For existing object detectors, anchor-based detectors lack global information, while anchor-free detectors based on key points lack prior position information. The above issues may lead to the imbalance of detection accuracy and shape robustness. In order to alleviate the above contradictions, a new auxiliary network key point generation network (GPNet) is proposed to improve the performance of existing object detectors. Specifically, a series of key points are generated through ground truth (GT) supervision to obtain more global information. These key points generate pseudo boxes (Pbox) with learnable parameters. Pbox has more specific prior information than the manually designed candidate boxes. The scale information of the Pbox is embed into the classification branch to obtain a more appropriate receptive field. In addition, a novel improved strategy for label assignment by combining Pbox and GT to enhance the ability to classify positive and negative samples is proposed. Extensive experiments on multiple dense prediction methods validate the effectiveness of GPNet, with a performance improvement of 1.5 AP over baseline. In particular, with ResNext-101-64× 4d-DCN as the backbone, this method achieves 49.5 AP with single-scale testing.

中文翻译：

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GPNet：用于目标检测的关键点生成辅助网络

对于现有的目标检测器，anchor-based 检测器缺乏全局信息，而基于关键点的 anchor-free 检测器缺乏先验位置信息。上述问题可能导致检测精度和形状鲁棒性的不平衡。为了缓解上述矛盾，提出了一种新的辅助网络关键点生成网络（GPNet）来提高现有物体检测器的性能。具体来说，通过地面实况（GT）监督生成一系列关键点，以获得更多的全局信息。这些关键点生成具有可学习参数的伪框（Pbox）。Pbox 比手动设计的候选框具有更具体的先验信息。Pbox的尺度信息被嵌入到分类分支中以获得更合适的感受野。此外，提出了一种新的标签分配改进策略，通过结合 Pbox 和 GT 来增强对正样本和负样本的分类能力。对多种密集预测方法的大量实验验证了 GPNet 的有效性，性能比基线提高了 1.5 AP。特别是，以 ResNext-101-64× 4d-DCN 为骨干，该方法通过单尺度测试实现了 49.5 AP。