Fashion Image Analysis using Single-stage Detector

Abstract

Clothing detection and landmark detection are important techniques in fashion image analysis. The availability of large annotated fashion datasets has made fashion image analysis a hot research topic. This paper proposes a single-stage detector that performs bounding box detection, fashion landmark detection, and can also predict end-to-end clothing category classification. This parallel processing provides improved time efficiency than the later technique that performs regional proposals first and then prediction module. The proposed network is designed with the revision of the EfficientDet model announced by Google Brain. The proposed approach can also be used within a real application because it can operate efficiently and quickly from the inference latency perspective.