DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are difficult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion1, a large-scale clothes dataset with comprehensive annotations. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effectiveness of FashionNet and the usefulness of DeepFashion.

1. Introduction

Recently, extensive research efforts have been devoted to clothes classification [11], [1], [29], attribute prediction [3], [13], [4], [24], and clothing item retrieval [17], [6], [10], [27], [15], because of their potential values to the industry. However, clothes recognition algorithms are often confronted with three fundamental challenges when adopted in realworld applications [12]. First, clothes often have large variations in style, texture, and cutting, which confuse existing systems. Second, clothing items are frequently subject to deformation and occlusion. Third, clothing images often exhibit serious variations when they are taken under different scenarios, such as selfies vs. online shopping photos.