Paper Citation Record · LEDGER

Streamlining the Development of Active Learning Methods in Real-World Object Detection

As of 10 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2508.19906.

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

pith.paper-citation-record.v1
2508.19906 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:28:02.622971Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T03:58:44.738935Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T04:02:13.425524Z

Reference resolution

57 of 57 outbound references displayed

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External citation measurements

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Outbound references

Observation 1bb00f3a-da4a-4c83-9636-704de36259c1 · outbound

This paper cites A survey of deep active learning,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection A survey of deep active learning,

Reference 1

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Observation 54a63599-f7fd-40ed-a6cd-ff853cd380f5 · outbound

This paper cites Ten years of active learning techniques and object detection: A systematic review,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Ten years of active learning techniques and object detection: A systematic review,

Reference 2

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Observation afa6e70e-9158-4139-91f9-cd0f1c07eaf4 · outbound

This paper cites Deep active learning for object detection,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Deep active learning for object detection,

Reference 3

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Observation 54b5012b-d26c-4916-8db4-65d4b9d59d8c · outbound

This paper cites Advanced active learning strategies for object detection,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Advanced active learning strategies for object detection,

Reference 4

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Observation f4a8580b-47d1-4e85-85f6-d949a5702135 · outbound

This paper cites Consistency-based semi-supervised active learning: Towards minimiz- ing labeling cost,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Consistency-based semi-supervised active learning: Towards minimiz- ing labeling cost,

Reference 5

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Observation 9965d185-cfe4-4600-b0ee-b2bcf50052b8 · outbound

This paper cites Active learn- ing for deep object detection via probabilistic modeling,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Active learn- ing for deep object detection via probabilistic modeling,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:03.469288Z

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Observation 84c9566d-256e-4bd5-9cbf-8686d6c5c681 · outbound

This paper cites Localization-aware active learning for object detection,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Localization-aware active learning for object detection,

Reference 7

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Observation 62a1e842-02e8-4978-bbaa-87f19927bb6d · outbound

This paper cites Single-model uncertainties for deep learning,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Single-model uncertainties for deep learning,

Reference 8

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Observation 17b3f079-1e87-424e-aa52-f4ced42b62e2 · outbound

This paper cites Uncertainty estimates as data selection criteria to boost omni- supervised learning,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Uncertainty estimates as data selection criteria to boost omni- supervised learning,

Reference 9

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Observation 08806acd-0d13-4e42-9b96-6cfeca95a67a · outbound

This paper cites Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods,

Reference 10

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Observation 3ca1df27-896e-4a77-acdd-76326e6671c2 · outbound

This paper cites Complementing Semi-Supervised Learning with Uncertainty Quantification.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Complementing Semi-Supervised Learning with Uncertainty Quantification

Reference 11

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Observation be60dd43-faca-4ffb-968d-943c6443b278 · outbound

This paper cites How to measure un- certainty in uncertainty sampling for active learning,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection How to measure un- certainty in uncertainty sampling for active learning,

Reference 12

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Source-reported events for the cited work

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Observation 06a6e2b1-ab13-4b11-981e-b826dde6945d · outbound

This paper cites A deeper look into aleatoric and epistemic uncertainty disentanglement,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection A deeper look into aleatoric and epistemic uncertainty disentanglement,

Reference 13

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Observation 5ac1d194-5487-4e7a-9cde-1ee23d0909e5 · outbound

This paper cites Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via Ensembles.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via Ensembles

Reference 14

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Observation 59d23fd3-1ba7-40b4-ab8c-90966838c18c · outbound

This paper cites Towards dynamic and scalable active learning with neural architecture adaption for object detection,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Towards dynamic and scalable active learning with neural architecture adaption for object detection,

Reference 15

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Observation 1891df67-5d2c-4054-9a0d-71f793791c03 · outbound

This paper cites Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets

Reference 16

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Observation 485087f9-460a-454f-89a7-74d455e291d3 · outbound

