Jihao Liu

I am a PhD student in Multimedia Lab (MMLab), department of Electronic Engineering at the Chinese University of Hong Kong, supervised by Prof. Hongsheng Li.

Email / Google Scholar / Twitter / Github

Research

My recent research focuses on large-scale vision-language pre-training and its application. I am also interested in building general-purpose multi-modal models that follow human instructions.

	GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D Understanding Jihao Liu, Tai Wang, Boxiao Liu, Qihang Zhang, Yu Liu, Hongsheng Li ICCV, 2023 arxiv / code We propose Geometry Enhanced Masked Image Modeling (GeoMIM) to transfer the knowledge of the LiDAR model in a pretrain-finetune paradigm for improving the multi-view camera-based 3D detection.
	MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers Jihao Liu, Xin Huang, Jinliang Zheng, Yu Liu, Hongsheng Li CVPR, 2023 arxiv / code We propose MixMAE for efficient pretraining of hierarchical vision transformers.
	TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers Jihao Liu, Boxiao Liu, Hang Zhou, Hongsheng Li, Yu Liu ECCV, 2022 arxiv / code A token-level augmentation technique that can be well applied to training various transformer-based architectures.
	UniNet: Unified Architecture Search with Convolution, Transformer, and MLP Jihao Liu, Xin Huang, Guanglu Song, Hongsheng Li, Yu Liu ECCV, 2022 arxiv / code A high-performance hybrid visual architectures through unified architecture search.
	Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images Hang Zhou, Jihao Liu, Ziwei Liu, Yu Liu, Xiaogang Wang CVPR, 2020 arxiv / code Self-supervised approach for face rotation in the wild.
	Learning Where to Focus for Efficient Video Object Detection Zhengkai Jiang, Yu Liu, Ceyuan Yang, Jihao Liu, Peng Gao, Qian Zhang, Shiming Xiang, Chunhong Pan ECCV, 2020 arxiv / code We propose LSTS module to learn semantic-level correspondences among adjacent frame features accurately.
	Differentiable Kernel Evolution Yu Liu, Jihao Liu, Ailing Zeng, Xiaogang Wang ICCV, 2019 PDF We proposes a differentiable kernel evolution (DKE) algorithm which can find a better layer-operator for the convolutional neural network.
	Meta Knowledge Distillation Jihao Liu, Jinliang Zheng, Boxiao Liu, Hongsheng Li, Yu Liu arxiv, 2022 arxiv We propose Meta Knowledge Distillation (MKD) to meta-learn the distillation with learnable meta temperature parameters.
	FNAS: Uncertainty-Aware Fast Neural Architecture Search Jihao Liu, Ming Zhang, Yangting Sun, Boxiao Liu, Guanglu Song, Yu Liu, Hongsheng Li arxiv, 2021 arxiv We propose a general pipeline to accelerate the convergence of the rollout process as well as the RL process in NAS.