103-6fe7-448f-89a5-0ddaf06e76dd · outbound

This paper cites BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:28.971862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9b7cd6d4-2dc2-437b-9e2b-8f6acbda90d6 · outbound

This paper cites Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:28.966188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 852da6fe-ff25-4316-bcec-362598332ea9 · outbound

This paper cites SAM on Medical Images: A Comprehensive Study on Three Prompt Modes.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery SAM on Medical Images: A Comprehensive Study on Three Prompt Modes

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:56:11.060194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a7b85513-eb7b-49ec-8f22-1924a84b14df · outbound

This paper cites Segment anything in medical images.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery Segment anything in medical images

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:28.975667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4d061c73-df99-416e-8433-199e29c181f8 · outbound

This paper cites HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmenta- tion.

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmenta- tion

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T18:35:28.982780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c4d10253-085b-41f1-bd0f-5613763a339a · outbound

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

Prompting Foundation Models for Zero-Shot Ship Instance Segmentation in SAR Imagery Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:56:11.062722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.