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Paper Citation Record · LEDGER

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking

As of 20 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2411.17251.

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

pith.paper-citation-record.v1
2411.17251 v8

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:23:39.091947Z

measured 37 of 37 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

37 of 37 outbound references displayed

  • verified exact8
  • verified fuzzy1
  • unresolved20
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch8

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bd7892c6-9107-43fd-bb4a-55a5d640ff4b · outbound

This paper cites YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T12:23:38.598830Z digest=sha256:1c8e5d311545d535a3d6b0440186573bcded6dce3ea04d8e59a5d11c012eab2c

Observation 2740e331-50b2-4c11-b834-6f1e4e16c027 · outbound

This paper cites Sah Bin Haji Salam, Usman Ullah Sheikh, and Sara Ayub.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Sah Bin Haji Salam, Usman Ullah Sheikh, and Sara Ayub

Reference 2

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metadata mismatch
raw_fallback, observed 2026-08-12T12:23:41.725574Z

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.

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Observation 6f97cbba-22c4-4dd7-94ec-b1d7653bab23 · outbound

This paper cites Efficient Visual Tracking With Exemplar Transformers.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Efficient Visual Tracking With Exemplar Transformers

Reference 3

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Observation c2e66360-6c5e-4b24-8d28-a84839040916 · outbound

This paper cites an unresolved cited work.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Unresolved cited work

Reference 4

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raw_fallback, observed 2026-08-12T12:23:41.494211Z

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.

source=arxiv_source observed=2026-08-12T12:23:38.697718Z digest=sha256:6f045e99ead72f9ebdc07366e944b7e6b011d2fea0c17515558b8ed460d59884

Observation 3e433124-191a-4115-a741-1bdf4cd2164c · outbound

This paper cites 3d multi-object tracking using graph neural networks with cross-edge modality attention.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking 3d multi-object tracking using graph neural networks with cross-edge modality attention

Reference 5

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source=arxiv_source observed=2026-08-12T12:23:38.702726Z digest=sha256:02d7ab262f281501cc320f912069a96512d0d3cc482764e62c14d7b9d5285118

Observation 890647d4-c266-4470-b257-686a06873b99 · outbound

This paper cites End-to-End Object Detection with Transformers.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking End-to-End Object Detection with Transformers

Reference 6

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source=arxiv_source observed=2026-08-12T12:23:38.707144Z digest=sha256:c9eabd9181985088921f8a31da5a8900905d6dd8158384138a4adfcc5d976329

Observation 2b95a351-35a5-4b12-abcd-4821892d86c0 · outbound

This paper cites Object Detection in Remote Sensing Images Based on a Scene - Contextual Feature Pyramid Network.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Object Detection in Remote Sensing Images Based on a Scene - Contextual Feature Pyramid Network

Reference 7

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verified exact
doi, observed 2026-08-12T12:23:39.534681Z

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.

source=arxiv_source observed=2026-08-12T12:23:38.712550Z digest=sha256:9f5d93f1f16ccafa0df9a2c8d28909eb0c163245aa35e35cfdcb7bf5c376d747

Observation 32691585-776f-49ec-8178-97d9af16bafe · outbound

This paper cites Transformer Tracking.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Transformer Tracking

Reference 8

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no resolver link, observed 2026-08-12T12:23:38.717574Z

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source=arxiv_source observed=2026-08-12T12:23:38.717574Z digest=sha256:905b86923f0404d0693be6e30670765ab8348b10b87aa61dec29389c709a606e

Observation 633ea645-f02b-4715-ab20-b4e7bd7387c3 · outbound

This paper cites Online Multi - Object Tracking Using CNN - Based Single Object Tracker With Spatial - Temporal Attention Mechanism.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Online Multi - Object Tracking Using CNN - Based Single Object Tracker With Spatial - Temporal Attention Mechanism

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-12T12:23:41.773277Z

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.

source=arxiv_source observed=2026-08-12T12:23:38.722323Z digest=sha256:8c4532934b7ae62ede120362bf44d7857b7ccbbf2743e1af5456265adacfd367

Observation 7a1a42f8-b572-4b6a-938b-759de63a40eb · outbound

This paper cites Histograms of oriented gradients for human detection.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Histograms of oriented gradients for human detection

Reference 10

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Unavailable: canonical work link unavailable.

