Paper Citation Record · LEDGER

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection

As of 18 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 1 inbound Pith citation observation for arXiv:2411.09180.

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

pith.paper-citation-record.v1
2411.09180 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:00:03.446500Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:00:03.288269Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T21:00:03.561322Z

Reference resolution

21 of 21 outbound references displayed

verified exact0
verified fuzzy17
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 91693539-151d-493e-b67b-5522ceb2bf44 · outbound

This paper cites LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T21:00:03.569843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c927f81a-1368-4305-a715-8aa6e27022a8 · outbound

This paper cites an unresolved cited work.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-12T21:00:04.047174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 39a25d15-f0da-4714-9a3c-d44d372a7069 · outbound

This paper cites Implementation Details The proposed method was evaluated on the VisDrone dataset [14], measuring object detection performance withmAP50, mAP75 and mAP50:95.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Implementation Details The proposed method was evaluated on the VisDrone dataset [14], measuring object detection performance withmAP50, mAP75 and mAP50:95

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:04.018267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2a8a59d3-8d40-45d5-9ef7-a6511187ef11 · outbound

This paper cites First, it removes domain-specific features from the entire scene rather than targeting them at the object level.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection First, it removes domain-specific features from the entire scene rather than targeting them at the object level

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.996258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 253d74e7-9879-4187-a651-5fdc865ce999 · outbound

This paper cites Our comparative experiments revealed that LEAP:D outperforms baseline models and other state-of-the-art methods.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Our comparative experiments revealed that LEAP:D outperforms baseline models and other state-of-the-art methods

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.974210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c94968eb-dff7-4e14-9729-908e170da914 · outbound

This paper cites High-resolution processing and sigmoid fusion modules for efficient detection of small objects in an embedded system,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection High-resolution processing and sigmoid fusion modules for efficient detection of small objects in an embedded system,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.950567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 17d15146-b3b5-49eb-83d0-7bec31c64e4f · outbound

This paper cites Enhanced detection of small objects in aerial imagery: A high-resolution neural network ap- proach with amplified feature pyramid and sigmoid re- weighting,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Enhanced detection of small objects in aerial imagery: A high-resolution neural network ap- proach with amplified feature pyramid and sigmoid re- weighting,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.928536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8c603a46-df68-477f-863e-2a8c0d489037 · outbound

This paper cites Delving into robust object detection from unmanned aerial ve- hicles: A deep nuisance disentanglement approach,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Delving into robust object detection from unmanned aerial ve- hicles: A deep nuisance disentanglement approach,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.898451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8ad4fd78-986d-42ff-b8e1-202440e98e60 · outbound

This paper cites Training domain-invariant object detector faster with feature replay and slow learner,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Training domain-invariant object detector faster with feature replay and slow learner,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.872285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 99391b4c-c6b3-41fc-b732-2fee8ff80203 · outbound

This paper cites ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.847105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 09fd50bd-ce96-4c2f-b88d-e9b8c160e230 · outbound

This paper cites Domain feature decomposition for efficient object de- tection in aerial images,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Domain feature decomposition for efficient object de- tection in aerial images,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.825499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c742609f-f26b-4c96-a0fe-5f9506faf799 · outbound

This paper cites Learning to prompt for vision-language models,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Learning to prompt for vision-language models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.798789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f9e150b0-5093-4a57-a334-24b31f27f579 · outbound

This paper cites Learning transferable visual models from natural lan- guage supervision,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Learning transferable visual models from natural lan- guage supervision,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T21:00:03.377805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 356e448b-c828-4ce6-9f17-de6adc8d7eab · outbound

This paper cites Conditional prompt learning for vision- language models,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Conditional prompt learning for vision- language models,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.754765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bf44fcbd-21ff-4ebb-ae0f-500e02d69ae2 · outbound

This paper cites Clip the gap: A single domain generalization approach for object detection,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Clip the gap: A single domain generalization approach for object detection,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.733579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 194fb227-9c09-43fb-a674-eb84b47de77b · outbound

This paper cites Shooting condition insensitive unmanned aerial vehicle object detection,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Shooting condition insensitive unmanned aerial vehicle object detection,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.707966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a3d8ceb7-c164-44bc-8a5e-008830364a3e · outbound

This paper cites Fast r-cnn,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Fast r-cnn,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.684910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b9a5f3ba-cc11-4157-8430-7779f01e484e · outbound

This paper cites Feature pyra- mid networks for object detection,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Feature pyra- mid networks for object detection,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.652414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c9a900f0-b4e7-462a-882c-9ba71165d82d · outbound

This paper cites Detection and tracking meet drones challenge,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Detection and tracking meet drones challenge,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.620444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f9961ec9-56b6-46ac-bf32-b6eecab16a10 · outbound

This paper cites An overview of gradient descent optimization algorithms.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection An overview of gradient descent optimization algorithms

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T21:00:03.437335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f17e7c64-4944-4549-9eab-5e1d4eafb59d · outbound

This paper cites Cascade r-cnn: Delving into high quality object detection,.

LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection Cascade r-cnn: Delving into high quality object detection,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:00:03.595526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 91693539-151d-493e-b67b-5522ceb2bf44 · inbound