Multi-modal aerial view object classification (MAVOC) in Automatic target recognition (ATR), although an important and challenging problem, has been under studied. This paper firstly finds that fine-grained data, class imbalance and various shooting conditions preclude the representational ability of general image classification. Moreover, the MAVOC dataset has scene aggregation characteristics. By exploiting these properties, we propose Scene Clustering Based Pseudo-labeling Strategy (SCP-Label), a simple yet effective method to employ in post-processing. The SCP-Label brings greater accuracy by assigning the same label to objects within the same scene while also mitigating bias and confusion with model ensembles. Its performance surpasses the official baseline by a large margin of +20.57% Accuracy on Track 1 (SAR), and +31.86% Accuracy on Track 2 (SAR+EO), demonstrating the potential of SCP-Label as post-processing. Finally, we win the championship both on Track1 and Track2 in the CVPR 2022 Perception Beyond the Visible Spectrum (PBVS) Workshop MAVOC Challenge. Our code is available at https://github.com/HowieChangchn/SCP-Label.
翻译:在自动目标识别(ATR)中,多式航空视图物体分类(MAVOC)虽然是一个重要和具有挑战性的问题,但已经对此进行了研究。本文件首先发现,细微的分类数据、阶级不平衡和各种射击条件排除了一般图像分类的代表性能力。此外,MAVOC数据集具有场景汇总特性。通过利用这些特性,我们建议采用基于环境的集合化标签战略(SCP-Label),这是在后处理中采用的一种简单而有效的方法。SCP-Label通过在同一场景中为同一对象指定相同的标签,同时减轻与模型组合的偏差和混淆,从而带来更高的准确性。其性能超过官方基线的幅度为+20.57%轨道1(SAR)和+31.86%轨道2(SAR+EO)的准确性能,展示SCP-Label在后处理中的潜力。最后,我们在CVPR 2022轨中赢得了第1和轨道2级的冠军,同时减轻了对模型的偏差和混淆。其性与模型的偏差。其性超过20.57.57/S/C/LAVC/LAVACS在可操作上,MAVC/C MAVC/C/CSOVACVS SAVAVLO可以提供。