Paper Citation Record · LEDGER

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery

As of 20 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2505.08932.

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

pith.paper-citation-record.v1
2505.08932 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:47:24.511290Z

measured 12 of 12 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 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

12 of 12 outbound references displayed

verified exact0
verified fuzzy9
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 43701998-4a8c-4901-9b35-f4aff3336b8f · outbound

This paper cites UA V-assisted seeding and monitoring of reforestation sites: a review,.

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery UA V-assisted seeding and monitoring of reforestation sites: a review,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:47:24.751294Z

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 75d72835-1d7c-413a-a4b0-a5fbe86b771a · outbound

This paper cites Factoring restoration practitioner perceptions into future design of mechanical direct seeders for native seeds,.

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery Factoring restoration practitioner perceptions into future design of mechanical direct seeders for native seeds,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:47:24.734606Z

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 9527f35a-4d91-4802-9083-6d391e7633da · outbound

This paper cites An Image is Worth 16x16 Words: Trans- formers for Image Recognition at Scale,.

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery An Image is Worth 16x16 Words: Trans- formers for Image Recognition at Scale,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T21:47:24.455407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5844bd29-7cc3-4958-bb06-462c9b48b02f · outbound

This paper cites Segment Anything,.

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery Segment Anything,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:47:24.703714Z

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 e6f654f3-9324-4e77-b520-bff24b8efab4 · outbound

This paper cites Learning Transferable Visual Models from Natural Language Supervision,.

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery Learning Transferable Visual Models from Natural Language Supervision,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:47:24.685028Z

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 28ac70ae-646e-4de9-a0a8-2e2362b93331 · outbound

This paper cites RSPrompter: Learning to Prompt for Remote Sensing Instance Seg- mentation Based on Visual Foundation Model,.

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery RSPrompter: Learning to Prompt for Remote Sensing Instance Seg- mentation Based on Visual Foundation Model,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:47:24.665053Z

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 bb5738ca-7bba-4944-ac69-cd62d3b9576a · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models,.

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery LoRA: Low-Rank Adaptation of Large Language Models,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T21:47:24.479683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61045170-3c98-49fd-b9df-8ff7a184467b · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP,.

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery Parameter-Efficient Transfer Learning for NLP,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:47:24.637904Z

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 44c7e347-a04c-483f-bb79-60890ba8af42 · outbound

This paper cites Adaptformer: Adapting Vision Transformers for Scalable Visual Recog- nition,.

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery Adaptformer: Adapting Vision Transformers for Scalable Visual Recog- nition,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:47:24.622090Z

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 227f1411-c3f1-447a-a494-2c4209032874 · outbound

This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T21:47:24.497304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de93b92a-5e88-4692-a55e-f5c29b17b58c · outbound

This paper cites Segment anything model for medical image analysis: An experimental study,.

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery Segment anything model for medical image analysis: An experimental study,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:47:24.603981Z

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 c4f9a777-fad4-45fe-98d5-db130e9e3b9c · outbound

This paper cites Fine-tuning vision foundation model for crack segmentation in civil infrastructures,.

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery Fine-tuning vision foundation model for crack segmentation in civil infrastructures,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:47:24.584121Z

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.

Pith citing papers

No inbound Pith citation observations are available.