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Observation bbe13db5-5d74-4731-a6d8-17bd25af311d · outbound

This paper cites A Survey on Artificial Intelligence ( AI ) and eXplainable AI in Air Traffic Management : Current Trends and Development with Future Research Trajectory.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking A Survey on Artificial Intelligence ( AI ) and eXplainable AI in Air Traffic Management : Current Trends and Development with Future Research Trajectory

Reference 11

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verified exact
doi, observed 2026-08-12T12:23:41.757897Z

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.

source=arxiv_source observed=2026-08-12T12:23:38.733429Z digest=sha256:35156c232e81d5617449a0c87c69f996f9ab3af663ef2675eb9e755bd8703873

Observation 80003ab0-48bf-453d-bb51-cbf1a8032572 · outbound

This paper cites Bifpn-yolo: One-stage object detection integrating bi-directional feature pyramid networks.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Bifpn-yolo: One-stage object detection integrating bi-directional feature pyramid networks

Reference 12

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T12:23:38.737855Z digest=sha256:36907b3d555cd7b2bf87e872deaa9ec90b15a0194300edaa22d582eb6a440003

Observation 8778d74a-f460-4e16-82a9-0a563299a6b5 · outbound

This paper cites Intelligent transportation systems for sustainable smart cities.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Intelligent transportation systems for sustainable smart cities

Reference 13

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no resolver link, observed 2026-08-12T12:23:38.742255Z

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Unavailable: canonical work link unavailable.

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Observation d87ddc0e-11cc-4223-bc4c-3f4e18c936a4 · outbound

This paper cites Handcrafted and Deep Trackers : Recent Visual Object Tracking Approaches and Trends.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Handcrafted and Deep Trackers : Recent Visual Object Tracking Approaches and Trends

Reference 14

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verified exact
doi, observed 2026-08-12T12:23:39.509236Z

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.

source=arxiv_source observed=2026-08-12T12:23:38.746643Z digest=sha256:1a6f33f55d38fa4177fa0d4598a302bc354ec8835e36142ded7f46502511b110

Observation bab88706-4bc0-4286-a120-a79a44fc4df0 · outbound

This paper cites Enabling Safe Autonomous Driving in Real - World City Traffic Using Multiple Criteria Decision Making.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Enabling Safe Autonomous Driving in Real - World City Traffic Using Multiple Criteria Decision Making

Reference 15

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metadata mismatch
raw_fallback, observed 2026-08-12T12:23:41.017834Z

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.

source=arxiv_source observed=2026-08-12T12:23:38.751762Z digest=sha256:c744425d856877dbbfb70a99c55780fd7631c11eccb464189dd583cbd12a34bb

Observation 3a7c6a9a-26d1-4624-a8ca-b5da2b0d6db0 · outbound

This paper cites Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking

Reference 16

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local_arxiv, observed 2026-08-12T12:23:39.493397Z

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.

source=arxiv_source observed=2026-08-12T12:23:38.771763Z digest=sha256:5c9bed7a27f05417367cd03856726c25ec2f248214e90c11619b9f35f5629e1e

Observation 0d544b1b-37c8-42a9-872a-7e6859863ddb · outbound

This paper cites Graph convolution neural network-based data association for online multi-object tracking.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Graph convolution neural network-based data association for online multi-object tracking

Reference 17

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raw_fallback, observed 2026-08-12T12:23:40.907178Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-12T12:23:38.822560Z digest=sha256:09f2c4cb7936e427391f670fe98ca1d6e5af44b98c53cad28146cc3e47ea4857

Observation e6720252-d54b-48e7-8ed6-d9a06d1b705f · outbound

This paper cites SwinTrack: A Simple and Strong Baseline for Transformer Tracking.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking SwinTrack: A Simple and Strong Baseline for Transformer Tracking

Reference 18

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation df79b557-873e-43b7-8ed3-796a07b13a29 · outbound

This paper cites Focal loss for dense object detection.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Focal loss for dense object detection

Reference 19

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Source-reported events for the cited work

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Observation d78e84bd-903d-4a43-8b6b-acbf30dfba9b · outbound

This paper cites SSD : Single Shot MultiBox Detector.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking SSD : Single Shot MultiBox Detector

