Paper Citation Record · LEDGER

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes

As of 19 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2504.16443.

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

pith.paper-citation-record.v1
2504.16443 v2

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:08:36.331768Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:26:12.344746Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

verified exact0
verified fuzzy26
unresolved26
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bbd16eb8-808e-4acf-a1b7-897d0d4a3f4f · outbound

This paper cites Super-gradients, 2021.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Super-gradients, 2021

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:39.150166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0ea656b9-25d3-4a5f-bb95-be14328565dd · outbound

This paper cites Objectron: A large scale dataset of object-centric videos in the wild with pose an- notations.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Objectron: A large scale dataset of object-centric videos in the wild with pose an- notations

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:34.919057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc7c3cc9-b095-46d5-b53f-01a66d2b256c · outbound

This paper cites ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:34.987198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa85fc48-671a-4f8a-af7f-e65653544bab · outbound

This paper cites Disentangled contour learn- ing for quadrilateral text detection.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Disentangled contour learn- ing for quadrilateral text detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:39.066310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7d04826d-12ed-42da-a466-a41d48616791 · outbound

This paper cites Omni3D: A large benchmark and model for 3D object detection in the wild.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Omni3D: A large benchmark and model for 3D object detection in the wild

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:38.895410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7218b8c0-79c7-45d8-ae51-e9edea92ff7a · outbound

This paper cites nuscenes: A multi- modal dataset for autonomous driving.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes nuscenes: A multi- modal dataset for autonomous driving

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.150681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e66cf06-fe60-4cbc-bad7-505eb818879d · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes ShapeNet: An Information-Rich 3D Model Repository

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.157442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a227ab0-c318-4baa-998f-d89d71096d66 · outbound

This paper cites MMCV: OpenMMLab computer vision foundation.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes MMCV: OpenMMLab computer vision foundation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:38.684232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c90c0a9d-1980-47ea-b27c-abecb9948467 · outbound

This paper cites Scannet: Richly-annotated 3d reconstructions of indoor scenes.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.171787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc4ff02c-dce2-45ed-84fe-37bc3730ad04 · outbound

This paper cites Vision meets robotics: The kitti dataset.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Vision meets robotics: The kitti dataset

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.176622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84e5911d-a973-41c5-b552-cbdd1d4266df · outbound

This paper cites Icdar2017 robust reading challenge on coco-text.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Icdar2017 robust reading challenge on coco-text

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:38.580582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6ee7bf40-736c-48dc-92cb-d5acd319d289 · outbound

This paper cites An end-to-end quadrilateral regression network for comic panel extraction.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes An end-to-end quadrilateral regression network for comic panel extraction

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:38.524553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 288c639e-2b04-48b4-8519-a3675c995341 · outbound

This paper cites Quad- box: A new approach for arbitrary quadrilateral detection.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Quad- box: A new approach for arbitrary quadrilateral detection

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:38.477678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f32a4f1d-d3df-4215-8587-ad70b2747a6d · outbound

This paper cites Diverse multiple trajectory prediction using a two-stage prediction network trained with lane loss.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Diverse multiple trajectory prediction using a two-stage prediction network trained with lane loss

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:38.386911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 27963d0c-dbfe-4838-a9be-f177287f67e8 · outbound

This paper cites Unimode: Unified monocular 3d object detection.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Unimode: Unified monocular 3d object detection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:38.374288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ee4b2754-d8a2-42f5-8f41-7ed5dd71bd8b · outbound

This paper cites Textboxes++: A single-shot oriented scene text detector.IEEE transactions on image processing, 27(8):3676–3690, 2018.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Textboxes++: A single-shot oriented scene text detector.IEEE transactions on image processing, 27(8):3676–3690, 2018

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:38.304456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ecbea8ff-8025-4330-b426-c71880d00905 · outbound

This paper cites Focal loss for dense object detection.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Focal loss for dense object detection

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.360716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a490a09b-eaf1-40ae-b2df-cf0edcd651d9 · outbound

