  Fine-grained multi-label classification models have broad applications in
e-commerce, 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.
