Paper Citation Record · LEDGER

Learning a Neural Association Network for Self-supervised Multi-Object Tracking

As of 14 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2411.11514.

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

pith.paper-citation-record.v1
2411.11514 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:32:48.907210Z

measured 44 of 44 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 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

44 of 44 outbound references displayed

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verified fuzzy31
unresolved13
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dbce4c0a-9bae-402a-8cf0-50a3da6f2046 · outbound

This paper cites Self-supervised multi-object track- ing with cross-input consistency.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Self-supervised multi-object track- ing with cross-input consistency

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.441793Z

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Observation 8d385b48-1e3e-4f01-9116-e3802c5bcd47 · outbound

This paper cites Tracking without bells and whistles.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Tracking without bells and whistles

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.429544Z

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Observation 4fb95466-c155-41db-b93c-4238e6d64df4 · outbound

This paper cites Evaluating multiple object tracking perfor- mance: the clear mot metrics.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Evaluating multiple object tracking perfor- mance: the clear mot metrics

Reference 3

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Observation bda13dca-fe9d-4d8c-9e15-31f612a20378 · outbound

This paper cites Simple online and realtime tracking.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Simple online and realtime tracking

Reference 4

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Observation cf856e30-c125-4698-a173-c4117320a65e · outbound

This paper cites PKF: Probabilistic Data Association Kalman Filter for Multi-Object Tracking.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking PKF: Probabilistic Data Association Kalman Filter for Multi-Object Tracking

Reference 5

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Observation 65f426d8-421c-4fb0-b292-9bb5002b76f2 · outbound

This paper cites Unifying short and long-term tracking with graph hierarchies.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Unifying short and long-term tracking with graph hierarchies

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.398282Z

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Observation ca9c1536-82af-4844-a575-ae41d92e793f · outbound

This paper cites CVPR19 Tracking and Detection Challenge: How crowded can it get?.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking CVPR19 Tracking and Detection Challenge: How crowded can it get?

Reference 7

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Observation 451a4147-4433-4e6a-ad8f-758ce8ebf52f · outbound

This paper cites Ob- ject detection with discriminatively trained part-based models.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Ob- ject detection with discriminatively trained part-based models

Reference 8

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Observation cbbbe917-b015-480f-aa05-ad00fef10ac0 · outbound

This paper cites Memotr: Long-term memory-augmented transformer for multi-object tracking.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Memotr: Long-term memory-augmented transformer for multi-object tracking

Reference 9

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Observation 3c4a1eaa-7c2d-4f6d-b91f-dc7bc7549915 · outbound

This paper cites Deep residual learning for image recognition.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Deep residual learning for image recognition

Reference 10

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Source-reported events for the cited work

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Observation b1034055-1c1f-4107-9910-8ce6a5aa3fec · outbound

This paper cites High-speed tracking with kernelized correlation filters.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking High-speed tracking with kernelized correlation filters

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.360815Z

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Observation 2d0d0483-e470-4464-9254-ba662ff39bc7 · outbound

This paper cites A two-stage minimum cost multicut approach to self-supervised multiple person tracking.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking A two-stage minimum cost multicut approach to self-supervised multiple person tracking

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.348578Z

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Observation e7cc23c9-3fb1-4408-804f-178d603aa3f2 · outbound

This paper cites Lifted disjoint paths with application in multiple object tracking.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Lifted disjoint paths with application in multiple object tracking

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.337501Z

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Observation 7660130e-f586-4a1b-a8e9-d7cef30ec271 · outbound

This paper cites Making higher order mot scalable: An efficient approximate solver for lifted disjoint paths.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Making higher order mot scalable: An efficient approximate solver for lifted disjoint paths

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.325553Z

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Observation a62200b7-6c8b-459e-b4cf-23dcf07d7171 · outbound

This paper cites Simple Unsupervised Multi-Object Tracking.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Simple Unsupervised Multi-Object Tracking

Reference 15

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Observation 64bbdce7-e655-4fe9-a684-362c6b8b7722 · outbound

This paper cites Multiple hypothesis tracking revisited.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Multiple hypothesis tracking revisited

Reference 16

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Observation 8a71bdca-d1a6-4816-854f-ebda1f413291 · outbound

This paper cites Multi-object tracking with neural gating using bilinear lstm.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Multi-object tracking with neural gating using bilinear lstm

Reference 17

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Observation 3f2805fe-afd1-4c62-9f60-c8f67de338c1 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Adam: A Method for Stochastic Optimization

Reference 18

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Observation 6a8c97df-ad48-4bd8-a42f-7cb41118db20 · outbound

This paper cites Deep Kalman Filters.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Deep Kalman Filters

Reference 19

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Observation eb3ca1c0-81ac-475c-8246-cc8e916e4a5a · outbound

This paper cites The hungarian method for the assignment problem.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking The hungarian method for the assignment problem

Reference 20

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Observation ab2680e6-d496-42f5-9c39-b770cfe88b44 · outbound

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Learning a Neural Association Network for Self-supervised Multi-Object Tracking Unresolved cited work

Reference 21

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Observation baa288b0-05b8-40c2-9cde-e5c0ce83b342 · outbound

This paper cites Learning of global objective for network flow in multi-object tracking.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Learning of global objective for network flow in multi-object tracking

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.270993Z

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Observation 3e76ea0c-f3e6-43a4-92fa-23d9e489d27f · outbound

