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Paper Citation Record · LEDGER

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing

As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2512.09700.

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

pith.paper-citation-record.v1
2512.09700 v4

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measured 36 of 36 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-03T17:28:21.682509Z

measured 36 of 36 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

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Reference resolution

36 of 36 outbound references displayed

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Outbound references

Observation 992c3ff4-53cc-42fc-81dd-eb06b869f6a8 · outbound

This paper cites Advanced ship detection and ocean monitoring with satel- lite imagery and deep learning for marine science applications,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Advanced ship detection and ocean monitoring with satel- lite imagery and deep learning for marine science applications,

Reference 1

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Observation 5fb0e6aa-2505-4f15-b325-546e3dec87ea · outbound

This paper cites Vessel detection leveraging satellite imagery and yolo in maritime surveillance,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Vessel detection leveraging satellite imagery and yolo in maritime surveillance,

Reference 2

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Observation 5df1c8ce-9db2-4392-bcf1-5a55b1b65492 · outbound

This paper cites Vessel detection in satellite images using deep learning,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Vessel detection in satellite images using deep learning,

Reference 3

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Observation 0ead3514-75f3-4920-be42-e2bad1d05a63 · outbound

This paper cites Ship detection with deep learning in optical remote-sensing images: A survey of challenges and advances,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Ship detection with deep learning in optical remote-sensing images: A survey of challenges and advances,

Reference 4

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Observation ce4859ea-4834-4288-8d4d-2a696f84e253 · outbound

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

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing You only look once: Unified, real-time object detection,

Reference 5

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Observation 504c18bb-da81-438e-9699-25b4f86e71fd · outbound

This paper cites Object detection in 20 years: A survey,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Object detection in 20 years: A survey,

Reference 6

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Observation 8dde0289-1a8e-4159-be6f-ae7fb5eb6b95 · outbound

This paper cites YOLOv3: An Incremental Improvement.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing YOLOv3: An Incremental Improvement

Reference 7

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Observation 4670d593-5883-4e78-81bb-64a75a265531 · outbound

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

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 8

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Observation 6e4ff916-2fad-4157-9820-7c2f850a71ca · outbound

This paper cites Ultralytics yolov5,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Ultralytics yolov5,

Reference 9

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Observation 3c05f28f-7c35-4ae4-91ae-7fb6b9cc79e8 · outbound

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

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

Reference 10

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Observation 77eac340-1ed6-4ec3-baa3-0fe44902e8a0 · outbound

This paper cites Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,

Reference 11

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Observation c8ba92e3-c45d-407d-9765-7aa8ab6da0a0 · outbound

This paper cites Ultralytics yolov8,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Ultralytics yolov8,

Reference 12

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Observation d2254a8a-d1ef-4d95-9bb9-ebe0d1741094 · outbound

This paper cites Yolov9: Learning what you want to learn using programmable gradient information,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Yolov9: Learning what you want to learn using programmable gradient information,

Reference 13

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Observation 800c85a3-8c2c-4793-8e02-a642fcdb3866 · outbound

This paper cites Yolov10: Real-time end-to-end object detection,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Yolov10: Real-time end-to-end object detection,

Reference 14

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Observation ea61501c-7679-4f38-9c63-bd0aea90616f · outbound

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

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing YOLOv11: An Overview of the Key Architectural Enhancements

Reference 15

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Observation 86db77d7-9d04-4d7c-bca9-193b96e02c9f · outbound

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

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing YOLOv12: Attention-Centric Real-Time Object Detectors

Reference 16

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Observation 734507fc-9029-4e71-a955-3fbce8f3ecf6 · outbound

This paper cites Communication in the presence of noise,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Communication in the presence of noise,

Reference 17

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Observation 2e54e677-6ade-4918-a432-036cfe990377 · outbound

This paper cites Group normalization,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Group normalization,

Reference 18

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Observation bb0fe29d-d614-4fad-99bd-113f6c2e2773 · outbound

This paper cites Soda: A low-power architecture for software radio,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Soda: A low-power architecture for software radio,

Reference 19

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Observation 58681dea-cf55-4a28-a5a8-578a4753de1c · outbound

This paper cites Dota: A large-scale dataset for object detection in aerial images,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Dota: A large-scale dataset for object detection in aerial images,

