Hello! I'm Yu-Ting Chang.
I receieved my B.S. degree in ECE Department at Taiwan National Chiao Tung University in 2015. I am currently working as a research assistant at Multimedia Computing Lab, Academia Sinica, Taiwan.
My research covers the field of Machine Learning, Multimedia, and Computer Vision. I focus on developing neural networks in the application of fashion and art related research, specialized in model combination, such as CNN based joint-training model and generative adversarial networks.
I have strong multimedia cumputing background and I am especially interested in image editing techniques.
I am interested in ML algorithms. I especially focus on building deep learning architectures by Keras, Tensorflow.
I love fashion and art! I am insterested in image retrieval, recommendation system, and style transfer techniques.
1. Yu-Ting Chang, Wen-Huang Cheng, Kai-Lung Hua, and Bo Wu, "Fashion World Map: Understanding Cities Through Streetwear Fashion," The 25th ACM International Conference on Multimedia (MM 2017), 23-27 October, 2017, Mountain View, USA.
2. Yu-Ting Chang, Min-Jhih Huang, Jorga Hu Yu, Cheng-Chun Hsu, Kai-Lung Hua, and Wen-Huang Cheng, "Fashion Eye: Understanding Streetwear Fashion Style," ChinaMM2017, Nanjing, China, September 2017.
My work was accepted as a full paper by the 25th ACM Multimedia!
I'm going to attend 2017 ACM Multimedia Conference @ Mountain View, CA, USA
I'm going to present Fashion World Map demo system at 2017 China Multimedia Conference (ChinaMM) @ Guangzhou, China
A novel framework for depicting the street fashion of a city by discovering fashion items in a particular look that are most iconic for the city.
I collected Global Fashion Dataset which contains over 400K street fashion pictures and their user provided associated data.
We aim to explore the iconic item of a city based on deep neural networks and PCST algorithm.
We create a fashion world map which shows the iconic street fashion of global major cities. It shows how the visual impression of local fashion cultures across the world can be depicted, modeled, and analyzed.
We provided insights and observations according to our result. We can infer the Correlation of Clothing Color and City Latitude, Influence of International Clothing Brands on Local Fashion.
A developed system to discover the city components from the user’s outfit. The application can profile the fashion taste of the users and offer smart insights embedded within the picture.
Oct. 2017 @ California, USA
Sep. 2017 @ Guangzhou, China
Nov. 2017 @ Taipei, Taiwan
No.128, Sec.2, Academia Rd.,