Multi-label classification models have a wide range of applications in E-commerce, including visual-based label predictions and language-based sentiment classifications. A major challenge in achieving satisfactory performance for these tasks in the real world is the notable imbalance in data distribution. For instance, in fashion attribute detection, there may be only six 'puff sleeve' clothes among 1000 products in most E-commerce fashion catalogs. To address this issue, we explore more data-efficient model training techniques rather than acquiring a huge amount of annotations to collect sufficient samples, which is neither economic nor scalable. In this paper, we propose a state-of-the-art weighted objective function to boost the performance of deep neural networks (DNNs) for multi-label classification with long-tailed data distribution. Our experiments involve image-based attribute classification of fashion apparels, and the results demonstrate favorable performance for the new weighting method compared to non-weighted and inverse-frequency-based weighting mechanisms. We further evaluate the robustness of the new weighting mechanism using two popular fashion attribute types in today's fashion industry: sleevetype and archetype.

翻译：多标签分类模型在电子商务领域具有广泛应用，包括基于视觉的标签预测和基于语言的情绪分类。在实际应用中实现这些任务的理想性能面临的一大挑战是数据分布的显著不平衡。例如，在时尚属性检测中，大多数电商时尚目录中1000件商品里可能仅有6件"泡泡袖"服装。为解决该问题，我们探索了更具数据效率的模型训练技术，而非通过获取大量标注来收集充足样本——这既不经济也不具备可扩展性。本文提出了一种前沿的加权目标函数，用于提升深度神经网络（DNN）在长尾数据分布下的多标签分类性能。实验基于时装图像属性分类任务，结果表明相较于非加权和基于逆频率的加权机制，新提出的加权方法展现出更优性能。我们进一步采用当今时尚行业流行的两种属性类型——袖型与原型——验证了新加权机制的鲁棒性。