Siqi Wang

I am a third year Computer Science PhD student at Boston University, Image and Video Computing Group, where I'm fortunate to be advised by Prof. Bryan A. Plummer. Before joining BU, I was advised by Prof. Daniel Ritchie at Brown Visual Computing and received my Master degree in Computer Science in 2020. I received my Bachelor’s degree in Computer Science at Beihang University, Beijing, China in 2018.

siqiwang@bu.edu / CV / Google Scholar / LinkedIn

Teaching Fellow

Research

My research interests fall within the umbrella of machine learning and computer vision. Right now, I'm focusing on learning image similarity. My ongoing research is about learning the stylistic compatibility between furnitures of the scene and fashion compatibility of the outfits. I'm interested in designing models with good taste in art.

	Anchoring to Exemplars for Training Mixture-of-Expert Cell Embeddings Siqi Wang, Manyuan Lu, Nikita Moshkov, Juan C. Caicedo, Bryan A. Plummer, arXiv, 2021 arXiv Treatment ExemplArs with Mixture-of-experts (TEAMs), an embedding learning approach that learns a set of experts that are specialized in capturing technical variations in our training set and then aggregates specialist's predictions at test time.
	Stylistic Compatibility Learning with Deep Neural Networks for Indoor Scene Siqi Wang, Daniel Ritchie (advisor), Master Project Report, 2020 A deep neural network with conditioning method to learn the scene style.
	An Efficient Adaptive Algorithm for Removal of Impulse Noises Siqi Wang, Tongyu Yue, Bo Lang, International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017 Adaptive Min-Max Average Filters (AMMAF) for the removal of impulse noises.
	Efficient Segmentation for Region-based Image Retrieval Using Edge Integrated Minimum Spanning Tree Yang Liu, Lei Huang, Siqi Wang, Xianglong Liu, Bo Lang, International Conference on Pattern Recognition (ICPR), 2016 A RBIR-oriented image segmentation algorithm named Edge Integrated Minimum Spanning Tree (EI-MST).
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