# Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism

arXiv: https://arxiv.org/abs/2301.10051 · HTML: https://mlsanity.com/paper/2301.10051/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 24 Jan 2023.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [UnitBox: An Advanced Object Detection Network](https://mlsanity.com/paper/1608.01471/) (2016)
- [FCOS: A simple and strong anchor-free object detector](https://mlsanity.com/paper/2006.09214/) (2020)
- [Single-Shot Refinement Neural Network for Object Detection](https://mlsanity.com/paper/1711.06897/) (2017)
- [Focal and Efficient IOU Loss for Accurate Bounding Box Regression](https://mlsanity.com/paper/2101.08158/) (2021)
- [SIoU Loss: More Powerful Learning for Bounding Box Regression](https://mlsanity.com/paper/2205.12740/) (2022)
- [PP-YOLO: An Effective and Efficient Implementation of Object Detector](https://mlsanity.com/paper/2007.12099/) (2020)

## Built on by (most cited first, 30 of 30)

- [Inner-IoU: More Effective Intersection over Union Loss with Auxiliary Bounding Box](https://mlsanity.com/paper/2311.02877/) (2023)
- [Lightweight Full-Convolutional Siamese Tracker](https://mlsanity.com/paper/2310.05392/) (2023)
- [BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection](https://mlsanity.com/paper/2309.12585/) (2023)
- [Transformer-based RGB-T Tracking with Channel and Spatial Feature Fusion](https://mlsanity.com/paper/2405.03177/) (2024)
- [Student Classroom Behavior Detection based on Improved YOLOv7](https://mlsanity.com/paper/2306.03318/) (2023)
- [SOD-YOLOv8 -- Enhancing YOLOv8 for Small Object Detection in Traffic Scenes](https://mlsanity.com/paper/2408.04786/) (2024)
- [Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices](https://mlsanity.com/paper/2412.02171/) (2024)
- [A Novel real-time arrhythmia detection model using YOLOv8](https://mlsanity.com/paper/2305.16727/) (2023)
- [Unified-IoU: For High-Quality Object Detection](https://mlsanity.com/paper/2408.06636/) (2024)
- [PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplanar MRI Slices](https://mlsanity.com/paper/2410.21822/) (2024)
- [LAM-YOLO: Drones-based Small Object Detection on Lighting-Occlusion Attention Mechanism YOLO](https://mlsanity.com/paper/2411.00485/) (2024)
- [InterpIoU: Rethinking Bounding Box Regression with Interpolation-Based IoU Optimization](https://mlsanity.com/paper/2507.12420/) (2025)
- [CP-WSP: A Declarative CP-SAT Framework for Configurable Multi-Constraint Workforce Scheduling](https://mlsanity.com/paper/2607.05177/) (2026)
- [Fast vehicle detection algorithm based on lightweight YOLO7-tiny](https://mlsanity.com/paper/2304.06002/) (2023)
- [A lightweight YOLOv5-FFM model for occlusion pedestrian detection](https://mlsanity.com/paper/2408.06633/) (2024)
- [Hybrid Classification-Regression Adaptive Loss for Dense Object Detection](https://mlsanity.com/paper/2408.17182/) (2024)
- [Dynamic Attention and Bi-directional Fusion for Safety Helmet Wearing Detection](https://mlsanity.com/paper/2411.19071/) (2024)
- [Improving Generalization Performance of YOLOv8 for Camera Trap Object Detection](https://mlsanity.com/paper/2412.14211/) (2024)
- [EDNet: Edge-Optimized Small Target Detection in UAV Imagery -- Faster Context Attention, Better Feature Fusion, and Hardware Acceleration](https://mlsanity.com/paper/2501.05885/) (2025)
- [Improved YOLOv7 model for insulator defect detection](https://mlsanity.com/paper/2502.07179/) (2025)
- [DEAL-YOLO: Drone-based Efficient Animal Localization using YOLO](https://mlsanity.com/paper/2503.04698/) (2025)
- [YOLO-FireAD: Efficient Fire Detection via Attention-Guided Inverted Residual Learning and Dual-Pooling Feature Preservation](https://mlsanity.com/paper/2505.20884/) (2025)
- [PACGNet: Pyramidal Adaptive Cross-Gating Network for Multimodal Object Detection in Aerial Imagery](https://mlsanity.com/paper/2512.18291/) (2025)
- [LAF-YOLOv10 with Partial Convolution Backbone, Attention-Guided Feature Pyramid, Auxiliary P2 Head, and Wise-IoU Loss for Small Object Detection in Drone Aerial Imagery](https://mlsanity.com/paper/2602.13378/) (2026)
- [MATHENA: Mamba-based Architectural Tooth Hierarchical Estimator and Holistic Evaluation Network for Anatomy](https://mlsanity.com/paper/2604.00537/) (2026)
- [MoEIoU: Rethinking Bounding-Box Regression as a Mixture of Experts](https://mlsanity.com/paper/2606.00844/) (2026)
- [Geometry Meets Semantics: Fractional Gradient Stabilization for Semantic-Driven Bounding Box Optimization in Visual Detection Tasks](https://mlsanity.com/paper/2607.23530/) (2026)
- [GRIPNet: Gaussian Radial Intensity Prior Guided Architecture for Pulmonary Nodule Detection in CT](https://mlsanity.com/paper/2609.11312/) (2026)
- [Contour-Guided Spectral Routing for Robust Real-Time Pedestrian Detection](https://mlsanity.com/paper/2609.14383/) (2026)
- [Small Object Detection in Drone Aerial Imagery with LAF-YOLOv10](https://mlsanity.com/paper/2609.14560/) (2026)

## Abstract (arXiv)

The loss function for bounding box regression (BBR) is essential to object detection. Its good definition will bring significant performance improvement to the model. Most existing works assume that the examples in the training data are high-quality and focus on strengthening the fitting ability of BBR loss. If we blindly strengthen BBR on low-quality examples, it will jeopardize localization performance. Focal-EIoU v1 was proposed to solve this problem, but due to its static focusing mechanism (FM), the potential of non-monotonic FM was not fully exploited. Based on this idea, we propose an IoU-based loss with a dynamic non-monotonic FM named Wise-IoU (WIoU). The dynamic non-monotonic FM uses the outlier degree instead of IoU to evaluate the quality of anchor boxes and provides a wise gradient gain allocation strategy. This strategy reduces the competitiveness of high-quality anchor boxes while also reducing the harmful gradient generated by low-quality examples. This allows WIoU to focus on ordinary-quality anchor boxes and improve the detector's overall performance. When WIoU is applied to the state-of-the-art real-time detector YOLOv7, the AP-75 on the MS-COCO dataset is improved from 53.03% to 54.50%. Code is available at https://github.com/Instinct323/wiou.