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 a first-year M.S. student in UC Merced Vision and Learning Lab, advised by Prof. Ming-Hsuan Yang.
My research covers the field of Computer Vision and Deep Learning. I focus on developing neural networks in the application of image/video generation and synthesis.
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 and PyTorch.
I love fashion and art! I am insterested in image retrieval, recommendation system, and style transfer techniques.
1. Fashion World Map: Understanding Cities Through Streetwear Fashion
Yu-Ting Chang, Wen-Huang Cheng, Kai-Lung Hua, Bo Wu
ACM International Conference on Multimedia (ACM MM), 2017
2. Fashion Eye: Understanding Streetwear Fashion Style
Yu-Ting Chang, Min-Jhih Huang, Jorga Hu Yu, Cheng-Chun Hsu, Kai-Lung Hua, Wen-Huang Cheng
ACM Multimedia 2017 China Pre-conference (Invited Demonstration), 2017
3. What Dress Fits Me Best?: Fashion Recommendation on the Clothing Style forPersonal Body Shape
Shintami Chusnul Hidayati, Cheng-Chun Hsu, Yu-Ting Chang, Kai-Lung Hua, Jianlong Fu, Wen-Huang Cheng
ACM International Conference on Multimedia (ACM MM), 2018
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
311 S&E Building 2 UC Merced, CA 95343