PolyU Hong Kong & Alibaba to establish FashionAI Dataset
Courtesy: Polytechnic University
The Institute of Textiles and Clothing (ITC) of The Hong Kong Polytechnic University (PolyU) has collaborated with the vision and beauty team at Alibaba Group to establish FashionAI Dataset. It will carry out systematic analysis and labelling of fashion images based on fashion attributes (fashion characteristics) and key points of apparel.
By integrating fashion knowledge and machine learning formulation, the establishment of the Dataset will enable machine to better understand fashion, bringing a new horizon to the fashion retail industry through the application of artificial intelligence (AI).
"Transforming fashion knowledge into determination of fashion related attributes and fashion item categorisation of the fashion image database is a very complicated and challenging task, while it is the most fundamental task in deep learning applications. ITC is pleased to collaborate with Alibaba to address the needs of fashion retailers and consumers," said professor Calvin Wong, Cheng Yik Hung professor in fashion and associate head of ITC.
"There is a huge potential for AI applications in the fashion industry. In order for AI to understand fashion, which could be very subjective, we need to turn fashion knowledge and experience into language that machine can understand. We hope to work with academics and the industry alike to explore the wider applications of AI in scenarios including fashion mix-and-match, assisting design and shopping guide, with the aim to bring new values to the fashion industry. The traditional fashion sector should embrace the new retail practice, and we hope FashionAI can be a bridge that connects AI with fashion," said Menglei Jia, senior staff engineer at the vision and beauty team at Alibaba.
Current fashion image searching technology used on online platforms is based on the whole fashion image to search the exact or other similar images. However, if a customer is interested in some particular fashion attributes of a fashion image and wants to search other fashion items with these attributes, the current searching technology cannot meet the needs of the customer. This greatly limits the potential development and applications for offering more customised shopping experience.
From AI research perspective, the limitation of the current image searching technology is caused by the absence of available fashion image dataset constructed with both fashion professional knowledge and fulfils the requirement of deep learning, i.e. the current technology is unable to train a machine to accurately understand and recognise the fashion attributes of each fashion image, said the two entities in a press release.
Fostering the application of AI in the fashion industry, a PolyU research team led by professor Wong, worked closely with Alibaba to develop FashionAI Dataset to solve two fundamental problems of the deep learning algorithm: apparel key points detection and attribute recognition.
Key points (e.g. neckline, cuff, waistline) and fashion attributes (e.g. sleeve length, collar type, skirt style) build the foundation for machine learning in understanding fashion images. The establishment of key points and fashion attribute database enables the computer to effectively and efficiently understand the fashion image which is fundamental for deep learning and recognition algorithms.
The accuracy of key points detection is determined by several factors such as the dimension and shape of the apparel, distance and angle of shooting, or even how the apparel is displayed or the model is posing in a photo. These factors can lead to poor key points detection and result in an inaccurate analysis of fashion images by the computer. Accurate key points detection can therefore improve the performance of deep learning algorithms.
Fashion attributes are the basic design elements of an apparel, and their combination determines the product category and styles of a fashion item. With the wide variety of fashion attributes, attribute recognition is a complicated process. A systemic classification of fashion attributes is essential to accurately label fashion attributes, facilitating research on deep learning and algorithm design for fashion image searching, navigating tagging and mix-and-match ideas, etc.
The Dataset can facilitate understanding fashion images and related algorithm design, and developing machine learning. It would help improve the accuracy of online fashion image searching, enhance effectiveness of cross-selling and up-selling, create innovative buying experience and facilitate customisation of online shopping platforms.
PolyU and Alibaba will also host two world-first events: Artificial Intelligent on Fashion & Textile (AIFT) conference and FashionAI Global Challenge.
AIFT Conference is a first-of-its-kind academic conference to bring together researchers, engineers and practitioners to share their insights on the most updated development and applications of AI and fashion. It will be held from July 3-6, 2018 at PolyU. This event will become an annual activity for academic exchange and networking with like-minded individuals who are redefining the world of AI and fashion and advancing AI research in fashion and textile.
FashionAI Challenge was open until April 21 for worldwide AI researchers and developers to solve two imminent issues on the application of AI in fashion with over 400,000 images with high-quality annotations from Alibaba ecommerce platforms. (KD)
Fibre2Fashion News Desk – India