Songyao Jiang

I am a PhD candidate at Northeastern University, where I work on computer vision and machine learning in SmileLab advised by Dr. Yun (Raymond) Fu.

I am a co-founder of an AI beauty startup company Giaran, Inc., which was acquired by Shiseido Americas in Nov. 2017 (Here's the News).

I received my masters degree at the University of Michigan and my bachelors at The Hong Kong Polytechnic University.

I am also a skilled astronomy and landscape photographer, and here is my Little Gallery

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Research

I'm interested in computer vision, machine learning, image processing, and computational photography. Much of my research is about human faces and pose estimation.

Video-based Multi-person Pose Estimation and Tracking
Songyao Jiang, and Yun Fu
Current Work, 2019
Paper / GitHub

Video-based Multi-person Pose Estimation and Tracking. Under development and construction. Inferencing model provided on GitHub.

Face Recognition and Verification in Low-light Condition
Songyao Jiang, Yue Wu, Zhengming Ding, and Yun Fu
2018
Paper / GitHub

This project tends to solve the problem of recognizing people in low light condition, which is quite useful in security. In low-light condition, we usually utilize near IR, mid-range IR and long-range IR to obtain the portrait images of the target person. However, those IR images are very different than the visible images that we used to train our deep face recognition and verification methods. To utilize the knowledge we learned from visible images and apply it on IR images, we developed a semi-supervised and an unsupervised transfer learning methods to transfer the knowledge we learned from visible spectrum to IR spectrum. Based on which, we developed our low-light face recognition and verification system.

Spatially Constrained Generative Adversarial Networks for Conditional Image Generation
Songyao Jiang, Hongfu Liu, Yue Wu and Yun Fu
Submitted to a Springer Journal (under review), 2018
Paper / GitHub

Image generation has raised tremendous attention in both academic and industrial areas, especially for criminal portrait and fashion design. The current studies always focus on class labels as the condition where spatial contents are randomly generated. The edge details and spatial information is usually blurred and difficult to preserve. In light of this, we propose a novel Spatially Constrained Generative Adversarial Network , which decouples the spatial constraints from the latent vector and makes them feasible as additional controllable signals. Experimentally, we provide both visual and quantitive results, and demonstrate that the proposed SCGAN is very effective in controlling the spatial contents as well as generating high-quality images.

Segmentation Guided Image-to-Image Translation with Adversarial Networks
Songyao Jiang, Zhiqiang Tao and Yun Fu
IEEE International Conference on Automatic Face & Gesture Recognition (FG), 2019
Paper / GitHub / ArXiv

Recently image-to-image translation methods neglect to utilize higher-level and instance-specific information to guide the training process, leading to a great deal of unrealistic generated images of low quality. Existing methods also lack of spatial controllability during translation. To address these challenge, we propose a novel Segmentation Guided Generative Adversarial Networks, which leverages semantic segmentation to further boost the generation performance and provide spatial mapping. Experimental results on multi-domain face image translation task empirically demonstrate our ability of the spatial modification and our superiority in image quality over several state-of-the-art methods.

Rule-Based Facial Makeup Recommendation System
Taleb Alashkar, Songyao Jiang and Yun Fu
IEEE International Conference on Automatic Face & Gesture Recognition (FG), 2017
Paper / GitHub

Facial makeup style plays a key role in the facial appearance making it more beautiful and attractive. Choosing the best makeup style for a certain face to fit a certain occasion is a full art. To solve this problem computationally, an automatic and smart facial makeup recommendation and synthesis system is proposed in this paper. Additionally, an automatic facial makeup synthesis system is developed to apply the recommended style on the facial image as well. To this end, a new dataset with 961 different females photos collected and labeled.

Examples-Rules Guided Deep Neural Network for Makeup Recommendation
Taleb Alashkar, Songyao Jiang, Shuyang Wang and Yun Fu
AAAI Conference on Artificial Intelligence (AAAI), 2017
Paper / GitHub

We consider a fully automatic makeup recommendation system and propose a novel examples-rules guided deep neural network approach. The framework consists of three stages. First, makeup-related facial traits are classified into structured coding. Second, these facial traits are fed in- to examples-rules guided deep neural recommendation model which makes use of the pairwise of Before-After images and the makeup artist knowledge jointly. Finally, to visualize the recommended makeup style, an automatic makeup synthesis system is developed as well.


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