This paper cites Active Learning for Deep Object Detection.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Active Learning for Deep Object Detection

Reference 17

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Observation f7bebf5e-a59d-4496-b0b8-8922dc6f5624 · outbound

This paper cites Talisman: targeted active learning for object detection with rare classes and slices using submodular mutual information,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Talisman: targeted active learning for object detection with rare classes and slices using submodular mutual information,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:03.353758Z

Source-reported events for the cited work

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Observation 4a82c922-2413-44fd-b8ec-19be4c2f76b3 · outbound

This paper cites Entropy-based active learning for object detection with progressive diversity constraint,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Entropy-based active learning for object detection with progressive diversity constraint,

Reference 19

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Observation 6c7b586d-a2e6-46e6-840a-e22a71d39548 · outbound

This paper cites Scalable active learning for object detection,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Scalable active learning for object detection,

Reference 20

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Observation 498a9540-72b2-44a4-ac95-0f30bd446364 · outbound

This paper cites The pascal visual object classes (voc) challenge,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection The pascal visual object classes (voc) challenge,

Reference 21

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Observation 158757b0-1f61-4abc-ab35-b2a9adfcb014 · outbound

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

Streamlining the Development of Active Learning Methods in Real-World Object Detection Microsoft coco: Common objects in context,

Reference 22

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Observation 09e66ed9-8fac-498c-a936-469edf25c9da · outbound

This paper cites Parting with Illusions about Deep Active Learning.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Parting with Illusions about Deep Active Learning

Reference 23

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Observation 0957f313-63e1-4775-b1a3-2e9f0152cb18 · outbound

This paper cites ALBench: A Framework for Evaluating Active Learning in Object Detection.

Streamlining the Development of Active Learning Methods in Real-World Object Detection ALBench: A Framework for Evaluating Active Learning in Object Detection

Reference 24

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Observation 13c129f9-e332-445e-82b0-6a28c0cf85c4 · outbound

This paper cites Coda: A real-world road corner case dataset for object detection in autonomous driving,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Coda: A real-world road corner case dataset for object detection in autonomous driving,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:03.281754Z

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Observation 6a7d0783-bc8d-46fa-89f0-b6d2acd71c35 · outbound

This paper cites Practical Obstacles to Deploying Active Learning.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Practical Obstacles to Deploying Active Learning

Reference 26

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Observation 1e177c9d-d57c-4b62-9252-1410da7c5bc0 · outbound

This paper cites Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection

Reference 27

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Observation 25361b9f-e752-46ba-b177-aa65a0e94ac0 · outbound

This paper cites A survey on active learning: State-of-the- art, practical challenges and research directions,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection A survey on active learning: State-of-the- art, practical challenges and research directions,

Reference 28

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Observation 8869cc05-771c-4c4c-b08a-888f1c3a3c2c · outbound

This paper cites Learning loss for active learning,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Learning loss for active learning,

Reference 29

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This paper cites A re-balancing strategy for class-imbalanced classification based on instance difficulty,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection A re-balancing strategy for class-imbalanced classification based on instance difficulty,

Reference 30

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This paper cites Smote: synthetic minority over-sampling technique,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Smote: synthetic minority over-sampling technique,

Reference 31

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Observation 7c0049fb-7667-4055-9be1-a904304d2608 · outbound

This paper cites Consistency-based active learning for object detection,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Consistency-based active learning for object detection,

Reference 32

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Observation ef4e5874-829c-4763-869d-c3681abcded3 · outbound

This paper cites Multiple instance active learning for object detection,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Multiple instance active learning for object detection,

Reference 33

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This paper cites Discrete cosine transform,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Discrete cosine transform,

Reference 34

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Streamlining the Development of Active Learning Methods in Real-World Object Detection Derivations for linear algebra and optimization,

Reference 35

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Streamlining the Development of Active Learning Methods in Real-World Object Detection Unresolved cited work

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Streamlining the Development of Active Learning Methods in Real-World Object Detection SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,

