Xiaoqian Shen 沈晓倩
Bio: I am currently a Master student of Computer Science at King Abdullah University of Science and Technology supervised by Mohamed Elhoseiny. Before that, I received BSc in Computer Science from Jilin University, China.
Research: I am excited about complex problems that can be tackled with learning-based systems. Currently, my research focuses on generative models, spatiotemporal representation and low-resource learning.
Feel free to reach out to me: xiaoqian.shen@kaust.edu.sa
Publications
MoStGAN: Video Generation with Temporal Motion Styles
Xiaoqian Shen, Xiang Li, Mohamed Elhoseiny
CVPR 2023
Project Page Paper Supplementary Code
Xiaoqian Shen, Xiang Li, Mohamed Elhoseiny
CVPR 2023
Project Page Paper Supplementary Code
Exploring hierarchical graph representation for large-scale zero-shot image classification
Kai Yi, Xiaoqian Shen, Yunhao Gou, Mohamed Elhoseiny
ECCV 2022
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Kai Yi, Xiaoqian Shen, Yunhao Gou, Mohamed Elhoseiny
ECCV 2022
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Adversarial Text to Continuous Image Generation
Kilichbek Haydarov, Aashiq Muhamed, Jovana Lazarevic, Ivan Skorokhodov, Xiaoqian Shen, Chamuditha Galappaththige, Mohamed Elhoseiny
OpenReview 2022
Project Page Paper Code
Kilichbek Haydarov, Aashiq Muhamed, Jovana Lazarevic, Ivan Skorokhodov, Xiaoqian Shen, Chamuditha Galappaththige, Mohamed Elhoseiny
OpenReview 2022
Project Page Paper Code
KeMRE: knowledge-enhanced medical relation extraction for Chinese medicine instructions
Tao Qi, Shan Qiu, Xiaoqian Shen, Haopu Chen, Shuai Yang, Hao Wen, Ya Zhang, Yuanqing Wu, Yongfeng Huang
Journal of Biomedical Informatics 2021
Paper
Tao Qi, Shan Qiu, Xiaoqian Shen, Haopu Chen, Shuai Yang, Hao Wen, Ya Zhang, Yuanqing Wu, Yongfeng Huang
Journal of Biomedical Informatics 2021
Paper
Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network
Ye Zhu, Xiaoqian Shen, Shikun Liu, Xiaoli Zhang, Gang Yan
Applied Sciences 2021
Paper
Ye Zhu, Xiaoqian Shen, Shikun Liu, Xiaoli Zhang, Gang Yan
Applied Sciences 2021
Paper
Projects
MoStGAN: Video Generation with Temporal Motion Style
Mohamed Elhoseiny, Vision-CAIR KAUST, Jedda Saudi Arabia, 2023
We argue that a single time-agnostic latent vector of style-based generator is insufficient to model various motions and hence introduce additional time-dependent motion styles to model diverse motion patterns. In addition, a Motion Style Attention modulation mechanism is proposed to augment frames with vivid dynamics.
Mohamed Elhoseiny, Vision-CAIR KAUST, Jedda Saudi Arabia, 2023
We argue that a single time-agnostic latent vector of style-based generator is insufficient to model various motions and hence introduce additional time-dependent motion styles to model diverse motion patterns. In addition, a Motion Style Attention modulation mechanism is proposed to augment frames with vivid dynamics.
Affective Visual Dialog
Mohamed Elhoseiny, Vision-CAIR KAUST, Jedda Saudi Arabia, 2022
We study the role of affective language in the form of conversation grounded on visual stimuli in informing human emotion and collect a new dataset for (1) Dialog-based Question Answering; (2) Dialog-based Emotion classification and Affective explanation.
Mohamed Elhoseiny, Vision-CAIR KAUST, Jedda Saudi Arabia, 2022
We study the role of affective language in the form of conversation grounded on visual stimuli in informing human emotion and collect a new dataset for (1) Dialog-based Question Answering; (2) Dialog-based Emotion classification and Affective explanation.
Adversarial Text to Continuous Image Generation
Mohamed Elhoseiny, Vision-CAIR KAUST, Jedda Saudi Arabia, 2022
We introduce HyperCGAN, a conceptually simple approach for Adversarial Text to Continuous Image Generation that utilizes HyperNetworks to condition an Implicit Neural Representations (INR)-based GAN model on text.
Mohamed Elhoseiny, Vision-CAIR KAUST, Jedda Saudi Arabia, 2022
We introduce HyperCGAN, a conceptually simple approach for Adversarial Text to Continuous Image Generation that utilizes HyperNetworks to condition an Implicit Neural Representations (INR)-based GAN model on text.
Knowledge-enhanced Medical Relation Extraction
Yongfeng Huang, Tsinghua University, Beijing China, 2021
We propose a knowledge-enhanced framework for medical relation extraction (RE), which can exploit medical knowledge of medicines to better conduct medical RE on Chinese medicine instructions.
Yongfeng Huang, Tsinghua University, Beijing China, 2021
We propose a knowledge-enhanced framework for medical relation extraction (RE), which can exploit medical knowledge of medicines to better conduct medical RE on Chinese medicine instructions.
Self-Supervised Medical Image Segmentation
Zhenghua Xu, Hebei University of Technology and University of Oxford, Tianjin China, 2021
We proposes a multimodality contrastive self-supervised medical image segmentation method that utilizes a novel domain sharing generative adversarial network to achieve a contrastive domain translation.
Zhenghua Xu, Hebei University of Technology and University of Oxford, Tianjin China, 2021
We proposes a multimodality contrastive self-supervised medical image segmentation method that utilizes a novel domain sharing generative adversarial network to achieve a contrastive domain translation.