Reference 20

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source=arxiv_source observed=2026-08-12T12:23:38.991210Z digest=sha256:20581540c2b0d51b334764a7ff0de6db6f637fe4f09d363c55c6360bf6df5acf

Observation e4c03aa1-4d73-4406-be38-9646c18e5dfa · outbound

This paper cites OD - XAI : Explainable AI - Based Semantic Object Detection for Autonomous Vehicles.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking OD - XAI : Explainable AI - Based Semantic Object Detection for Autonomous Vehicles

Reference 21

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verified exact
doi, observed 2026-08-12T12:23:39.460757Z

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.

source=arxiv_source observed=2026-08-12T12:23:39.015655Z digest=sha256:6471a308a8ce846c50ac781561cc6fa115cc493a27ddc79294e77449127c7527

Observation 0c925cf0-1db3-4100-a157-c823e511a899 · outbound

This paper cites Deep Learning for Visual Tracking : A Comprehensive Survey.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Deep Learning for Visual Tracking : A Comprehensive Survey

Reference 22

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source=arxiv_source observed=2026-08-12T12:23:39.020943Z digest=sha256:c08a3a5e0e478af4363ea4577be449de5fc5d331c8862c0f8c67008a2fa46aff

Observation b5c1bc8c-b2b0-48d4-a607-60e7a6f4dd22 · outbound

This paper cites You Only Look Once: Unified, Real-Time Object Detection.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking You Only Look Once: Unified, Real-Time Object Detection

Reference 23

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source=arxiv_source observed=2026-08-12T12:23:39.024968Z digest=sha256:cc732667d78b283ec486d484b03d115c2d67251c6fd185acc6204b0dc04da7eb

Observation 49a59f9b-5c46-47cb-b0e6-a6a9aea8fe7a · outbound

This paper cites Faster R - CNN : Towards Real - Time Object Detection with Region Proposal Networks.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Faster R - CNN : Towards Real - Time Object Detection with Region Proposal Networks

Reference 24

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Observation aba49c8c-33c6-4eb4-9989-8cdc487f9328 · outbound

This paper cites an unresolved cited work.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Unresolved cited work

Reference 25

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source=arxiv_source observed=2026-08-12T12:23:39.034827Z digest=sha256:71940a9cce14a37694eff18f71bfb3ebbc930089f3a4f0356877f5cc66d74b51

Observation d3e7f488-c8cb-4c28-94c7-dc6e34f91616 · outbound

This paper cites RSOD : Real -time small object detection algorithm in UAV -based traffic monitoring.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking RSOD : Real -time small object detection algorithm in UAV -based traffic monitoring

Reference 26

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verified exact
doi, observed 2026-08-12T12:23:39.399914Z

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.

source=arxiv_source observed=2026-08-12T12:23:39.039818Z digest=sha256:b2f431f9fb96281bc8d7bec3c261c83f5e782b6fca732755345d89eb3a2c1748

Observation d87e7d3c-c03c-4b3f-9488-8176dda7f98b · outbound

This paper cites Rapid object detection using a boosted cascade of simple features.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Rapid object detection using a boosted cascade of simple features

Reference 27

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T12:23:39.045149Z digest=sha256:4c4f87e2bee5954b7755b917415a56eb891fc8fd489a419bddb98e88df6c73ac

Observation 40c343f5-59fd-4cc1-8cd8-e7406f3e7b0b · outbound

This paper cites Yolo-anti: Yolo-based counterattack model for unseen congested object detection.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Yolo-anti: Yolo-based counterattack model for unseen congested object detection

Reference 28

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metadata mismatch
raw_fallback, observed 2026-08-12T12:23:40.286994Z

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.

source=arxiv_source observed=2026-08-12T12:23:39.049754Z digest=sha256:93817a6e2155ae1eb8d447faf22534d33dea695850e7a5a14c30b22ad06eb48f

Observation d4b288ff-ef11-41f8-858d-40c4c8f58274 · outbound

This paper cites Fast online object tracking and segmentation: A unifying approach.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Fast online object tracking and segmentation: A unifying approach

Reference 29

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no resolver link, observed 2026-08-12T12:23:39.054204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T12:23:39.054204Z digest=sha256:233d967897bedc13a164b68d0b9c115b088614988ee38ab55e6b5c9b06119dda