This paper cites Deep matching prior network: Toward tighter multi-oriented text detection.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Deep matching prior network: Toward tighter multi-oriented text detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:38.229280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b29603db-fe1d-423a-8787-7d946e21a14c · outbound

This paper cites Yolo-pose: Enhancing yolo for multi person pose estimation using object keypoint similarity loss.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Yolo-pose: Enhancing yolo for multi person pose estimation using object keypoint similarity loss

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:38.075712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f755127d-d47c-4ede-9603-212e702990b0 · outbound

This paper cites Improv- ing movement prediction of traffic actors using off-road loss and bias mitigation.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Improv- ing movement prediction of traffic actors using off-road loss and bias mitigation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:38.061867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 21214da2-e3fb-4027-91d6-5b50d20e9533 · outbound

This paper cites PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.378460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e8931dc-e3e5-44b9-aaad-edc236c3e0ba · outbound

This paper cites Learning modulated loss for rotated object detection.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Learning modulated loss for rotated object detection

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.477854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 427f867b-73b4-478f-a8b4-ca5e41c927a5 · outbound

This paper cites Accelerating 3D Deep Learning with PyTorch3D.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Accelerating 3D Deep Learning with PyTorch3D

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.501361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56c977cf-cf48-4862-8531-3d2d63f2e181 · outbound

This paper cites Yolo9000: better, faster, stronger.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Yolo9000: better, faster, stronger

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.505272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d358a113-f596-43cd-a2fe-821e6c9f715d · outbound

This paper cites You only look once: Unified, real-time object de- tection.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes You only look once: Unified, real-time object de- tection

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.508810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a0bbf6d-fab3-4fc2-8fd6-f20b10f46561 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region 9 proposal networks.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Faster r-cnn: Towards real-time object detection with region 9 proposal networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:37.974543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b10d3f2e-0261-4d50-91d8-b4395d77d52f · outbound

This paper cites Generalized in- tersection over union: A metric and a loss for bounding box regression.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Generalized in- tersection over union: A metric and a loss for bounding box regression

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.516599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46416b89-850c-4592-b169-0c0c645a6533 · outbound

This paper cites Hypersim: A photorealistic syn- thetic dataset for holistic indoor scene understanding.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Hypersim: A photorealistic syn- thetic dataset for holistic indoor scene understanding

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:37.820214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 219b2fb6-015f-470f-a9bf-943993827cd2 · outbound

This paper cites Motion transformer with global intention localization and lo- cal movement refinement.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Motion transformer with global intention localization and lo- cal movement refinement

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:37.806684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5c3d36ff-df9b-4889-bb69-2114cdd724a4 · outbound

This paper cites MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.613984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a618016-f0e2-4e36-b1e7-c51288755265 · outbound

This paper cites Sun rgb-d: A rgb-d scene understanding benchmark suite.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Sun rgb-d: A rgb-d scene understanding benchmark suite

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.703228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0600fee-d0cb-45c4-97df-476623b6c064 · outbound

This paper cites Deep learning on the ro- tation manifold for object detection in 3d point clouds.IEEE Transactions on Pattern Analysis and Machine Intelligence,.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Deep learning on the ro- tation manifold for object detection in 3d point clouds.IEEE Transactions on Pattern Analysis and Machine Intelligence,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:37.747588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aace6d3f-bc09-4647-bad8-dad6dc22d0f0 · outbound

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

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Scalability in perception for autonomous driving: Waymo open dataset

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:37.598212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 88590f26-7f61-4e5f-93a2-2894ad485aed · outbound

This paper cites Trafficsim: Learning to simulate realistic multi- agent behaviors.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Trafficsim: Learning to simulate realistic multi- agent behaviors

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:37.549453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3fbbe737-67c8-4e1b-812a-de8625d6a6e2 · outbound

This paper cites Resolving the polarized dust emission of the disk around the massive star powering the HH~80-81 radio jet.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Resolving the polarized dust emission of the disk around the massive star powering the HH~80-81 radio jet