This paper cites Unsupervised multi-object tracking via dynamical vae and variational inference.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Unsupervised multi-object tracking via dynamical vae and variational inference

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.255958Z

Source-reported events for the cited work

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Observation d5a9a9b7-2c17-413d-bfd2-21f3d200f437 · outbound

This paper cites Uncertainty- aware unsupervised multi-object tracking.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Uncertainty- aware unsupervised multi-object tracking

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.244772Z

Source-reported events for the cited work

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Observation 92ca221a-72fb-4567-a75a-0bc0610c60d8 · outbound

This paper cites Online multi-object tracking with unsupervised re-identification learning and occlusion estimation.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Online multi-object tracking with unsupervised re-identification learning and occlusion estimation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.232844Z

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Observation 7c34e60b-0571-40f8-b630-45621112fb20 · outbound

This paper cites Self-supervised multi-object tracking with path consistency.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Self-supervised multi-object tracking with path consistency

Reference 26

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Observation 49d81b76-7ef7-4c32-8176-5b31d69051e2 · outbound

This paper cites Hota: A higher order metric for evaluating multi-object tracking.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Hota: A higher order metric for evaluating multi-object tracking

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.208077Z

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Observation b4b00ae7-c442-434c-8977-4298f87100e3 · outbound

This paper cites Trackformer: Multi-object tracking with transformers.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Trackformer: Multi-object tracking with transformers

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.195437Z

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Observation d45d4705-70be-46af-be1e-9e39a54ba680 · outbound

This paper cites Learning latent permutations with gumbel-sinkhorn networks.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Learning latent permutations with gumbel-sinkhorn networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.182912Z

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

Observation 50e96bf1-425b-47e2-bcfa-f585ba85a384 · outbound

This paper cites Tracking without label: Unsuper- vised multiple object tracking via contrastive similarity learning.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Tracking without label: Unsuper- vised multiple object tracking via contrastive similarity learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.169809Z

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Observation 410ec021-f00e-40ad-ade0-b4fabda2f742 · outbound

This paper cites Learning data as- sociation for multi-object tracking using only coordinates.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Learning data as- sociation for multi-object tracking using only coordinates

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.155640Z

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Observation e51e016b-3795-4741-9c75-c2f18444a351 · outbound

This paper cites MOT16: A Benchmark for Multi-Object Tracking.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking MOT16: A Benchmark for Multi-Object Tracking

Reference 32

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Observation 7d93402e-df70-4499-b6df-a12bd3a60fb0 · outbound

This paper cites Quasi-dense similarity learning for multiple object tracking.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Quasi-dense similarity learning for multiple object tracking

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.143813Z

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

Observation 26e4c6c4-ff97-4c66-aab7-f5bb7f9b68a5 · outbound

This paper cites Maximum likelihood estimates of linear dynamic systems.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Maximum likelihood estimates of linear dynamic systems

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.132318Z

Source-reported events for the cited work

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Observation 24e8d79d-a8c4-4a24-82b3-38554b411951 · outbound

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

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Faster r-cnn: Towards real- time object detection with region proposal networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.120746Z

Source-reported events for the cited work

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Observation 8479da20-b944-48cd-9d4c-364f3bbc056b · outbound

This paper cites Probabilistic tracklet scoring and inpainting for multiple object track- ing.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Probabilistic tracklet scoring and inpainting for multiple object track- ing

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.107971Z

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Observation c2917e95-2d25-42ed-8b3e-1fbe8e8739b7 · outbound

This paper cites A relationship between arbitrary positive matrices and doubly stochastic matrices.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking A relationship between arbitrary positive matrices and doubly stochastic matrices

Reference 37

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Source-reported events for the cited work

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Observation 474de073-99d8-470c-a92c-31774f137779 · outbound

This paper cites Simple online and realtime track- ing with a deep association metric.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Simple online and realtime track- ing with a deep association metric

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.082014Z

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.

Observation cebb71f0-0b9a-4b21-b6b2-c13a9dfe5e98 · outbound

This paper cites Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.069110Z

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.

Observation 8352f330-690e-4e32-88d1-3b4aa142c9c9 · outbound

This paper cites Hard to track ob- jects with irregular motions and similar appearances? make it easier by buffering the matching space.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Hard to track ob- jects with irregular motions and similar appearances? make it easier by buffering the matching space

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.057512Z

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.

Observation 44bcdfdc-966d-4016-badb-a999870df18c · outbound

This paper cites Bdd100k: A diverse driving dataset for hetero- geneous multitask learning.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Bdd100k: A diverse driving dataset for hetero- geneous multitask learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.041727Z

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.

Observation 2bf3d432-9c28-40b5-93dd-cabfa81238ea · outbound

This paper cites Motr: End-to-end multiple-object tracking with transformer.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Motr: End-to-end multiple-object tracking with transformer

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.029495Z

Source-reported events for the cited work

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Observation 17c74a1b-1581-4f7c-abf9-01c89db301c0 · outbound

This paper cites Bytetrack: Multi-object tracking by associating every detection box.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Bytetrack: Multi-object tracking by associating every detection box

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T18:32:48.902790Z

Source-reported events for the cited work

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Observation c09ef3e8-41ad-46eb-a812-460547ae939b · outbound

This paper cites Motrv2: Bootstrapping end-to-end multi-object tracking by pretrained object detectors.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking Motrv2: Bootstrapping end-to-end multi-object tracking by pretrained object detectors

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:32:49.010696Z

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.

Pith citing papers

No inbound Pith citation observations are available.