Ziwei Wang

Ziwei Wang is a PhD candidate at the Intelligent Vision Group (IVG), Department of Automation, Tsinghua University, advised by Prof. Jiwen Lu. He received the BS degree from the Department of Physics, Tsinghua University, China, in 2018. His research interests include model compression, compact representation learning and visual reasoning. He has published over 10 scientific papers in the IEEE Transactions on Pattern Analysis and Machine Intelligence, CVPR, ICCV and ECCV. He serves as a regular reviewer member for the IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, Pattern Recognition Letters, CVPR, ICCV, NeurIPS, ICML, ICLR, WACV, ACCV, ICPR, ICME and ICIP.

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We propose the unsupervised binary descriptor learning method via dynamic bitwise interaction mining (D-GraphBit), where a graph convolutional network called GraphMiner reasons the optimal bitwise interaction for each input sample.

We propose a Shapley value based method to evaluate operation contribution (Shapley-NAS) for neural architecture search.

We propose a generalizable mixed-precision quantization (GMPQ) method for efficient inference.

We propose an instance similarity learning (ISL) method for unsupervised feature representation.

We propose apresent binary neural networks with automatic information compression (AutoBiDet) to automatically adjust the IB trade-off according to the input complexity.

We present a hierarchical channel-wise interaction based binary convolutional neural networks (HCI-BCNN) method to minimize the quantiztaion error for activations via hierarchical reinforcement learning.

We propose a Deep Hashing method with Active Pairwise Supervision (DH-APS) to learn effective binary codes for image search with limited annotation budget.

We propose an efficient binarized object detector that fully utilizes the representational capacity of the binary neural networks by redundancy removal.

We propose a channel-wise interaction based binary convolutional neural network learning method (CI-BCNN) for efficient inference with minimal information loss.

We present a deep multi-quantization network to learn a data-dependent binarization for unsupervised features.

We propose a GraphBit method to learn deep binary descriptors with enhanced reliability in a directed acyclic graph unsupervisedly.

We present an unsupervised feature learning method called deep binary descriptor with multiquantization (DBD-MQ) for visual matching.

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