Fine-grained multi-label classification models have broad applications in
Amazon production features, such as visual based label predictions ranging from
fashion attribute detection to brand recognition. One challenge to achieve
satisfactory performance for those classification tasks in real world is the
wild visual background signal that contains irrelevant pixels which confuses
model to focus onto the region of interest and make prediction upon the
specific region. In this paper, we introduce a generic semantic- embedding deep
neural network to apply the spatial awareness semantic feature incorporating a
channel- wise attention based model to leverage the localization guidance to
boost model performance for multi- label prediction. We observed an
Avg.relative improvement of 15.27% in terms of AUC score across all labels
compared to the baseline approach. Core experiment and ablation studies involve
multi-label fashion attribute classification performed on Instagram fashion
apparels' image. We compared the model performances among our approach,
baseline approach, and 3 alternative approaches to leverage semantic features.
Results show favorable performance for our approach.

本文介绍了一种使用语义嵌入深度神经网络，采用基于空间感知语义特征和通道注意力模型的方法来提高多标签预测模型性能的方法。实验证明，与基线方法相比，该方法平均相对改进 15.27％，在 Instagram 时尚服装图像上进行了多标签时尚属性分类的核心实验和消融研究，并将模型性能与我们的方法进行比较，发现我们的方法性能更优秀。