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Paper Citation Record · LEDGER

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos

As of 14 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2506.20550.

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

pith.paper-citation-record.v1
2506.20550 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:50:43.557595Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:35:47.049695Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T00:35:47.143270Z

Reference resolution

26 of 26 outbound references displayed

  • verified exact1
  • verified fuzzy15
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 819cf284-94c8-4197-9474-47130028480b · outbound

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

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,

Reference 1

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no resolver link, observed 2026-08-06T22:50:40.128704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d675ffdc-ce47-4dc7-94cf-d828bbde0c24 · outbound

This paper cites Recurrent neural networks for video object detection,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Recurrent neural networks for video object detection,

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-06T22:50:45.977906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 7a2d0391-6822-4c50-ac39-10360c64c210 · outbound

This paper cites Flow-guided feature aggregation for video object detection,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Flow-guided feature aggregation for video object detection,

Reference 3

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raw_fallback, observed 2026-08-06T22:50:45.963750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 5f51163e-2f4e-49bb-a51c-149cb56211ce · outbound

This paper cites Sequence level seman- tics aggregation for video object detection,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Sequence level seman- tics aggregation for video object detection,

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-06T22:50:45.949297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation aae06bcf-f8d0-49da-a300-b78902796f9c · outbound

This paper cites Slowfast networks for video recognition,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Slowfast networks for video recognition,

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:50:40.676193Z digest=sha256:19e372e836eb698dd13aaee46838a546a44f843462633260ff6efd927a8ef396

Observation 2e603338-f870-43fd-b7cc-3e32a8c12652 · outbound

This paper cites A brief introduction to weakly supervised learning,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos A brief introduction to weakly supervised learning,

Reference 6

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no resolver link, observed 2026-08-06T22:50:40.812950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f543736c-604c-4501-845b-1d477d804e65 · outbound

This paper cites MOT20: A benchmark for multi object tracking in crowded scenes.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos MOT20: A benchmark for multi object tracking in crowded scenes

Reference 7

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unresolved
no resolver link, observed 2026-08-06T22:50:41.211905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d3cd499d-a79e-4c78-86a9-3f3421c1317e · outbound

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

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Faster r-cnn: Towards real- time object detection with region proposal networks,

Reference 8

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raw_fallback, observed 2026-08-06T22:50:45.881151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 8bcca8c7-d2a2-4ae9-8915-d5c1d56c94af · outbound

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

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos You only look once: Unified, real-time object detection,

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-06T22:50:45.577983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation aac0882c-a9c4-436a-81a8-b3f5b714930f · outbound

This paper cites Focal loss for dense object detection,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Focal loss for dense object detection,

Reference 10

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 6e3995a0-7f80-4580-926d-c92be1bb7f55 · outbound

This paper cites A detailed study of the association task in tracking-by- detection-based multi-person tracking,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos A detailed study of the association task in tracking-by- detection-based multi-person tracking,

Reference 11

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raw_fallback, observed 2026-08-06T22:50:45.091877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T22:50:42.340216Z digest=sha256:d7f22740b4f23b6e70fbe554389e48e77491bb0777bdd342792b5a03aa13d0ad

Observation c8bde5fb-6667-432d-84d3-43eb09eb114f · outbound

This paper cites Video object detection with an aligned spatial-temporal memory,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Video object detection with an aligned spatial-temporal memory,

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-06T22:50:44.961372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation a184505b-27c6-4736-8ccb-45cd45e4f3b8 · outbound

This paper cites Learning recurrent memory activation networks for visual tracking,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Learning recurrent memory activation networks for visual tracking,

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-06T22:50:44.800024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T22:50:42.505935Z digest=sha256:ef1ecc9e2cdc4778d5b93e42c3f17bba78786095d92cbfd68e9e8dae60a264a6

Observation 92a80f96-5f9d-4ec5-a1c4-0e24d7b3bb36 · outbound

This paper cites Video visual relation detection via 3d convolutional neural network,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Video visual relation detection via 3d convolutional neural network,

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-06T22:50:44.670775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T22:50:42.616257Z digest=sha256:a8d7a61e8c8f84158a1e03976c644605019b107776c96c124c1b7ffe71b55b1c

Observation 95f5e276-5813-43e9-815a-5a3f918887ad · outbound

This paper cites An Efficient 3D CNN for Action/Object Segmentation in Video.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos An Efficient 3D CNN for Action/Object Segmentation in Video

Reference 15

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verified exact
local_arxiv, observed 2026-08-06T22:50:43.708852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T22:50:42.721245Z digest=sha256:8ca282e92269236ca1b326aa79cf3abe596070f3aa17b6e418207f68319824c6

Observation 78cf56d1-e896-4a85-8bfb-0d187447940d · outbound

This paper cites New generation deep learning for video object detection: A survey,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos New generation deep learning for video object detection: A survey,

