Location-aware Fashion Attribute Recognition and Retrieval

Automatic fashion attribute recognition enables retailers to address an array of applications. Usually, fashion attributes are manually input in the system by retailers, which is a time-consuming and an error prone process. To alleviate this, several existing works use traditional CNN-based backbones to recognize attributes. These backbones generate attribute embeddings that are entangled in the feature space. Existing methods that generate disentangled attribute embedding do not explicitly specify the location of attributes, and often extract features from irrelevant regions. This directly impacts the quality of downstream tasks. To alleviate this problem, we have proposed a novel framework to extract location-aware attribute representation using localization maps created from fashion landmarks. These localization maps highlight regions of interest in an image, aiding localized attribute feature extraction. Moreover, we have proposed a novel fusion module to effectively select important features from the global representation of an image to enhance the local features of the attribute. These attribute embeddings are then used in downstream applications such as attribute recognition, hierarchical taxonomy classification, and retrieval with two large-scale datasets. Using the proposed model, we observe improvement in performance from the state-of-the-art by a significant margin.