Reference 35

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T11:08:36.458180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5925f4aa-f22e-4dfc-817e-5f07b17fdabf · outbound

This paper cites Machine learning the nuclear mass.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Machine learning the nuclear mass

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.842585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48280967-5e70-4873-8ce4-372c06a03963 · outbound

This paper cites Rethinking rotated object detection with gaussian wasserstein distance loss.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Rethinking rotated object detection with gaussian wasserstein distance loss

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.881739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ff4eee2-d5c2-425b-ad7c-7b9c4dc05f93 · outbound

This paper cites Learning high-precision bounding box for rotated object detection via kullback- leibler divergence.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Learning high-precision bounding box for rotated object detection via kullback- leibler divergence

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:37.460531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b8df1acc-b2d7-4eaf-9bc9-9e1a0a3c3f04 · outbound

This paper cites The KFIoU Loss for Rotated Object Detection.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes The KFIoU Loss for Rotated Object Detection

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:35.927872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ffcc807-6645-41c8-9eec-be3f33bdca99 · outbound

This paper cites Trajgen: Generating realistic and diverse trajectories with re- active and feasible agent behaviors for autonomous driving.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Trajgen: Generating realistic and diverse trajectories with re- active and feasible agent behaviors for autonomous driving

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:37.446216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 496a933f-1bfb-4495-ac6f-ed5ceb257677 · outbound

This paper cites Distance-iou loss: Faster and bet- ter learning for bounding box regression.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Distance-iou loss: Faster and bet- ter learning for bounding box regression

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:37.432869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 73249057-21a4-4d95-997a-fb35a1da1b86 · outbound

This paper cites Enhancing ge- ometric factors in model learning and inference for object detection and instance segmentation.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Enhancing ge- ometric factors in model learning and inference for object detection and instance segmentation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:37.344435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 04978834-ee6b-463c-af49-6705baaf579d · outbound

This paper cites Iou loss for 2d/3d ob- ject detection.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Iou loss for 2d/3d ob- ject detection

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:37.332033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 79ad9fd1-2028-488f-a0b0-2604dc88e1a8 · outbound

This paper cites Mmrotate: A rotated object detection benchmark using pytorch.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Mmrotate: A rotated object detection benchmark using pytorch

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:37.227862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c758a451-e7fe-4c54-ac4b-81b862c568fe · outbound

This paper cites an unresolved cited work.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:08:37.123083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f5f7ff13-ef7f-4b8d-9ef8-7820d19b808e · outbound

This paper cites Specifically, we analyze the following properties forLMGIoU over structured convex shapesP andG with a shared para- metric domain:.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Specifically, we analyze the following properties forLMGIoU over structured convex shapesP andG with a shared para- metric domain:

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:08:37.033614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation af06f311-ca32-401b-ad94-1e779bc18145 · outbound

This paper cites an unresolved cited work.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:08:36.546204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aee835b8-eb4e-4218-be3e-4ea01084bcd0 · outbound

This paper cites an unresolved cited work.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:08:36.898288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1d2e9d62-0221-43b0-aec6-6f22bad8787d · outbound

This paper cites an unresolved cited work.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:08:36.840289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation eb9d7afb-23fa-4b63-8082-bf92c0565f38 · outbound

This paper cites an unresolved cited work.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:08:36.736275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5397cc6c-1436-4332-82e7-38dbc79559b8 · outbound

This paper cites an unresolved cited work.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:08:36.666639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8183c204-c3b3-4b7c-8883-ee1285fb048a · outbound

This paper cites an unresolved cited work.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:08:36.532706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1ec8e91b-928a-4d11-9be6-d9e343440b86 · outbound

This paper cites an unresolved cited work.

Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:08:37.134527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 20e45bc6-1fa4-42c1-92c3-ac8967a9c9c3 · inbound

LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection cites this paper.

LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection Marginalized Generalized IoU (MGIoU): A Unified Objective Function for Optimizing Any Convex Parametric Shapes

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T18:26:12.344746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.