Reference 20

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Observation d887ad11-98ac-4e86-9d3e-39a268969622 · outbound

This paper cites Fair1m: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Fair1m: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery,

Reference 21

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Observation a67fef19-a132-480b-bd98-2c412e773ec7 · outbound

This paper cites Shiprsimagenet: A large-scale fine-grained dataset for ship detection in high-resolution optical remote sensing images,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Shiprsimagenet: A large-scale fine-grained dataset for ship detection in high-resolution optical remote sensing images,

Reference 22

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Observation 3df71ae3-5a91-4dd1-91e2-e5ba85aa4841 · outbound

This paper cites Detrs beat yolos on real-time object detection,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Detrs beat yolos on real-time object detection,

Reference 23

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Observation f151b03c-4941-427e-8850-c1fee178bfab · outbound

This paper cites YOLOX: Exceeding YOLO Series in 2021.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing YOLOX: Exceeding YOLO Series in 2021

Reference 24

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Observation 1554b6d1-f919-468c-be72-5bf905d742dc · outbound

This paper cites Ultralytics yolo11,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Ultralytics yolo11,

Reference 25

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Observation f477aeec-1fb4-4f7a-8884-9a4c016092c3 · outbound

This paper cites Lmo-yolo: A ship detection model for low- resolution optical satellite imagery,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Lmo-yolo: A ship detection model for low- resolution optical satellite imagery,

Reference 26

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Observation 4fc94435-4401-4607-b83e-1cada524591c · outbound

This paper cites Yolov7-ship: A lightweight algorithm for ship object detection in complex marine environments,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Yolov7-ship: A lightweight algorithm for ship object detection in complex marine environments,

Reference 27

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Observation 017dc256-616a-4306-a981-fa6740c92eb2 · outbound

This paper cites Cm-yolo: Typical object detection method in remote sensing cloud and mist scene images,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Cm-yolo: Typical object detection method in remote sensing cloud and mist scene images,

Reference 28

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Observation ca89dfa1-4008-4068-aaba-4a3e60f38f7a · outbound

This paper cites Shadowfpn-yolo: A real- time nms-free detector for remote sensing ship detection,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Shadowfpn-yolo: A real- time nms-free detector for remote sensing ship detection,

Reference 29

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Observation 3257f130-9a66-4f18-9049-197a9269c92d · outbound

This paper cites Omni-Dimensional Dynamic Convolution.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Omni-Dimensional Dynamic Convolution

Reference 30

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Observation 61620cd8-d552-4b6b-b2fa-58732cb1a643 · outbound

This paper cites Carafe: Content-aware reassembly of features,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Carafe: Content-aware reassembly of features,

Reference 31

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Observation 2103589b-d883-4268-b67a-fdf40be189a3 · outbound

This paper cites Yolo-rsa: a multiscale ship detection algorithm based on optical remote sensing image,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Yolo-rsa: a multiscale ship detection algorithm based on optical remote sensing image,

Reference 32

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Observation 16eb4c42-92e3-40e8-8119-e65a00820a6b · outbound

This paper cites Yolo-ssboat: Super-small ship detection network for large-scale aerial and remote sensing scenes,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Yolo-ssboat: Super-small ship detection network for large-scale aerial and remote sensing scenes,

Reference 33

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Observation 6119ee41-6789-456e-894d-47abe23d0dea · outbound

This paper cites Internimage: Exploring large-scale vision foundation models with deformable convolutions,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Internimage: Exploring large-scale vision foundation models with deformable convolutions,

Reference 34

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Observation a55db1d3-a426-4211-82f9-1f21fd25a078 · outbound

This paper cites A high resolution optical satellite image dataset for ship recognition and some new baselines,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing A high resolution optical satellite image dataset for ship recognition and some new baselines,

Reference 35

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Observation 50bdaeae-749f-459c-98ec-62ce9d21c802 · outbound

This paper cites Understanding the effective receptive field in deep convolutional neural networks,.

LiM-YOLO: Less is More with Pyramid Level Shift for Ship Detection in Optical Remote Sensing Understanding the effective receptive field in deep convolutional neural networks,

Reference 36

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T17:28:21.682509Z digest=sha256:f3b1dc9c280a52c27f5356ff5a18b42381a3e11b7dec48808a873df5a738756d

Pith citing papers

No inbound Pith citation observations are available.