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Paper Citation Record · LEDGER

Attention from Above: A Multimodal Model for Drone-Based Object Localization

As of 7 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2607.17669.

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

pith.paper-citation-record.v1
2607.17669 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T17:23:57.404158Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

47 of 47 outbound references displayed

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  • verified fuzzy0
  • unresolved33
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  • malformed identifier3
  • metadata mismatch0

External citation measurements

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Outbound references

Observation 1bef4fee-9e63-4fdf-8a7f-e180f49007cd · outbound

This paper cites To achieve this goal, extensive experiments were conducted using the VisDrone dataset, which consists of drone- captured images collected under diverse environmental conditions.

Attention from Above: A Multimodal Model for Drone-Based Object Localization To achieve this goal, extensive experiments were conducted using the VisDrone dataset, which consists of drone- captured images collected under diverse environmental conditions

Reference 1

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Observation a561de8c-449e-4a39-98d4-a433f5833b27 · outbound

This paper cites car” and “truck.

Attention from Above: A Multimodal Model for Drone-Based Object Localization car” and “truck

Reference 2

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Observation 91e437f5-0d08-4921-b9f9-f2d67b8f8a84 · outbound

This paper cites From toys to tools: The co-evolution of technological and entrepreneurial developments in the drone industry,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization From toys to tools: The co-evolution of technological and entrepreneurial developments in the drone industry,

Reference 3

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Observation 2d9985fb-6f1b-48df-a148-1a2001dcde3d · outbound

This paper cites Edge Computing, Emerging Trends, and Drone Technology for the Business Industry,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Edge Computing, Emerging Trends, and Drone Technology for the Business Industry,

Reference 4

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source=pdf_text observed=2026-08-01T17:23:53.514533Z digest=sha256:83f005eda09726cb38b4e407d24ab9edbfc214f6bf63b3b1d6fd777f06c332ca

Observation 5ea99d68-08d9-48d7-8b96-cc7597ea9923 · outbound

This paper cites Use of AI applications for the drone industry,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Use of AI applications for the drone industry,

Reference 5

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Observation ffe00af6-5be1-4ea2-8e46-b3f4f115e446 · outbound

This paper cites UFPMP-Det:Toward Accurate and Efficient Object Detection on Drone Imagery,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization UFPMP-Det:Toward Accurate and Efficient Object Detection on Drone Imagery,

Reference 6

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Observation 8f6c5cc4-1dac-4142-be85-9343f04d42aa · outbound

This paper cites DR-YOLO: An improved multi-scale small object detection model for drone aerial photography scenes based on YOLOv7,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization DR-YOLO: An improved multi-scale small object detection model for drone aerial photography scenes based on YOLOv7,

Reference 9

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Observation 7786534a-98a8-4136-9fe7-90803ff21054 · outbound

This paper cites DHANet: Dual-Stream Hierarchical Interaction Networks for Multimodal Drone Object Detection,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization DHANet: Dual-Stream Hierarchical Interaction Networks for Multimodal Drone Object Detection,

Reference 10

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Observation 6c761c0f-a839-461f-aa63-5f075220547f · outbound

This paper cites HiCAL: Hierarchical Consistency-Based Active Learning for Drone-View Object Detection,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization HiCAL: Hierarchical Consistency-Based Active Learning for Drone-View Object Detection,

Reference 11

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Observation 7780b813-e22d-437a-9a6d-86239e415759 · outbound

This paper cites Automated Detection and Monitoring of Ground-Nesting Bee Nests Using Drone Imagery and Deep Learning,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Automated Detection and Monitoring of Ground-Nesting Bee Nests Using Drone Imagery and Deep Learning,

Reference 12

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Observation cf949cc6-cfc5-42b1-a9c2-b0728cc31c7e · outbound

This paper cites SPVD-DETR: A novel real-time end-to-end object detector of sweetpotato virus disease from unmanned aerial vehicle ortho imagery,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization SPVD-DETR: A novel real-time end-to-end object detector of sweetpotato virus disease from unmanned aerial vehicle ortho imagery,

Reference 13

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Observation 65b55fb4-2438-4fd0-bfe8-a91832c9fd07 · outbound

This paper cites Single-tree Delineation by Instance Segmentation Using Drone-based Lidar and Multispectral Imagery: a Comparative Study in Various Forest Structures,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Single-tree Delineation by Instance Segmentation Using Drone-based Lidar and Multispectral Imagery: a Comparative Study in Various Forest Structures,

Reference 14

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Observation 2472ed9d-4eb8-4b45-b26d-96e121b4702b · outbound

