Paper Citation Record · LEDGER

Instance-Level Post Hoc Uncertainty Quantification in Object Detection

As of 5 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2606.04656.

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

pith.paper-citation-record.v1
2606.04656 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T06:33:44.301617Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

28 of 28 outbound references displayed

verified exact6
verified fuzzy0
unresolved22
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0a555014-7bc9-48b0-b67c-14f2b2f596dc · outbound

This paper cites Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:56:47.233780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3a60323e-3dc9-488c-a711-3fea56c78680 · outbound

This paper cites Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:56:47.227055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fd6e2fba-5dfe-47f9-aa7e-9fb7ee216050 · outbound

This paper cites Estimating and evaluating regression predictive uncertainty in deep object detectors,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Estimating and evaluating regression predictive uncertainty in deep object detectors,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78dfea7a-3f4c-436f-a7c6-da83e81c10b4 · outbound

This paper cites Laplace approximation based epistemic uncertainty estimation in 3d object detection,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Laplace approximation based epistemic uncertainty estimation in 3d object detection,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e55c6018-ad35-442c-8606-2ad30d1c87db · outbound

This paper cites Improving predictions of bayesian neural nets via local linearization,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Improving predictions of bayesian neural nets via local linearization,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da74bc4b-daa6-4429-8c96-fe847dd9af3a · outbound

This paper cites A survey of uncertainty in deep neural networks,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection A survey of uncertainty in deep neural networks,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9fd8968-975e-468f-b8e7-92f2db0319bb · outbound

This paper cites Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-07-02T07:56:47.224467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4e977e68-00c6-41d9-9a96-7c02acc39a5d · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Dropout as a bayesian approximation: Representing model uncertainty in deep learning,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9d2510a-baa4-4c29-9e7e-d2fb77c956e4 · outbound

This paper cites Practical variational inference for neural networks,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Practical variational inference for neural networks,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c78d205-f6c3-428a-be85-6bfc2b5933b3 · outbound

This paper cites Structured and efficient variational deep learning with matrix gaussian posteriors,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Structured and efficient variational deep learning with matrix gaussian posteriors,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1bede56-21b8-4abe-be1c-7da3e13264da · outbound

This paper cites Learning structured weight uncertainty in bayesian neural networks,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Learning structured weight uncertainty in bayesian neural networks,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5858052f-7d7e-4a21-8449-4b72ed32f04d · outbound

This paper cites Eigenvalue Corrected Noisy Natural Gradient.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Eigenvalue Corrected Noisy Natural Gradient

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-02T07:56:47.230383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4675ad83-09ea-4ef3-b014-087269956405 · outbound

This paper cites Practical deep learning with bayesian principles,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Practical deep learning with bayesian principles,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dac14744-5ebf-485d-97be-323e74f76e95 · outbound

This paper cites Bayesian model comparison and backprop nets,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Bayesian model comparison and backprop nets,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9762496d-8c50-49b1-bab1-1ab233ca4e22 · outbound

This paper cites Optimizing neural networks with kronecker-factored ap- proximate curvature,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Optimizing neural networks with kronecker-factored ap- proximate curvature,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c6c267f-694a-4599-a352-4d32c40f68eb · outbound

This paper cites Fast approximate natural gradient descent in a kronecker factored eigenbasis,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Fast approximate natural gradient descent in a kronecker factored eigenbasis,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cde27b5a-571d-41ff-960c-7ea43f0b0d41 · outbound

This paper cites Estimating model uncertainty of neural networks in sparse information form,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Estimating model uncertainty of neural networks in sparse information form,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab23f1b7-bc58-4b57-b6d2-63acfa6a0dc7 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6c0c0c2-55f6-43ed-b421-f7d224efc4b0 · outbound

This paper cites Hyperparameter ensembles for robustness and uncertainty quantification,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Hyperparameter ensembles for robustness and uncertainty quantification,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f16363ed-2764-4c61-b68b-c762658274c1 · outbound

This paper cites Autoregressive uncertainty modeling for 3d bounding box prediction,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Autoregressive uncertainty modeling for 3d bounding box prediction,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e4b1c71-5756-46fb-b164-e8556d5aee5c · outbound

This paper cites Bayesod: A bayesian approach for uncertainty estimation in deep object detectors,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Bayesod: A bayesian approach for uncertainty estimation in deep object detectors,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 228c05c5-82d3-4ed7-8b08-f738426d91da · outbound

This paper cites Monte Carlo DropBlock for Modelling Uncertainty in Object Detection.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Monte Carlo DropBlock for Modelling Uncertainty in Object Detection

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:56:47.218820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 97b402c9-cb5d-4947-a038-1cca7d6c40a2 · outbound

This paper cites Probabilistic object detection via deep ensembles,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Probabilistic object detection via deep ensembles,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 388b90dd-7a36-4861-83c6-3077ca080fe1 · outbound

This paper cites Laplace approximation for real-time uncertainty estimation in object detection,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Laplace approximation for real-time uncertainty estimation in object detection,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37b9a5e0-d66a-419c-b7c3-310a7f843e8f · outbound

This paper cites Deep evidential regression,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Deep evidential regression,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1236640d-9096-4130-8273-8af782759188 · outbound

This paper cites Evaluating Bayesian Deep Learning Methods for Semantic Segmentation.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Evaluating Bayesian Deep Learning Methods for Semantic Segmentation

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-07-02T07:56:47.221587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9e6091ff-72a9-464f-8c98-f2975dc9e81b · outbound

This paper cites Center-based 3d object detection and tracking,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection Center-based 3d object detection and tracking,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e50a8e6-1643-4d2a-9ba2-5e30d3d86fd4 · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving,.

Instance-Level Post Hoc Uncertainty Quantification in Object Detection nuscenes: A multimodal dataset for autonomous driving,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-28T06:33:44.301617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.