Observation 2ab29c0c-3376-46fe-9baa-bff8a06a188e · outbound

This paper cites Towards Real-Time Multi-Object Tracking.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Towards Real-Time Multi-Object Tracking

Reference 30

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verified exact
doi, observed 2026-08-12T12:23:39.287115Z

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.

source=arxiv_source observed=2026-08-12T12:23:39.058604Z digest=sha256:5dded45f421cef8a662df97ab250aef598d1f840e04f87b7316bc529dd05410a

Observation 6a34104e-a77e-4880-9177-5ae3912940e1 · outbound

This paper cites Gnn3dmot: Graph neural network for 3d multi-object tracking with 2d-3d multi-feature learning.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Gnn3dmot: Graph neural network for 3d multi-object tracking with 2d-3d multi-feature learning

Reference 31

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unresolved
no resolver link, observed 2026-08-12T12:23:39.063681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T12:23:39.063681Z digest=sha256:9ee1b5dd487e26d5111c7242ec044c77c0fd4def4744d3af3f9802b9421d03d9

Observation 2c7f2077-34e7-42c3-8f52-0570d3df92ec · outbound

This paper cites Advances in Convolutional Neural Networks for Object Detection and Recognition.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Advances in Convolutional Neural Networks for Object Detection and Recognition

Reference 32

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T12:23:39.068024Z digest=sha256:7d80d1a73d59fc6b08fa728f510a293c6e7b70e1af149de245b49be7ab831e4a

Observation 789de97f-c11e-486b-b928-8a28a4e48432 · outbound

This paper cites Camouflaged Object Detection via Dual-branch Fusion and Dual Self-similarity constraints.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Camouflaged Object Detection via Dual-branch Fusion and Dual Self-similarity constraints

Reference 33

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raw_fallback, observed 2026-08-12T12:23:39.877242Z

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.

source=arxiv_source observed=2026-08-12T12:23:39.072802Z digest=sha256:8fa433fc231f45039dc18108ce4c176c9597d20314bac5d45b27a5f43c3e1ff8

Observation d76e13bf-b031-42e5-973e-e675f5be3c9e · outbound

This paper cites Temporal dynamic graph lstm for action-driven video object detection.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Temporal dynamic graph lstm for action-driven video object detection

Reference 34

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doi, observed 2026-08-12T12:23:39.164408Z

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.

source=arxiv_source observed=2026-08-12T12:23:39.077724Z digest=sha256:e6c056c9d7dea2d68cb1495135f9aacd56b61e27ccc09b104e7403e5d39a5c7b

Observation ec73a3f2-cd73-4cbd-bca9-86840e9d349a · outbound

This paper cites SCGTracker : Spatio-temporal correlation and graph neural networks for multiple object tracking.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking SCGTracker : Spatio-temporal correlation and graph neural networks for multiple object tracking

Reference 35

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metadata mismatch
raw_fallback, observed 2026-08-12T12:23:39.759137Z

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.

source=arxiv_source observed=2026-08-12T12:23:39.082547Z digest=sha256:5a5a1420ef7b5e5a52ff11012773c898f5243bcdae12a703d3bc83c8be9577d6

Observation ed3195c4-0430-4293-91af-5aa5596cec87 · outbound

This paper cites Fairmot: On the fairness of detection and re-identification in multiple object tracking.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Fairmot: On the fairness of detection and re-identification in multiple object tracking

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-12T12:23:39.087239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T12:23:39.087239Z digest=sha256:a0f6815fd3e0c8d90f3c54df4e40f998bb69dd5d985556cfd8d2f12a967678d6

Observation d6662389-c0d9-48fd-bbcb-515e21442b80 · outbound

This paper cites Dehazing & Reasoning YOLO: Prior knowledge-guided network for object detection in foggy weather.

Interpretable Dynamic Graph Neural Networks for Small Occluded Object Detection and Tracking Dehazing & Reasoning YOLO: Prior knowledge-guided network for object detection in foggy weather

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-12T12:23:39.091947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T12:23:39.091947Z digest=sha256:ebeb65960ebe2f839e518b72e69c59e93b80ba1270717c77e4158f481749a0fd

Pith citing papers

No inbound Pith citation observations are available.