Research
My area of interest lies in the techniques of network binarization and quantization, along with knowledge distillation.
My research objective is to facilitate the deployment of advanced neural network models on hardware with limited resources.
This involves compressing various neural architectures and ensuring their adaptable deployment on diverse hardware platforms. My research focus is mainly on:
Network binarization and quantization
Knowledge distillation
Image synthesizing
Object detection
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Learning Accurate Low-bit Quantization towards Efficient Computational Imaging
Sheng Xu*, Yanjing Li*, Chuanjian Liu, Baochang Zhang
International Journal of Computer Vision (IJCV), 2024
[Paper coming] /
[arXiv coming] /
[code coming] (* Equal Contribution)
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Learning 1-Bit Tiny Object Detector with Discriminative Feature Refinement
Sheng Xu*, Mingze Wang*, Yanjing Li*, Mingbao Lin, Baochang Zhang, David Doermann, Xiao Sun
International Conference on Machine Learning (ICML), 2024
[Paper coming] /
[arXiv coming] /
[code coming] (* Equal Contribution)
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Bi-ViT: Pushing the Limit of Vision Transformer Quantization
Yanjing Li*, Sheng Xu*, Mingbao Lin, Xianbin Cao, Chuanjian Liu, Xiao Sun, Baochang Zhang
AAAI Conference on Artificial Intelligence (AAAI), 2024
[Paper coming] /
[arXiv] /
[code] (* Equal Contribution)
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Q-DM: An Efficient Low-bit Quantized Diffusion Model
Yanjing Li*, Sheng Xu*, Xianbin Cao, Xiao Sun, Baochang Zhang
Conference on Neural Information Processing Systems (NeurIPS), 2023
[Paper] /
[arXiv coming] /
[code coming] (* Equal Contribution)
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Representation Disparity-aware Distillation for 3D Object Detection
Yanjing Li*, Sheng Xu*, Mingbao Lin, Jihao Yin, Baochang Zhang, Xianbin Cao
International Conference on Computer Vision (ICCV), 2023
[Paper] /
[arXiv] /
[code] (* Equal Contribution)
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DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs
Yanjing Li*, Sheng Xu*, Xianbin Cao, Li'an Zhuo, Baochang Zhang, Tian Wang, Guodong Guo
International Journal of Computer Vision (IJCV), 2023
[Paper] /
[arXiv] /
[code coming] (* Equal Contribution)
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Q-DETR: An Efficient Low-Bit Quantized Detection Transformer
Sheng Xu*, Yanjing Li*, Mingbao Lin, Peng Gao, Guodong Guo, Jinhu Lu, Baochang Zhang
Computer Vision and Pattern Recognition (CVPR), 2023
Highlight presentation
[Paper] /
[arXiv] /
[code] (* Equal Contribution)
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Implicit Diffusion Models for Continuous Super-Resolution
Sicheng Gao, Xuhui Liu, Bohan Zeng, Sheng Xu, Yanjing Li, Xiaoyan Luo, Jianzhuang Liu, Xiantong Zhen, Baochang Zhang
Computer Vision and Pattern Recognition (CVPR), 2023
[Paper] /
[arXiv] /
[code]
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Resilient Binary Neural Network
Sheng Xu*, Yanjing Li*, Teli Ma*, Mingbao Lin, Hao Dong, Baochang Zhang, Peng Gao, Jinhu Lu
AAAI Conference on Artificial Intelligence (AAAI), 2023
Oral presentation
[Paper coming] /
[arXiv] /
[code] (* Equal Contribution)
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Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer
Yanjing Li*, Sheng Xu*, Baochang Zhang, Xianbin Cao, Peng Gao, Guodong Guo
Conference on Neural Information Processing Systems (NeurIPS), 2022
[Paper] /
[arXiv] /
[code] (* Equal Contribution)
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Recurrent Bilinear Optimization for Binary Neural Networks
Sheng Xu*, Yanjing Li*, Tiancheng Wang, Teli Ma, Baochang Zhang, Peng Gao, Yu Qiao, Jinhu Lv, Guodong Guo
European Conference on Computer Vision (ECCV), 2022
Oral presentation
[Paper] /
[arXiv] /
[code] (* Equal Contribution)
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IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors
Sheng Xu*, Yanjing Li*, Bohan Zeng*, Baochang Zhang, Xianbin Cao, Peng Gao, Jinhu Lv
European Conference on Computer Vision (ECCV), 2022
[Paper] /
[arXiv] /
[code] (* Equal Contribution)
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Academic Services
Program Committee of Conferences: CVPR 2022/2023, ECCV 2022, ICCV 2023, NeurIPS 2023, ICLR 2024, etc.
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