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 sentiment 分类。在实际应用中，实现这些任务满意性能的主要挑战在于数据分布的显著不均衡性。例如，在时尚属性检测中，大多数电商时尚目录中的1000件商品可能仅有6件'泡泡袖'服装。为解决该问题，我们探索更高效的数据模型训练技术，而非通过获取海量标注来收集充足样本——这既不经济也不具备可扩展性。本文提出一种先进加权目标函数，用于提升深度神经网络在长尾数据分布下执行多标签分类任务的性能。实验基于时尚服饰图像属性分类，结果表明相较于非加权机制和基于逆频率的加权机制，新加权方法展现出更优性能。我们进一步使用当今时尚产业中两种主流时尚属性类型（袖型与原型）评估了新加权机制的鲁棒性。