[论文] A Turbo-Inference Strategy for Object Detection and Instance Segm...

论文概要

研究领域: CV
作者: Zhen Zhao, Gang Zhang, Xiaolin Hu, Liang Tang
发布时间: 2026-06-10
arXiv: 2606.12371

中文摘要

目标检测和实例分割任务密切相关。现有自上而下实例分割方法通常遵循先检测后分割范式，其中初始检测器用于识别和定位带边界框的对象，随后在每个边界框内分割实例掩膜。在这些方法中，检测精度直接影响后续分割性能。然而，先前研究很少探索实例分割任务对目标检测的影响。本文提出自上而下方法的turbo推理策略，迭代利用检测和分割任务之间的互补信息。具体而言，我们设计两个模块：turbo检测头和turbo分割头，促进任务间通信。两个模块形成闭环，交织检测和分割结果，无需重新训练模型。在COCO、iFLYTEK和Cityscapes数据集上的全面实验表明，我们的方法在计算成本一定增加的情况下大幅提升检测和分割精度。所提方法代表预测精度和推理速度之间的权衡。代码可在 https://github.com/zhaozhen2333/Turbo-Learning.git 获取。

原文摘要

Object detection and instance segmentation tasks are closely related. Existing top-down instance segmentation methods usually follow a detect-then-segment paradigm, where an initial detector is used to recognize and localize objects with bounding boxes, followed by the segmentation of an instance mask within each bounding box. In such methods, the detection accuracy directly influences the subsequent segmentation performance. However, previous research has seldom explored the impact of the instance segmentation task on object detection. In this paper, we present a turbo-inference strategy for the top-down methods that leverages the complementary information between detection and segmentation tasks iteratively. Specifically we design two modules: turbo-detection head and turbo-segmentation ...

自动采集于 2026-06-12

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