Methods › Computer Vision › Localization Models › IoU-Net

IoU-Net

3 papers tagged archive 2025-07-28

Introduced by Borui Jiang et al. in Acquisition of Localization Confidence for Accurate Object Detection

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

IoU-Net is an object detection architecture that introduces localization confidence. IoU-Net learns to predict the IoU between each detected bounding box and the matched ground-truth. The network acquires this confidence of localization, which improves the NMS procedure by preserving accurately localized bounding boxes. Furthermore, an optimization-based bounding box refinement method is proposed, where the predicted IoU is formulated as the objective.

PaperSource

Papers archive 2025-07-28

3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

8 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
regression2
General Classification1
Image-to-Image Translation1
Object1
Object Detection1
Prediction1
Rgb-T Tracking1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with IoU-Net: 2018 to 2020, peak 1 1 0 2018: 1 paper 2018 2019: 1 paper 2019 2020: 1 paper 2020
Papers per year the archive tags with this method, by the paper's archive date (3 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Localization Models

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