Paper Citation Record · LEDGER

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 3 inbound Pith citation observations for arXiv:2507.00049.

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

pith.paper-citation-record.v1
2507.00049 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:05:19.488311Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T20:38:42.563481Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T03:56:35.127847Z

Reference resolution

41 of 41 outbound references displayed

verified exact1
verified fuzzy14
unresolved26
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation df092bff-07a4-4e0e-8887-30e7e22d9577 · outbound

This paper cites How much more data do i need? estimating requirements for downstream tasks.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training How much more data do i need? estimating requirements for downstream tasks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:28.014744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 543d5c5b-99ce-47dc-98e0-2fd57955c782 · outbound

This paper cites SemDeDup: Data-efficient learning at web-scale through semantic deduplication.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training SemDeDup: Data-efficient learning at web-scale through semantic deduplication

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:16.343098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 801f729f-da63-416f-9923-ed6f3eed2863 · outbound

This paper cites Optimizing data collection for machine learning.Journal of Machine Learning Research, 26(38):1–52, 2025.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Optimizing data collection for machine learning.Journal of Machine Learning Research, 26(38):1–52, 2025

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:27.644835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bad166d6-0574-4981-8d6f-b702dcddcf9d · outbound

This paper cites Beyond neural scaling laws: beating power law scaling via data pruning.Advances in Neural Information Processing Systems, 35:19523–19536, 2022.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Beyond neural scaling laws: beating power law scaling via data pruning.Advances in Neural Information Processing Systems, 35:19523–19536, 2022

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:16.375997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6054ab60-5b70-47e7-88ad-fc35592d390c · outbound

This paper cites Sse: Multimodal semantic data selection and enrichment for industrial-scale data assimilation.ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2025.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Sse: Multimodal semantic data selection and enrichment for industrial-scale data assimilation.ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2025

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:27.209821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4b1ab1d7-d169-41db-98aa-fcad646ef059 · outbound

This paper cites Effective pruning of web-scale datasets based on complexity of concept clusters.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Effective pruning of web-scale datasets based on complexity of concept clusters

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:16.387914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63247bde-863f-4f3f-915c-4fdd2c7fd10d · outbound

This paper cites Zero-shot coreset selection: Efficient pruning for unlabeled data.arXiv preprint arXiv:2411.15349, 2024.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Zero-shot coreset selection: Efficient pruning for unlabeled data.arXiv preprint arXiv:2411.15349, 2024

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:16.393755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75cd7295-e410-4728-b7e8-e083be2f0a3b · outbound

This paper cites Efficient coreset selection with cluster-based methods.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Efficient coreset selection with cluster-based methods

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:26.924748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 30b93b8f-956e-491a-9731-551f0a7e365f · outbound

This paper cites Moderate coreset: A universal method of data selection for real-world data-efficient deep learning.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Moderate coreset: A universal method of data selection for real-world data-efficient deep learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:26.640880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 27b66ebc-d49f-49e8-b514-da280b265ef1 · outbound

This paper cites Data pruning via moving-one-sample-out.Advances in neural information processing systems, 36: 18251–18262, 2023.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Data pruning via moving-one-sample-out.Advances in neural information processing systems, 36: 18251–18262, 2023

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:25.684759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 81a77012-a96a-4efe-bbab-3930ff5d4fc9 · outbound

This paper cites Data curation via joint example selection further accelerates multimodal learning.Advances in Neural Information Processing Systems, 37:141240–141260, 2024.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Data curation via joint example selection further accelerates multimodal learning.Advances in Neural Information Processing Systems, 37:141240–141260, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:25.225174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 79239bdb-65d4-42e0-b0f1-ce1ff346bc1a · outbound

This paper cites Selection via Proxy: Efficient Data Selection for Deep Learning.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Selection via Proxy: Efficient Data Selection for Deep Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:16.571968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eacc3155-12d0-43aa-97e1-d0b54709299e · outbound

This paper cites Coreset selection for object detection.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Coreset selection for object detection

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:24.687139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3fe57e05-7a8b-48a5-972d-f3078253aeab · outbound

This paper cites Goodcore: Data-effective and data-efficient machine learning through coreset selection over incomplete data.Proceedings of the ACM on Management of Data, 1(2):1–27, 2023.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Goodcore: Data-effective and data-efficient machine learning through coreset selection over incomplete data.Proceedings of the ACM on Management of Data, 1(2):1–27, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:24.276016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3da021c2-a114-4376-bf05-ae1dd5893103 · outbound

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

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training nuscenes: A multimodal dataset for autonomous driving

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:16.692484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0675a12f-453e-4d67-9dd2-397902a8f84e · outbound

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

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Scalability in perception for autonomous driving: Waymo open dataset

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:16.705078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 277c114e-3a35-47a1-a70a-6dcbf3c18dc7 · outbound

This paper cites Lvis: A dataset for large vocabulary instance segmentation.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Lvis: A dataset for large vocabulary instance segmentation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:16.720049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 743eae11-e7fd-41c5-aa34-192f7807026b · outbound

This paper cites Microsoft coco: Common objects in context.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Microsoft coco: Common objects in context

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:16.844745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61881223-4809-447a-8288-85a17946b662 · outbound

This paper cites Performance scaling via optimal transport: Enabling data selection from partially revealed sources.Advances in Neural Information Processing Systems, 36:61341–61363, 2023.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Performance scaling via optimal transport: Enabling data selection from partially revealed sources.Advances in Neural Information Processing Systems, 36:61341–61363, 2023

