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

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arxiv 2301.10051 v3 pith:56LJ7WRA submitted 2023-01-24 cs.CV

Zanjia Tong , Yuhang Chen , Zewei Xu , Rong Yu This is my paper · ORCID
classification cs.CV
keywords losswiouanchorboxesdynamicexamplesnon-monotonicperformance
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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. InterpIoU: Rethinking Bounding Box Regression with Interpolation-Based IoU Optimization

    cs.CV 2025-07 conditional novelty 6.0 of 10

    InterpIoU replaces geometric penalty terms in IoU-based bounding box regression with an IoU term on an interpolated box, improving small object localization.

  2. Geometry Meets Semantics: Fractional Gradient Stabilization for Semantic-Driven Bounding Box Optimization in Visual Detection Tasks

    cs.CV 2026-07 conditional novelty 5.5 of 10

    FrSIoU couples semantic feature similarity with IoU and modulates the loss by a fractional history of sIoU to stabilize oriented-box angle gradients, with modest gains on DOTA, HRSC2016, and SSDD.

  3. FSDC-DETR: A Frequency-Spatial Domain Collaborative DETR for Small Object Detection

    cs.CV 2026-07 conditional novelty 5.5 of 10

    FSDC-DETR improves small object detection by explicitly modeling frequency-spatial representations through dual-branch adaptive fusion, shunt feature fusion, and wavelet-based dynamic downsampling, achieving state-of-...

  4. YOLO-FireAD: Efficient Fire Detection via Attention-Guided Inverted Residual Learning and Dual-Pooling Feature Preservation

    cs.CV 2025-05 conditional novelty 4.0 of 10

    YOLO-FireAD uses attention guided inverted residuals and fused max average pooling to reach 34.6% mAP50-95 with 1.45M parameters, about 1.8 points above YOLOv8n on one fire dataset.

  5. Improved YOLOv7 model for insulator defect detection

    cs.CV 2025-02 conditional novelty 4.0 of 10

    An improved YOLOv7 with RFB, coordinate attention, and WIoU loss raises insulator-defect mAP_0.5 from 93.3% to 94.9% on a private 3000-image dataset.

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