Reference 37

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Streamlining the Development of Active Learning Methods in Real-World Object Detection Kullback-leibler divergence estimation of continuous distributions,

Reference 38

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Streamlining the Development of Active Learning Methods in Real-World Object Detection Unresolved cited work

Reference 39

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Streamlining the Development of Active Learning Methods in Real-World Object Detection Efficientdet: Scalable and efficient object detection,

Reference 40

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Streamlining the Development of Active Learning Methods in Real-World Object Detection GitHub - google/automl: Google Brain AutoML,

Reference 41

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This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Are we ready for autonomous driving? the kitti vision benchmark suite,

Reference 42

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Streamlining the Development of Active Learning Methods in Real-World Object Detection Bdd100k: A diverse driving dataset for heterogeneous multitask learning,

Reference 43

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Streamlining the Development of Active Learning Methods in Real-World Object Detection Settles, Active learning literature survey

Reference 44

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Streamlining the Development of Active Learning Methods in Real-World Object Detection Efficient object localization using convolutional networks,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:02.989227Z

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Observation 516bb940-6b0a-4a11-b3c1-e84123e3c68e · outbound

This paper cites Uncertainty estimation in deep neural object detectors for autonomous driving,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Uncertainty estimation in deep neural object detectors for autonomous driving,

Reference 46

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Observation 34e0add2-42ad-447a-a04a-09783340ba60 · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision?.

Streamlining the Development of Active Learning Methods in Real-World Object Detection What uncertainties do we need in bayesian deep learning for computer vision?

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:02.956792Z

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Observation 7e12a859-71bb-41e3-ba2a-c9e87a8adedb · outbound

This paper cites Overcoming the limitations of localization uncertainty: Efficient and exact non-linear post-processing and calibration,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Overcoming the limitations of localization uncertainty: Efficient and exact non-linear post-processing and calibration,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:02.942719Z

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Streamlining the Development of Active Learning Methods in Real-World Object Detection Vii. note on regression and inheritance in the case of two parents,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:02.928030Z

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Observation c550a5bb-1b23-47a1-8315-1eeeaec0059d · outbound

This paper cites A new measure of rank correlation,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection A new measure of rank correlation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:02.912939Z

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Observation 7070e0a2-0913-470d-9d18-744c80d5e865 · outbound

This paper cites On initial pools for deep active learning,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection On initial pools for deep active learning,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:02.898128Z

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Observation a1f085ed-7da2-4bfc-ae1c-7a626a49454e · outbound

This paper cites Localization- based active learning (local) for object detection in 3d point clouds,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Localization- based active learning (local) for object detection in 3d point clouds,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:02.880794Z

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Observation d9c051b3-2f13-4203-80c2-75ff86db62cf · outbound

This paper cites Gpu pricing,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Gpu pricing,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:02.864535Z

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Observation 7ed8e403-d9fc-488e-8f1f-abdc1ab1d796 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T15:28:02.609558Z

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Observation bd8b0894-9c49-4217-9185-f3ee2f857c65 · outbound

This paper cites Vgg16 and vgg19,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Vgg16 and vgg19,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:02.849943Z

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Observation b6ec8ce4-08ca-4bdc-a087-3d2a45d0b166 · outbound

This paper cites Yolov3: An incremental improvement,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection Yolov3: An incremental improvement,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:02.835278Z

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Observation a7ea659c-144e-46fc-a59c-27bcba03dd09 · outbound

This paper cites GitHub - yolov3 in tensorflow 2.0,.

Streamlining the Development of Active Learning Methods in Real-World Object Detection GitHub - yolov3 in tensorflow 2.0,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:28:02.820260Z

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Pith citing papers

Observation 88704b15-a2a1-4709-8519-bb4ddbf8ec2e · inbound

From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review cites this paper.

From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review Streamlining the Development of Active Learning Methods in Real-World Object Detection

Reference 84

Resolution: verified exact; arxiv_id, observed 2026-05-13T04:02:13.428031Z

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