This paper cites Advancing traffic object detection in complex environments: a deep learning object detection approach with vehicle-mounted UAV data for traffic scene perception,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Advancing traffic object detection in complex environments: a deep learning object detection approach with vehicle-mounted UAV data for traffic scene perception,

Reference 15

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Observation 9c4bef65-df51-434f-ad6b-c79b34cae56f · outbound

This paper cites Enhancing Wildfire Preparedness and Response: A Drone Network- Based Early Warning System for Bushfires,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Enhancing Wildfire Preparedness and Response: A Drone Network- Based Early Warning System for Bushfires,

Reference 16

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Observation 29689278-d1f3-4d33-9fd2-93a85acf7d06 · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Rich feature hierarchies for accurate object detection and semantic segmentation,

Reference 17

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Observation a5a3bb49-a97d-4d4a-83a1-3e3a54515206 · outbound

This paper cites Fast r-cnn,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Fast r-cnn,

Reference 18

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Observation 9fa90ed1-9a0e-4886-8ac7-bb4dc822255d · outbound

This paper cites Faster R-CNN: Towards real-time object detection with region proposal networks,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Faster R-CNN: Towards real-time object detection with region proposal networks,

Reference 19

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Observation b4eb8cc3-e27b-4979-bbbe-3202be1a270d · outbound

This paper cites Mask r-cnn,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Mask r-cnn,

Reference 20

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Observation a3de1e62-763e-48f7-adee-090aaf353268 · outbound

This paper cites You only look once: Unified real-time object detection,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization You only look once: Unified real-time object detection,

Reference 21

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Observation 973f211f-f83c-4dbf-af2d-fe795383d0d8 · outbound

This paper cites YOLO9000: better, faster, stronger,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization YOLO9000: better, faster, stronger,

Reference 22

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Observation 5357f8dc-7047-4fc5-bc27-704ff6326514 · outbound

This paper cites YOLOv3: An Incremental Improvement.

Attention from Above: A Multimodal Model for Drone-Based Object Localization YOLOv3: An Incremental Improvement

Reference 23

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Observation 23821e80-e1ce-4259-9d3c-0f7f474461d1 · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

Attention from Above: A Multimodal Model for Drone-Based Object Localization YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 24

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Observation dbe1a9b8-2f9b-44ba-b33c-5ed0e1099b04 · outbound

This paper cites Available online: https://github.com/ultralytics/yolov5, (accessed on: 23 November 2022).

Attention from Above: A Multimodal Model for Drone-Based Object Localization Available online: https://github.com/ultralytics/yolov5, (accessed on: 23 November 2022)

Reference 25

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source=pdf_text observed=2026-08-01T17:23:55.944808Z digest=sha256:beb50ebd57eb86db5ea46d3ee9f5760520b3ec733ab39fbda0716432d951cfb8

Observation 7d0d31c5-5f2c-4d96-8ceb-01140b2c4a26 · outbound

This paper cites YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications.

Attention from Above: A Multimodal Model for Drone-Based Object Localization YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

Reference 26

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Observation 26f2fa26-e336-4647-8d50-ccb17e873f2d · outbound

This paper cites YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,

Reference 27

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Observation b4771bab-be7c-4155-b5ed-d0afc477c915 · outbound

This paper cites Available online: https://docs.ultralytics.com/models/yolov8 (accessed on: 11 November 2023).

Attention from Above: A Multimodal Model for Drone-Based Object Localization Available online: https://docs.ultralytics.com/models/yolov8 (accessed on: 11 November 2023)

Reference 28

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Observation af854cfb-0d65-4bdb-a2af-a0d718e5f420 · outbound

This paper cites YOLOv9: Learning what you want to learn using programmable gradient information. European Conference on Computer Vision,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization YOLOv9: Learning what you want to learn using programmable gradient information. European Conference on Computer Vision,

Reference 29

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Observation 15fb4cce-0936-424c-99cb-d196f5f53514 · outbound

This paper cites YOLOv10: Real-Time End-to-End Object Detection,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization YOLOv10: Real-Time End-to-End Object Detection,

Reference 30

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source=pdf_text observed=2026-08-01T17:23:56.233855Z digest=sha256:6743000c7ad9b21d477019b0db8befa206d4f9b254b161050734c8a8e7ddb91f

Observation 4e601489-88d3-4816-b315-018c761de33c · outbound

This paper cites YOLOv11: An Overview of the Key Architectural Enhancements,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization YOLOv11: An Overview of the Key Architectural Enhancements,

Reference 31

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Observation 9e8fe89e-42c4-4984-ac15-445d47cbcd43 · outbound

This paper cites Ssd: Single shot multibox detector,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Ssd: Single shot multibox detector,

Reference 32

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Observation 236e03c3-f614-4183-bfa5-5ac4db9d31af · outbound

This paper cites YOLOv12: Attention-Centric Real-Time Object Detectors.

