I am currently a final-year PhD candidate on video understanding under the supervision of Prof. Cees Snoek, in the VIS Lab, University of Amsterdam, The Netherlands.
Previously, I worked as a visiting researcher under the supervision of Prof. Ling Shao in the Artificial Intelligence Lab, School of Computing Sciences, University of East Anglia, UK. And from 2015 to 2016, I worked in Panasonic R&D Center Singapore and visited the Learning and Vision Group, ECE, National University of Singapore. I received my Master's Degree and Bachelor's Degree under the supervision of Prof. Maoguo Gong in the Department of Electronic Engineering, XiDian University, China.
I'm interested in computer vision and deep learning. During my PhD, I am mainly focusing on video understanding, specifically video action detection, action recognition, video object segmentation. I am also excited about getting inspiration from classical signal/image processing methodologies for addressing some basic problems in deep learning. Besides, I have worked on image colorization, face recognition/detection/aligment, and change detection.
By adopting the philosophy of the classical Lifting Scheme from signal processing, we propose LiftPool for bidirectional pooling layers, including LiftDownPool and LiftUpPool.
Go with the Flow: Aligned 3D Convolutions for Video Action Recognition Jiaojiao Zhao,
Cees Snoek
We present aligned 3D convolution blocks, which collect the valuable information from the locations aligned by the learned offsets rather than the original dislocated positions.
We propose a novel set-to-set (S2S) distance measure to calculate the similarity between two sets with the aim to improve the recognition accuracy for faces with real-world challenges, such as extreme poses or severe illumination conditions.
We establish a deep neural network using stacked Restricted Boltzmann Machines (RBMs) to analyze the difference images and detect changes between multitemporal synthetic aperture radar (SAR) images.