Xijie Huang (Owen) 黄悉偈

I am a PhD Student at Hong Kong University of Science and Technology (HKUST), where I work on efficient deep learning (algorithm-hardware co-design) and computer vision. I am a member of Vision and System Design Lab (VSDL), advised by Prof. Tim Kwang-Ting CHENG and Prof. Zhiqiang Shen. Previously, I work with Prof.Cewu Lu from SJTU and Prof.Mani Srivastava from UCLA.

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My research interests lie in the general area of artificial intelligence, particularly in model compression and its applications in computer vision. More concretely, My research interests focus on quantization, pruning, algorithm-hardware co-design, and human-centric computer vision. Representative papers are highlighted.

A novel stochastic quantization framework to learn the optimal mixed precision quantization strategy.

An analysis of the underlying difficulty of ViT quantization in the view of variation. A multi-crop knowledge distillation-based quantization method is proposed.

An automatic, vision-based system for monitoring and analyzing the physical and mental well-being of senior citizens.

A transferable knowledge learner and can be cooperated with any HOI detection models to achieve desirable results. TIN outperforms state-of-the-art HOI detection results by a great margin, verifying its efficacy and flexibility.

A large-scale knowledge base PaStaNet, which contains 7M+ PaSta annotations. And two corresponding models are proposed: first, we design a model named Activity2Vec to extract PaSta features, which aim to be general representations for various activities.

A latent fingerprint enhancement method based on the progressive generative adversarial network (GAN).

A large-scale Human Activity Knowledge Engine (HAKE) based on the human body part states to promote the activity understanding.

A framework to detect trojan backdoors in DNNs via output explanation techniques

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