HKU-IDS Scholar
Department of Electrical and Electronic Engineering, HKU
P307D, Graduate House, HKU
Department of Electrical and Electronic Engineering
Dr. Xihui Liu is an Assistant Professor at the Department of Electrical and Electronic Engineering (EEE) and the Musketeers Foundation Institute of Data Science (IDS), The University of Hong Kong. Before joining HKU, she was a Postdoctoral Researcher at UC Berkeley working with Prof. Trevor Darrell. She received her Ph.D. degree from Multimedia Lab, The Chinese University of Hong Kong in 2021 and her Bachelor’s degree from Tsinghua University in 2017. She has won several awards such as Adobe Research Fellowship 2020, MIT EECS Rising Stars 2021, CVPR 2021 Doctoral Consortium Award, WAIC Rising Star Award 2022, CVPR Outstanding Reviewers Award, and ICLR Outstanding Reviewers Award.
More information about Dr. Liu can be found at her personal website https://xh-liu.github.io/ and Google Scholar page https://scholar.google.com.hk/citations?user=4YL23GMAAAAJ&hl=en
Dr Liu’s research projects cover computer vision, machine learning, and artificial intelligence, with special emphasis on multimodal AI, vision and language, open-world recognition, visual synthesis, and generative models. Her research goal is to build artificial intelligence that can perceive, understand, create, and interact with the multi-modal world.
The objective of Dr Liu’s current project is to study a challenging research problem on compositional text-to-image synthesis with diffusion models. With the recent progress in text-to-image synthesis, generative models such as GANs and diffusion models have been proven effective in synthesizing photo-realistic images with text instructions. However, synthesizing images with novel compositions of concepts remains a challenging problem. This project aims to explore generalizable diffusion model architectures that can synthesize images with novel compositions of concepts and new methods of synthesizing images with long, descriptive text instructions with diffusion models.
- Xihui Liu, Dong Huk Park, Samaneh Azadi, Gong Zhang, Arman Chopikyan, Yuxiao Hu, Humphrey Shi, Anna Rohrbach, and Trevor Darrell. “More Control for Free! Image Synthesis with Semantic Diffusion Guidance.” Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 289-299, (2023).
- Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, and Hengshuang Zhao, “Point Transformer V2: Grouped Vector Attention and Partition-based Pooling.” Proceedings of the Conference on Neural Information Processing Systems (NeurIPS),
- Yuying Ge, Yixiao Ge, Xihui Liu, Dian Li, Ying Shan, Xiaohu Qie, Ping Luo. “BridgeFormer: Bridging Video-text Retrieval with Multiple Choice Questions.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: https://doi.org/10.48550/arXiv.2210.05666, (2022).
- Dong Huk Park*, Samaneh Azadi*, Xihui Liu, Trevor Darrell, Anna Rohrbach. “Benchmark for Compositional Text-to-Image Synthesis.” Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) dataset and benchmark track, (2021).
- Xihui Liu, Zhe Lin, Jianming Zhang, Handong Zhao, Quan Tran, Xiaogang Wang, and Hongsheng Li. “Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructions.” Proceedings of the European Conference on Computer Vision (ECCV), (2020).
- Xihui Liu, Guojun Yin, Jing Shao, Xiaogang Wang, and Hongsheng Li. “Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis”, Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), (2019).
- Xihui Liu, Zihao Wang, Hongsheng Li, Jing Shao, and Xiaogang Wang. “Improving Referring Expression Grounding with Cross-modal Attention-guided Erasing.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2019).
- Zihao Wang*, Xihui Liu*, Hongsheng Li, Lu Sheng, Junjie Yan, Xiaogang Wang, and Jing Shao. “CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval.” Proceedings of the IEEE International Conference on Computer Vision (ICCV), (2019).
- Xihui Liu, Hongsheng Li, Jing Shao, Dapeng Chen, and Xiaogang Wang. “Show, Tell and Discriminate: Image Captioning by Self-retrieval with Partially Labeled Data.” Proceedings of the European Conference on Computer Vision (ECCV), (2018).
- Xihui Liu*, Haiyu Zhao*, Maoqing Tian, Lu Sheng, Jing Shao, Shuai Yi, Junjie Yan, and Xiaogang Wang. “Hydraplus-Net: Attentive Deep Features for Pedestrian Analysis.” Proceedings of the IEEE International Conference on Computer Vision (ICCV), (2017).
2021 MIT EECS Rising Stars
2019 Adobe Research Fellowship
ICLR 2021 Outstanding Reviewer Award
CVPR 2019 Outstanding Reviewer Award
Computer Vision; Machine Learning; Artificial Intelligence
