Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-16T21:40:34.466112Z
Paper Citation Record · LEDGER
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation
As of 4 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2512.15564.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T21:55:54.401751Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
20 of 20 outbound references displayed
- verified exact2
- verified fuzzy18
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 52118a45-b536-40f9-8880-66e35eda6610 · outbound
This paper cites Monitoring forest changes with foundation models and sentinel-2 time series.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Monitoring forest changes with foundation models and sentinel-2 time series
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.019730Z
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 1191160c-62e2-40f0-a1b0-c700713b4641 · outbound
This paper cites The segment anything model (sam) for remote sensing applications: From zero to one shot.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation The segment anything model (sam) for remote sensing applications: From zero to one shot
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.039895Z
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 0e5263e4-f639-4644-9f0b-b1726a95d0bf · outbound
This paper cites Remote sensing image segmentation advances: A meta-analysis.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Remote sensing image segmentation advances: A meta-analysis
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.022238Z
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 a6dff122-a386-4db5-8686-beda90bd550a · outbound
This paper cites Pointsam: Pointly- supervised segment anything model for remote sensing images.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Pointsam: Pointly- supervised segment anything model for remote sensing images
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.032685Z
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 96a056d4-8509-4dda-9fc0-0ff725bec8c8 · outbound
This paper cites Learning transferable visual models from natural language supervision.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Learning transferable visual models from natural language supervision
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.017254Z
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 fd2118fe-5d94-42a7-ae6f-4c9127489997 · outbound
This paper cites Grounded language-image pre-training.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Grounded language-image pre-training
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.037299Z
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 e30ce821-f119-49eb-8f7d-d3d7a8684c99 · outbound
This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Grounding dino: Marrying dino with grounded pre-training for open-set object detection
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.044597Z
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 6e4a41bd-0ae8-4020-af6a-a22dd7967062 · outbound
This paper cites Image segmentation using text and image prompts.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Image segmentation using text and image prompts
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.028430Z
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 5166939d-e9dd-447a-aa38-47eadf5aa7c9 · outbound
This paper cites Remoteclip: A vision language foundation model for remote sensing.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Remoteclip: A vision language foundation model for remote sensing
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.030558Z
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 7334a4d4-7b16-4723-a382-c09b62a96eb8 · outbound
This paper cites Strong and weak prompt engineering for remote sensing image-text cross-modal retrieval.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Strong and weak prompt engineering for remote sensing image-text cross-modal retrieval
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.042134Z
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 389efeb2-abd6-465b-b97e-1a28f5c561aa · outbound
This paper cites Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensing.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensing
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T21:41:17.574513Z
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 407fe71e-0f10-431f-9109-1a407d3ba301 · outbound
This paper cites Segclip: Mul- timodal visual-language and prompt learning for high-resolution remote sensing semantic segmentation.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Segclip: Mul- timodal visual-language and prompt learning for high-resolution remote sensing semantic segmentation
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.035151Z
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 e131ec55-065a-4f67-8065-36bc3cde2303 · outbound
This paper cites Reviving iterative training with mask guidance for interactive segmentation.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Reviving iterative training with mask guidance for interactive segmentation
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.014918Z
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 af4eab21-04c8-4dac-b0ea-752c390e639f · outbound
This paper cites Focalclick: Towards practical interactive image segmentation.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Focalclick: Towards practical interactive image segmentation
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.025945Z
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 7b4ac16e-1920-4728-80fd-197a06b0a7fc · outbound
This paper cites Segment anything.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Segment anything
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.007272Z
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 7d29adc9-cdda-47c6-b500-ca3ce65755b2 · outbound
This paper cites SAM 2: Segment Anything in Images and Videos.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation SAM 2: Segment Anything in Images and Videos
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-05-16T21:41:17.577785Z
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 c0a2e79f-7804-43bc-bace-4c665c29e381 · outbound
This paper cites Rsprompter: Learning to prompt for remote sensing instance seg- mentation based on visual foundation model.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Rsprompter: Learning to prompt for remote sensing instance seg- mentation based on visual foundation model
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.005145Z
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 1916de71-044b-4b4b-8129-6428dca2b6f4 · outbound
This paper cites Segment everything everywhere all at once.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Segment everything everywhere all at once
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.009827Z
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 12deddb3-a166-4c2e-8bcf-7a50321dacef · outbound
This paper cites Sam 3: Segment anything with concepts.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Sam 3: Segment anything with concepts
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.012199Z
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 8d128de1-d2bd-4dc1-80e9-b89cdadbe7dd · outbound
This paper cites Wikidata: a free collaborative knowl- edgebase.
On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation Wikidata: a free collaborative knowl- edgebase
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T21:41:18.002266Z
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 88371389-5098-4a82-9efd-a020ee5e0b77 · inbound
More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe cites this paper.
More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T21:55:54.401751Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.