论文标题
基于场景聚类基于多模式空中视图对象分类的伪标记策略
Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classification
论文作者
论文摘要
自动目标识别（ATR）中的多模式空中视图对象分类（MAVOC）虽然有一个重要且具有挑战性的问题，但仍未研究。本文首先发现，细粒度的数据，类不平衡和各种拍摄条件排除了一般图像分类的代表性。此外，MAVOC数据集具有场景聚合特征。通过利用这些属性，我们提出了基于场景聚类基于伪标记策略（SCP-Label），这是一种简单而有效的方法。 SCP标签通过将相同的标签分配给同一场景中的对象，同时减轻模型集合的偏见和混淆，从而带来了更高的精度。它的性能超过了正式基线，在轨道1（SAR）上的准确度 +20.57％，轨道2（SAR +EO）的精度 +31.86％，这表明SCP标签作为后处理的潜力。最后，我们在CVPR 2022感知中赢得了Track1和Track2的冠军，超出了可见频谱（PBVS）车间Mavoc Challenge。我们的代码可从https://github.com/howiechangchn/scp-label获得。
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.