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-06T22:50:44.525546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T22:50:42.837489Z digest=sha256:3d3d2496f4b26fb3318b86ce088ff3c557a47c8778bfce4628bba000ac0891ed

Observation b89d4ad5-65cc-4e4c-a04f-d3ad2dfaf949 · outbound

This paper cites Label- efficient online continual object detection in streaming video,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Label- efficient online continual object detection in streaming video,

Reference 17

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raw_fallback, observed 2026-08-06T22:50:44.368875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T22:50:42.896913Z digest=sha256:fd34b34ff9c99fc4421035ab8b475f97a878849f1b867c8cb6752857e66083c4

Observation e834f025-55b6-486d-a7c4-ceeda7efb76a · outbound

This paper cites A review of video object detection: Datasets, metrics and methods,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos A review of video object detection: Datasets, metrics and methods,

Reference 18

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raw_fallback, observed 2026-08-06T22:50:44.195751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation f2c17c79-b7af-4a9c-9df3-2dc4c0f6a602 · outbound

This paper cites Yolov: Making still image object detectors great at video object detection,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Yolov: Making still image object detectors great at video object detection,

Reference 19

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verified fuzzy
raw_fallback, observed 2026-08-06T22:50:44.098627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T22:50:43.010015Z digest=sha256:3b0939f2e998ea4edec06a6a6a09a273dee76d67241b31ec5f7305c46c597acf

Observation 8c45a11b-ce2c-4d9d-b860-161963fdda8c · outbound

This paper cites Seadronessee: A maritime benchmark for detecting humans in open water,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Seadronessee: A maritime benchmark for detecting humans in open water,

Reference 20

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raw_fallback, observed 2026-08-06T22:50:43.926569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T22:50:43.046876Z digest=sha256:3148c7b2a42758c9a2188f92bdf83700477cb32c68af3687739aa0f077ffb426

Observation e55fc948-bcba-4147-aaee-ec7062ebcbee · outbound

This paper cites The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024

Reference 21

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no resolver link, observed 2026-08-06T22:50:43.153310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:50:43.153310Z digest=sha256:263b38c5cde0a13c3bb2fc56134504f375cdc92852bc71f0076d16105cb6f4c0

Observation aeb32941-9aeb-4d57-9a83-44795dc8bf24 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 22

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no resolver link, observed 2026-08-06T22:50:43.245635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:50:43.245635Z digest=sha256:efcd306bc5e379d0b9d8cf02a3a1369c8d420d6b1171af9e60524ba83b4ddbb8

Observation 299c51e6-379c-4aff-acb1-8f1316e73b23 · outbound

This paper cites Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks,

Reference 23

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no resolver link, observed 2026-08-06T22:50:43.305234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:50:43.305234Z digest=sha256:8949251ca433bb180e104cf5d943e5eeb0f360d0cd107254968fc664f51ac183

Observation 5263cb3c-6fb4-4250-80cf-b4fd45eaecf9 · outbound

This paper cites Eigen-cam: Class activation map using principal components,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Eigen-cam: Class activation map using principal components,

Reference 24

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no resolver link, observed 2026-08-06T22:50:43.369754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:50:43.369754Z digest=sha256:a1ca2063fd6899a97972c1cd35e5944294ce87cf45c2a367186ca6cfda179a46

Observation ef6b1acd-d479-47ce-bead-59397ff12127 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Imagenet classification with deep convolutional neural networks,

Reference 25

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no resolver link, observed 2026-08-06T22:50:43.468469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:50:43.468469Z digest=sha256:e60236065128bff9afe7969d210cc9545e382a118bccb113ed8a1ed4fc910fa6

Observation 76791451-a0dc-42d6-a6cd-5ca0fc1fd5b0 · outbound

This paper cites Microsoft coco: Common objects in context,.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos Microsoft coco: Common objects in context,

Reference 26

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no resolver link, observed 2026-08-06T22:50:43.557595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:50:43.557595Z digest=sha256:1a7245c26ec9ef57dcfa50d90ef08c18a6e5b2a702aef75d81228f54431adf8c

Pith citing papers

Observation b1089453-a262-48d6-833b-9c01d58f9341 · inbound

A Review of Vision-Based Vehicle Detection for UAV-Based Traffic Monitoring: Experimental Insights and Future Directions cites this paper.

A Review of Vision-Based Vehicle Detection for UAV-Based Traffic Monitoring: Experimental Insights and Future Directions Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos

Reference 140

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verified exact
local_arxiv, observed 2026-08-11T00:35:47.149758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T00:35:47.049695Z digest=sha256:d10b85fb69a93c8d92842e7a4a26c7b6050c8686428cb0bf55ea2e03dcfd89d5