Average precision (AP) loss has recently shown promising performance on the dense object detection task. However,a deep understanding of how AP loss affects the detector from a pairwise ranking perspective has not yet been developed.In this work, we revisit the average precision (AP)loss and reveal that the crucial element is that of selecting the ranking pairs between positive and negative samples.Based on this observation, we propose two strategies to improve the AP loss. The first of these is a novel Adaptive Pairwise Error (APE) loss that focusing on ranking pairs in both positive and negative samples. Moreover,we select more accurate ranking pairs by exploiting the normalized ranking scores and localization scores with a clustering algorithm. Experiments conducted on the MSCOCO dataset support our analysis and demonstrate the superiority of our proposed method compared with current classification and ranking loss. The code is available at https://github.com/Xudangliatiger/APE-Loss.
翻译:平均精确度损失(AP)最近显示,在密集物体探测任务上,平均精确度(AP)损失表现良好。然而,尚未从对称排名的角度深入了解AP损失如何影响探测器。我们在此工作中重新审视平均精确度(AP)损失,并发现关键因素是选择正样和负样的对等。我们根据这一观察,提出了两项战略来改进AP损失,第一项是新的适应性对称错误(APE)损失,其重点是正样和负样的对等。此外,我们通过利用组合算法来利用归正分分和本地化得分来选择更准确的对等。在MCCO数据集上进行的实验支持了我们的分析,并展示了我们拟议方法相对于当前分类和排名损失的优越性。该代码可在https://github.com/Xudangliatiger/APE-Loss查阅。