Paper Citation Record · LEDGER

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets

As of 7 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 2 inbound Pith citation observations for arXiv:2506.04737.

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

pith.paper-citation-record.v1
2506.04737 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:40:46.556032Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T17:17:55.617945Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T08:50:58.323179Z

Reference resolution

37 of 37 outbound references displayed

verified exact3
verified fuzzy19
unresolved14
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 67c58406-8817-4fc3-abb9-54bc7a50a276 · outbound

This paper cites Scaledet: A scalable multi-dataset object detector.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Scaledet: A scalable multi-dataset object detector

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:47.308788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a269b451-a679-4c1a-bfb9-5a77d1e27edc · outbound

This paper cites Towards universal object detection by domain attention.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Towards universal object detection by domain attention

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:47.290687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9104a824-b609-4158-b778-c2e234c4b7b3 · outbound

This paper cites Transferring labels to solve annotation mismatches across object detection datasets.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Transferring labels to solve annotation mismatches across object detection datasets

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:47.262529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d72a01eb-c1f0-42b5-a608-7e4babf1a50e · outbound

This paper cites Learning transferable visual models from natural language supervision, 2021.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Learning transferable visual models from natural language supervision, 2021

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T10:40:46.371740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 642cc05b-279e-479c-8b97-c1a6e7610856 · outbound

This paper cites Openclip, July 2021.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Openclip, July 2021

Reference 5

Resolution: malformed identifier; no resolver link, observed 2026-08-07T10:40:46.377178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d45dbf7b-a47b-4eac-92b0-f32f651ae3c4 · outbound

This paper cites Plain-det: A plain multi-dataset object detector.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Plain-det: A plain multi-dataset object detector

Reference 6

Resolution: verified exact; doi, observed 2026-08-07T10:40:46.662247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dac575d6-dc1f-43b2-b4f6-0cd9f84dac4e · outbound

This paper cites LMSeg: Language-guided multi-dataset segmentation.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets LMSeg: Language-guided multi-dataset segmentation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:47.208593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eb6930dd-dcee-4ab9-b3c3-9a6ce971e50e · outbound

This paper cites Detection hub: Unifying object detection datasets via query adaptation on language embedding.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Detection hub: Unifying object detection datasets via query adaptation on language embedding

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:47.184313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 587b9ea1-ce33-45b0-a2be-cea8a1e7c12d · outbound

This paper cites Universal-rcnn: Universal object detector via transferable graph r-cnn.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Universal-rcnn: Universal object detector via transferable graph r-cnn

Reference 9

Resolution: verified exact; doi, observed 2026-08-07T10:40:46.640149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7cd96b33-47de-4c4d-8d45-f6aec1cb18e1 · outbound

This paper cites Automated label unification for multi-dataset semantic segmentation with GNNs.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Automated label unification for multi-dataset semantic segmentation with GNNs

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:47.162999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation be9fb146-1aa5-4661-9615-8888c621b85c · outbound

This paper cites MSeg: A composite dataset for multi-domain semantic segmentation.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets MSeg: A composite dataset for multi-domain semantic segmentation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:47.142904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9422142e-64ca-44b1-a0a3-bd0691ca0b92 · outbound

This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T10:40:46.419300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 551e532b-d02f-4b9c-988c-6f94ca7b7548 · outbound

This paper cites Unbiased teacher for semi-supervised object detection.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Unbiased teacher for semi-supervised object detection

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:47.110003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 053ceddb-f123-4d58-b2bf-7b739bcc0ea4 · outbound

This paper cites an unresolved cited work.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:40:47.092747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d83e7683-4c2e-45c7-828e-5ae02aee7e7a · outbound

This paper cites an unresolved cited work.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:40:47.076368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0b4772ab-423e-402b-8457-2b534b653b8e · outbound

This paper cites Cross-domain adaptive teacher for object detection.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Cross-domain adaptive teacher for object detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:47.058231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 93677b99-199b-4e70-8e54-614c24727487 · outbound

This paper cites Cycada: Cycle-consistent adversarial domain adaptation.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Cycada: Cycle-consistent adversarial domain adaptation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:47.040010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 858b2397-0bcc-409a-9dda-c814627b98d8 · outbound

This paper cites Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T10:40:46.451625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8eded646-6ab1-4cba-adb0-41ffb5ba37cf · outbound

This paper cites Forkgan: Seeing into the rainy night.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Forkgan: Seeing into the rainy night