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:23.564580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3124ad71-1d6c-467e-a46e-75af66985240 · outbound

This paper cites Understanding black-box predictions via influence functions.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Understanding black-box predictions via influence functions

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:17.099478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48ef057a-a115-485e-92f5-3b9b82afad2a · outbound

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

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:17.215945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec9f6b0e-b343-47e2-9233-e88e2c9dff33 · outbound

This paper cites An Empirical Study of Example Forgetting during Deep Neural Network Learning.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training An Empirical Study of Example Forgetting during Deep Neural Network Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:17.373329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3d4775e-8c76-47d0-badf-6cf2cfcc8cde · outbound

This paper cites Deep learning on a data diet: Finding important examples early in training.Advances in neural information processing systems, 34:20596–20607, 2021.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Deep learning on a data diet: Finding important examples early in training.Advances in neural information processing systems, 34:20596–20607, 2021

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:17.494757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db496b88-e096-4b81-98d2-37a61dfccc95 · outbound

This paper cites Coresets via bilevel optimization for continual learning and streaming.Advances in neural information processing systems, 33: 14879–14890, 2020.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Coresets via bilevel optimization for continual learning and streaming.Advances in neural information processing systems, 33: 14879–14890, 2020

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:23.188679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 534cf64d-9d93-4bf7-84a8-5ef277a4b5ac · outbound

This paper cites Gradient-based Bi-level Optimization for Deep Learning: A Survey.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Gradient-based Bi-level Optimization for Deep Learning: A Survey

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:17.754757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f87939b7-05ff-4bf8-8123-9da59246c5db · outbound

This paper cites Springer Science & Business Media, 1998.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Springer Science & Business Media, 1998

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:22.834754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9bde4033-9a53-4ca5-a709-fbf69d375687 · outbound

This paper cites Datamodels: Predicting Predictions from Training Data.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Datamodels: Predicting Predictions from Training Data

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:17.959429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 954c19a8-77e9-43a9-b6ba-fd903efb4431 · outbound

This paper cites Autoscale: Automatic prediction of compute-optimal data composition for training llms.arXiv preprint arXiv:2407.20177, 2024.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Autoscale: Automatic prediction of compute-optimal data composition for training llms.arXiv preprint arXiv:2407.20177, 2024

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:18.055970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d57657e4-056d-4efd-b467-745183bff5d8 · outbound

This paper cites Bevformer: learning bird’s-eye-view representation from lidar-camera via spatiotemporal transformers.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Bevformer: learning bird’s-eye-view representation from lidar-camera via spatiotemporal transformers.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:18.281909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd12d4f9-5682-438a-b892-7c0e059b4ec1 · outbound

This paper cites Deep residual learning for image recognition.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Deep residual learning for image recognition

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:18.414756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6f60cdb-6113-4d21-af70-02c342b744f0 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.IEEE transactions on pattern analysis and machine intelligence, 39(6):1137–1149, 2016.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Faster r-cnn: Towards real-time object detection with region proposal networks.IEEE transactions on pattern analysis and machine intelligence, 39(6):1137–1149, 2016

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:22.254852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fa675ba9-778c-44f6-85df-891fa30eda39 · outbound

This paper cites Detectron2.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Detectron2

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:18.611387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73ae992f-e6b4-49e0-8023-74e342996359 · outbound

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

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Learning transferable visual models from natural language supervision

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:18.658590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e7326f9-8050-45c3-94b3-28c37fecf0bc · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:18.734412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c55ee1f-b522-4b07-b89a-a853b7b30a93 · outbound

This paper cites Improved baselines with visual instruction tuning.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Improved baselines with visual instruction tuning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:18.791413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f1a53f7-f1b1-4688-9ee9-8813fedbc8ca · outbound

This paper cites Data Distillation: A Survey.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Data Distillation: A Survey

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:18.887426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3892ec02-ade5-4c19-81ca-e4d17933f0f3 · outbound

This paper cites Glister: Generalization based data subset selection for efficient and robust learning.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Glister: Generalization based data subset selection for efficient and robust learning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:18.988068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb772b09-e0cb-4486-80d6-9180cfcea93f · outbound

This paper cites Grad-match: Gradient matching based data subset selection for efficient deep model training.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Grad-match: Gradient matching based data subset selection for efficient deep model training

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:19.154962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 138d5e7b-863f-4c4a-8f16-d86e7fe89b68 · outbound

This paper cites LAVA: Data Valuation without Pre-Specified Learning Algorithms.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training LAVA: Data Valuation without Pre-Specified Learning Algorithms

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:19.248709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b97147c0-475f-4324-9591-13ad768c0f23 · outbound

This paper cites Estimating training data influence by tracing gradient descent.Advances in Neural Information Processing Systems, 33: 19920–19930, 2020.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Estimating training data influence by tracing gradient descent.Advances in Neural Information Processing Systems, 33: 19920–19930, 2020

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:05:21.414754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b98c8d11-f9cd-47c0-8857-ca48f2a0bca2 · outbound

This paper cites Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs.

AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:05:19.774905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 6fbfa801-1566-43a2-9f2a-7f2c1d1f8e0e · inbound

Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems cites this paper.

Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:56:02.288684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9600728b-38be-4d23-bf75-7f62b737a7ab · inbound

Can Generalist Agents Automate Data Curation? cites this paper.

Can Generalist Agents Automate Data Curation? AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:56:35.129131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1021dff6-719b-430f-94b0-369afe2392bc · inbound

Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning cites this paper.

Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T20:38:42.563481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.