Paper Citation Record · LEDGER

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking

As of 4 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2605.09858.

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

pith.paper-citation-record.v1
2605.09858 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-12T04:53:56.917355Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T04:53:56.917355Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-12T05:51:24.385703Z

Reference resolution

33 of 33 outbound references displayed

verified exact4
verified fuzzy26
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 836c5399-dab8-4fd3-a94b-ba10849137af · outbound

This paper cites Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-05-12T05:51:24.389607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d03264ab-97b3-465e-9bc3-6485f56a5d54 · outbound

This paper cites an unresolved cited work.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-05-12T13:06:35.558305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 08649e9e-5984-4b15-9d97-f17e9afe6661 · outbound

This paper cites Problem Setting We formulateclip-level active learningfor multi-frame end- to-end MOT, employing fixed-length clips as the acquisition unit.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Problem Setting We formulateclip-level active learningfor multi-frame end- to-end MOT, employing fixed-length clips as the acquisition unit

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.544905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1c400d4e-22ff-4f92-8749-854a6c84e7d8 · outbound

This paper cites Due to space con- straints, we report the quantitative comparisons in this sec- tion.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Due to space con- straints, we report the quantitative comparisons in this sec- tion

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.562768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0733d55f-8905-44b9-964b-4ef89af166aa · outbound

This paper cites an unresolved cited work.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-05-12T13:06:35.536059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d6b0093f-d2d3-46ab-85c1-4203a1761939 · outbound

This paper cites Bdd100k: A diverse driving dataset for heterogeneous mul- titask learning.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Bdd100k: A diverse driving dataset for heterogeneous mul- titask learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.554044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c40ae8db-b62e-43a8-a519-fcd09c06fcf5 · outbound

This paper cites Sportsmot: A large multi-object tracking dataset in multiple sports scenes.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Sportsmot: A large multi-object tracking dataset in multiple sports scenes

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.567440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d53c68ab-e67f-4666-a719-ddd9607ce3ef · outbound

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

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Trackformer: Multi-object tracking with transformers

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.540237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fca2d44b-5e9d-473c-9536-b6a30b5fc639 · outbound

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

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Motr: End-to-end multiple- object tracking with transformer

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.549567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation df60be03-3dc7-4e9b-90c5-e67b5d7f0955 · outbound

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

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Memotr: Long-term memory-augmented transformer for multi-object tracking

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.509829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4d336360-6022-4719-bc2b-707dcdad65a8 · outbound

This paper cites Samba: Synchronized set- of-sequences modeling for multiple object tracking.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Samba: Synchronized set- of-sequences modeling for multiple object tracking

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.523377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8a8387ee-28de-43ac-9910-2e749c09df71 · outbound

This paper cites Cost-effective active learning for deep image classifica- tion.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Cost-effective active learning for deep image classifica- tion

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.532126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 04e1564d-931e-44e9-add9-e9dd3d0e4a38 · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:51:24.364966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ab80530f-ce73-4028-a90f-49842f525b6c · outbound

This paper cites Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:51:24.369717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 22828ab1-6123-4834-84a6-cd1ff2e72f70 · outbound

This paper cites Are all frames equal? active sparse labeling for video action detection.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Are all frames equal? active sparse labeling for video action detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.496442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fbd37d69-8d8a-451e-bb4b-125c5fe8dd05 · outbound

This paper cites Hybrid active learning via deep clustering for video action detection.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Hybrid active learning via deep clustering for video action detection

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.518756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d14fd5d7-2a28-412b-901e-ac8abdf6b035 · outbound

This paper cites Heterogeneous diversity driven active learning for multi-object tracking.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Heterogeneous diversity driven active learning for multi-object tracking

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.484692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a910be98-d422-4564-9aa5-b086c0ded0c8 · outbound

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

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Bytetrack: Multi-object tracking by associating every detection box

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.514201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3e417bbb-44f6-4260-a54a-dd2ba58231ba · outbound

This paper cites YOLOX: Exceeding YOLO Series in 2021.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking YOLOX: Exceeding YOLO Series in 2021

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-13T10:31:31.716715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 56fa849d-3a1e-451d-831b-cfd2173cc229 · outbound

This paper cites Simple online and realtime tracking.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Simple online and realtime tracking

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.488458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f32059ed-f8d7-4fe3-8ae2-b950e46afd8c · outbound

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

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Fairmot: On the fairness of detection and re- identification in multiple object tracking

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.479475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 816f3c72-a028-40d4-af6b-44c1db143e9e · outbound

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

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking MOT16: A Benchmark for Multi-Object Tracking

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:51:24.381405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8aaee0a4-f738-4c44-a792-1c79134319bf · outbound

This paper cites Mamba: Linear-time sequence mod- eling with selective state spaces.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Mamba: Linear-time sequence mod- eling with selective state spaces

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.451950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f9960cfd-6b81-4a53-bf30-f8cb715d352f · outbound

This paper cites Spamming labels: Efficient annotations for the trackers of tomorrow.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Spamming labels: Efficient annotations for the trackers of tomorrow

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.447609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation af54d8ff-fe97-40d3-95b0-2b31decca530 · outbound

This paper cites Plug and play active learning for object detection.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Plug and play active learning for object detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.456120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 66787f34-0a2a-48d1-9a24-f6a69ce0d858 · outbound

This paper cites Active domain adaptation with false negative prediction for object detection.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Active domain adaptation with false negative prediction for object detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.474741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f3bb4072-29e2-47ae-ba53-60fc5f51543e · outbound

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

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Hota: A higher order metric for evaluating multi-object tracking

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.505768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9fa07879-53e6-4c4a-aa0a-b9109e7cc703 · outbound

This paper cites Performance measures and a data set for multi-target, multi-camera tracking.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Performance measures and a data set for multi-target, multi-camera tracking

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.492339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6be90cd0-ec22-44cd-8f44-999476bf8c52 · outbound

This paper cites Making your first choice: to address cold start problem in medical active learn- ing.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Making your first choice: to address cold start problem in medical active learn- ing

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.470556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e7afc6ab-f40b-4737-8577-629bfc8ec1f2 · outbound

This paper cites Dancetrack: Multi-object tracking in uniform appearance and diverse motion.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Dancetrack: Multi-object tracking in uniform appearance and diverse motion

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.460779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 151f3ef1-ac11-4c18-b356-d77a3663644c · outbound

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

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Memotr: Long-term memory- augmented transformer for multi-object tracking

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.501361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 55b05229-db82-4556-ba80-abb74e458bff · outbound

This paper cites Samba: Synchronized set-of- sequences modeling for multiple object tracking.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Samba: Synchronized set-of- sequences modeling for multiple object tracking

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.466502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 26a8538b-034a-4d22-8092-71de106d22c3 · outbound

This paper cites Making your first choice: to address cold start problem in medical active learning.

Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking Making your first choice: to address cold start problem in medical active learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:06:35.527653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation 836c5399-dab8-4fd3-a94b-ba10849137af · inbound