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:47.022803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 943f35b4-7419-4319-916b-d31e2a1ed6e6 · outbound

This paper cites Pasta: Proportional amplitude spectrum training augmentation for syn-to-real domain generalization.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Pasta: Proportional amplitude spectrum training augmentation for syn-to-real domain generalization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:47.004555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 08ba0642-ff08-45eb-8e82-5ba299b971eb · outbound

This paper cites an unresolved cited work.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:40:46.984970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2d568042-9641-4a89-b56c-cdc8185e47ea · outbound

This paper cites Are we done with ImageNet?.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Are we done with ImageNet?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T10:40:46.473215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8476f585-6cb6-4a5a-8145-4bbc12b7df3d · outbound

This paper cites Re-labeling imagenet: from single to multi-labels, from global to localized labels.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Re-labeling imagenet: from single to multi-labels, from global to localized labels

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:46.965078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 92d8f566-8c74-40d0-b111-9b001452915c · outbound

This paper cites Automatic universal taxonomies for multi-domain semantic segmentation.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Automatic universal taxonomies for multi-domain semantic segmentation

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:40:46.617431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8a403d05-6c79-4b8f-8551-df1ce9ee9e3d · outbound

This paper cites Multi-domain semantic segmentation with overlapping labels.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Multi-domain semantic segmentation with overlapping labels

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:46.944199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1eab01e9-f994-4b0c-affd-4c08bd244a4d · outbound

This paper cites Automated detection of label errors in semantic seg- mentation datasets via deep learning and uncertainty quantification.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Automated detection of label errors in semantic seg- mentation datasets via deep learning and uncertainty quantification

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:46.926751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9a0ecbed-1005-4f7c-a16b-027eedc0ded9 · outbound

This paper cites Simple multi-dataset detection.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Simple multi-dataset detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:46.908165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6a3995a7-74c9-42dc-857d-4573ce8ace20 · outbound

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

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Faster r-cnn: Towards real-time object detection with region proposal networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:46.888159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b5101169-ff05-4a5d-9d7d-2571ef1969ec · outbound

This paper cites an unresolved cited work.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:40:46.867547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 673eabde-ed0a-46f7-bb0a-6e7905902e08 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets The cityscapes dataset for semantic urban scene understanding

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T10:40:46.518849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fb6e80e-41da-4669-af22-5ba3bf1f429e · outbound

This paper cites nuScenes: A multimodal dataset for autonomous driving.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets nuScenes: A multimodal dataset for autonomous driving

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T10:40:46.524464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c227d3fa-3f5d-484c-9836-4d306546759f · outbound

This paper cites Scalability in perception for autonomous driving: Waymo open dataset.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Scalability in perception for autonomous driving: Waymo open dataset

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T10:40:46.530409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e501acc-f916-4717-8eb9-df4bb90dc40e · outbound

This paper cites ACDC: The adverse conditions dataset with correspondences for semantic driving scene understanding.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets ACDC: The adverse conditions dataset with correspondences for semantic driving scene understanding

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:46.822152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0c58c59f-5c60-4fab-8f26-a87edfa5dce6 · outbound

This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Bdd100k: A diverse driving dataset for heterogeneous multitask learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T10:40:46.540338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2bc6b98-b9d3-443f-9177-24f712d97f72 · outbound

This paper cites SHIFT: a synthetic driving dataset for continuous multi-task domain adaptation.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets SHIFT: a synthetic driving dataset for continuous multi-task domain adaptation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:46.787358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f67783ef-1712-4247-89f5-70774cfbafd4 · outbound

This paper cites Detectron2.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Detectron2

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T10:40:46.551038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88e2dcf3-3af8-46db-808f-8f80052315dd · outbound

This paper cites Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T10:40:46.556032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c69ab5be-f08a-41fd-8772-119ec283a077 · inbound

Generalization Under Scrutiny: Cross-Domain Detection Progresses, Pitfalls, and Persistent Challenges cites this paper.

Generalization Under Scrutiny: Cross-Domain Detection Progresses, Pitfalls, and Persistent Challenges Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets

Reference 74

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:06:15.536166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ba1b3c76-ef5a-4511-a628-c59c667c9dcb · inbound

Improving Layout Representation Learning Across Inconsistently Annotated Datasets via Agentic Harmonization cites this paper.

Improving Layout Representation Learning Across Inconsistently Annotated Datasets via Agentic Harmonization Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:50:58.327711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.