Attention from Above: A Multimodal Model for Drone-Based Object Localization YOLOv12: Attention-Centric Real-Time Object Detectors

Reference 33

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Observation cdc16677-0d7a-4c62-a637-fba3676c3fd6 · outbound

This paper cites Available online: https://github.com/ultralytics/ultralytics, (accessed on: 14 January 2026).

Attention from Above: A Multimodal Model for Drone-Based Object Localization Available online: https://github.com/ultralytics/ultralytics, (accessed on: 14 January 2026)

Reference 34

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Observation c9db5f4a-4918-4b80-b4d7-a056a1dbd40f · outbound

This paper cites LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction,

Reference 35

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source=pdf_text observed=2026-08-01T17:23:56.787092Z digest=sha256:33cd8aa84430c2e3a24186620601dba828d47f6b38fef02aed819046cfe36468

Observation 00eb6df5-d678-4810-9610-2cd2cb655595 · outbound

This paper cites Focal loss for dense object detection,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Focal loss for dense object detection,

Reference 36

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Observation 9cbf7331-363f-49e8-8783-208d3c961756 · outbound

This paper cites Single-shot refinement neural network for object detection,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Single-shot refinement neural network for object detection,

Reference 37

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Observation f289f90c-614a-42d5-94c4-ad1b7882dcf2 · outbound

This paper cites Strong and Weak Prompt Engineering for Remote Sensing Image-Text Cross- Modal Retrieval,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Strong and Weak Prompt Engineering for Remote Sensing Image-Text Cross- Modal Retrieval,

Reference 38

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Observation f1ca4c81-3974-4047-8027-aadede2e1f62 · outbound

This paper cites GADNet: Improving image–text matching via graph-based aggregation and disentanglement,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization GADNet: Improving image–text matching via graph-based aggregation and disentanglement,

Reference 39

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Observation c6628848-d655-4f8f-86d3-7d88aaf3cfc2 · outbound

This paper cites A System of Multimodal Image-Text Retrieval Based on Pre-Trained Models Fusion,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization A System of Multimodal Image-Text Retrieval Based on Pre-Trained Models Fusion,

Reference 40

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Observation 430f971a-7a95-459d-beb4-e9672928593d · outbound

This paper cites Make It Count: Text-to-Image Generation with an Accurate Number of Objects,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Make It Count: Text-to-Image Generation with an Accurate Number of Objects,

Reference 41

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Observation 3dd9d87d-5f50-4b14-9640-fee2d161228d · outbound

This paper cites Local-enhanced representation for text-based person search,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Local-enhanced representation for text-based person search,

Reference 42

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Observation 007992c8-c938-4784-8bd6-d749e67fb5c1 · outbound

This paper cites An efficient YOLOv12-based framework for detecting extremely small-scale objects,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization An efficient YOLOv12-based framework for detecting extremely small-scale objects,

Reference 44

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 4b51e30a-843d-48bb-874b-615a899ae47f · outbound

This paper cites Perception-Guided Jailbreak Against Text-to-Image Models,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Perception-Guided Jailbreak Against Text-to-Image Models,

Reference 45

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Observation 652c627b-c5d6-4e04-a238-d3b93bfe4cb5 · outbound

This paper cites A text-guided vision model for enhanced recognition of small instances,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization A text-guided vision model for enhanced recognition of small instances,

Reference 46

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation e0bbfa59-d679-45f3-8b82-42f17126eac7 · outbound

This paper cites YOLO-World: Real-Time Open-Vocabulary Object Detection,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization YOLO-World: Real-Time Open-Vocabulary Object Detection,

Reference 47

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8b6f9ad4-2dbf-4374-81ab-cd0b863bb924 · outbound

This paper cites CSPNet: A new backbone that can enhance learning capability of CNN,.

Attention from Above: A Multimodal Model for Drone-Based Object Localization CSPNet: A new backbone that can enhance learning capability of CNN,

Reference 49

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Observation 4ae386db-caf3-40e9-abdd-a6908ac7d767 · outbound

This paper cites YOLOv11: An Overview of the Key Architectural Enhancements.

Attention from Above: A Multimodal Model for Drone-Based Object Localization YOLOv11: An Overview of the Key Architectural Enhancements

Reference 2024

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Observation 6b6c01c8-a10a-4cd7-b64e-4f48188c240d · outbound

This paper cites an unresolved cited work.

Attention from Above: A Multimodal Model for Drone-Based Object Localization Unresolved cited work

Reference 2025

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Pith citing papers

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