Fang Zhao

I am an Associate Professor at the School of Intelligence Science and Technology, Nanjing University. My research is currently on visual understanding in an open world, transfer learning and anomaly detection. Previously, I was a senior researcher in Tencent AI Lab from 2021 to 2023, and a research scientist in Inception Institute of Artificial Intelligence (IIAI) from 2018 to 2021, where I worked with Prof. Shengcai Liao. I was a research fellow in National University of Singapore (NUS) from 2015 to 2017, co-supervised by Prof. Jiashi Feng and Prof. Shuicheng Yan. I received my Ph.D. degree from Institute of Automation, Chinese Academy of Sciences (CASIA) under the supervision of Prof. Liang Wang.

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This paper proposes a novel light-weight module, the Attentive WaveBlock (AWB), which can be integrated into the dual networks of mutual learning to enhance the complementarity.

A Noise Resistible Mutual-Training (NRMT) method, which maintains two networks during training to perform collaborative clustering and mutual instance selection.

A novel Dynamic Conditional Convolutional Network (DCCN) is proposed to handle conditional few-shot learning, i.e, only a few training samples are available for each condition.

This paper proposes an anchor-based deformation model, namely AnchorDEF, to predict 3D garment animation from a body motion sequence.

We propose a novel learnable Anchored Unsigned Distance Function (AnchorUDF) representation for 3D garment reconstruction from a single image.

A human parsing based texture transfer model via cross-view consistency learning which generates the texture of 3D human body from a single image.

We propose a robust LSTM-Autoencoders (RLA) model consisting of two LSTM components, which aims at occlusion-robust face encoding and recurrent occlusion removal respectively.

We propose a deep semantic ranking based method for learning hash functions that preserve multilevel semantic similarity between multi-label images.