论文标题
重新思考行人属性识别：具有高效方法的现实数据集
Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method
论文作者
论文摘要
尽管提出了各种方法来在行人属性识别方面取得进展，但现有数据集上的一个关键问题通常被忽略，即在火车和测试集中有很多相同的行人身份，这与实际应用不一致。因此，火车集和测试集中相同的行人身份的图像非常相似，从而导致现有数据集中最先进的方法的性能高估了。为了解决此问题，我们建议两个现实的数据集PETA \ TextSubScript {$ ZS $}和RAPV2 \ TextSubScript {$ ZS $}，遵循基于PETA和RAPV2数据集的行人身份的零拍设置。此外，与我们强大的基线方法相比，我们观察到，最近的最新方法无法对PETA，RAPV2，PETA \ TextSubScript {$ ZS $}和RAPV2 \ TextSubScript {$ ZS $}进行改进。因此，通过解决行人属性识别中固有的属性不平衡，提出了一种有效的方法来进一步改善性能。对现有和提议的数据集进行的实验通过实现最新性能来验证我们方法的优势。
Despite various methods are proposed to make progress in pedestrian attribute recognition, a crucial problem on existing datasets is often neglected, namely, a large number of identical pedestrian identities in train and test set, which is not consistent with practical application. Thus, images of the same pedestrian identity in train set and test set are extremely similar, leading to overestimated performance of state-of-the-art methods on existing datasets. To address this problem, we propose two realistic datasets PETA\textsubscript{$zs$} and RAPv2\textsubscript{$zs$} following zero-shot setting of pedestrian identities based on PETA and RAPv2 datasets. Furthermore, compared to our strong baseline method, we have observed that recent state-of-the-art methods can not make performance improvement on PETA, RAPv2, PETA\textsubscript{$zs$} and RAPv2\textsubscript{$zs$}. Thus, through solving the inherent attribute imbalance in pedestrian attribute recognition, an efficient method is proposed to further improve the performance. Experiments on existing and proposed datasets verify the superiority of our method by achieving state-of-the-art performance.
