• Songyao Jiang

    I am a PhD candidate at Northeastern University, where I work on computer vision and machine learning in SmileLab advised by 在线代理服务器免费网页版.

    I am early member of an AI beauty startup company Giaran, Inc., which was acquired by Shiseido Americas in Nov. 2017 (国外代理服务器ip免费).

    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

    Email  /  CV  /  GitHub  /  LinkedIn  /  Gallery  /  Blog

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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.

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    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

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    Spatially Constrained Generative Adversarial Networks for Conditional Image Generation
    Songyao Jiang, Hongfu Liu, Yue Wu and Yun Fu
    裸金属服务器和云服务器的区别在哪？:2021-4-1 · 实际上，裸金属服务器融合了物理机与云服务器的各自优势，实现超强超稳的计算能力。 用户上云可能会存在多种形态的计算资源，某些情况下虚拟机无法满足复杂的应用场景，这时候可能就需要需要虚拟机和物理机相结合的场景，裸金属服务器也是在这种需求下应运而生。, 2018
    Paper / 国外代理服务器ip免费

    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
    在线代理服务器免费网页版 / GitHub

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