Jing Liu

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I am a final-year Ph.D. student at Monash University, supervised by Asst. Prof. Bohan Zhuang, Prof. Jianfei Cai, and Prof. Chunhua Shen. I am a member of ZIP Lab. Prior to my Ph.D., I completed my master’s degree at South China University of Technology, under the supervision of Prof. Mingkui Tan.

My research focus on developing efficient AI. This involves optimizing AI algorithms for improving performance with reduced computational resources, aiming to make AI more accessible and sustainable for real-world applications. Some topics that I currently focus on:

🌟🌟 I am currently on the industry job market. 🌟🌟

News

Jan 17, 2024 Two papers are accepted by ICLR 2024!
Dec 29, 2023 One paper is accepted by TPAMI!
Sep 22, 2023 One paper is accepted by NeurIPS 2023!
Jul 13, 2023 One paper is accepted by ICCV 2023!
May 13, 2023 One paper is accepted by TPAMI!
Apr 20, 2023 One survey paper is accepted by IJCAI 2023!
Feb 28, 2023 One paper is accepted by CVPR 2023!
Nov 02, 2022 Ecoformer is selected as a spotlight paper!
Sep 15, 2022 One paper is accepted by NeruIPS 2022!

Publications

(* indicates equal contributions)
  1. QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models
    Jing Liu ,  Ruihao Gong ,  Xiuying Wei ,  Zhiwei Dong ,  Jianfei Cai ,  and  Bohan Zhuang
    In International Conference on Learning Representations (ICLR) , 2024
  2. ICLR Spotlight
    EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models
    Yefei He ,  Jing Liu ,  Weijia Wu ,  Hong Zhou ,  and  Bohan Zhuang
    In International Conference on Learning Representations (ICLR) , 2024
    Spotlight (top 5% of all submitted papers)
  3. Pruning self-attentions into convolutional layers in single path
    Haoyu He ,  Jing Liu ,  Zizheng Pan ,  Jianfei Cai ,  Jing Zhang ,  Dacheng Tao ,  and  Bohan Zhuang
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
  4. PTQD: Accurate Post-Training Quantization for Diffusion Models
    Yefei He ,  Luping Liu ,  Jing Liu ,  Weijia Wu ,  Hong Zhou ,  and  Bohan Zhuang
    In Conference on Neural Information Processing Systems (NeurIPS) , 2023
  5. BiViT: Extremely Compressed Binary Vision Transformers
    Yefei He ,  Zhenyu Lou ,  Luoming Zhang ,  Jing Liu ,  Weijia Wu ,  Hong Zhou ,  and  Bohan Zhuang
    In International Conference on Computer Vision (ICCV) , 2023
  6. Single-path bit sharing for automatic loss-aware model compression
    Jing Liu ,  Bohan Zhuang ,  Peng Chen ,  Chunhua Shen ,  Jianfei Cai ,  and  Mingkui Tan
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
  7. A Survey on Efficient Training of Transformers
    Bohan Zhuang ,  Jing Liu ,  Zizheng Pan ,  Haoyu He ,  Yuetian Weng ,  and  Chunhua Shen
    In International Joint Conference on Artificial Intelligence (IJCAI) , 2023
    Survey Track
  8. Dynamic Focus-Aware Positional Queries for Semantic Segmentation
    Haoyu He ,  Jianfei Cai ,  Zizheng Pan ,  Jing Liu ,  Jing Zhang ,  Dacheng Tao ,  and  Bohan Zhuang
    In Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
  9. NeurIPS Spotlight
    EcoFormer: Energy-Saving Attention with Linear Complexity
    Jing Liu ,  Zizheng Pan ,  Haoyu He ,  Jianfei Cai ,  and  Bohan Zhuang
    In Conference on Neural Information Processing Systems (NeurIPS) , 2022
    Spotlight (top 5% of all submitted papers)
  10. Less is more: Pay less attention in vision transformers
    Zizheng Pan ,  Bohan Zhuang ,  Haoyu He ,  Jing Liu ,  and  Jianfei Cai
    In AAAI Conference on Artificial Intelligence (AAAI) , 2022
  11. Scalable Vision Transformers With Hierarchical Pooling
    Zizheng Pan ,  Bohan Zhuang ,  Jing Liu ,  Haoyu He ,  and  Jianfei Cai
    In International Conference on Computer Vision (ICCV) , 2021
  12. Discrimination-aware network pruning for deep model compression
    Jing Liu ,  Bohan Zhuang ,  Zhuangwei Zhuang ,  Yong Guo ,  Junzhou Huang ,  Jinhui Zhu ,  and  Mingkui Tan
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
  13. Effective training of convolutional neural networks with low-bitwidth weights and activations
    Bohan Zhuang* ,  Mingkui Tan* ,  Jing Liu* ,  Lingqiao Liu ,  Ian Reid ,  and  Chunhua Shen
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
  14. CVPR Oral
    AQD: Towards Accurate Quantized Object Detection
    Peng Chen* ,  Jing Liu* ,  Bohan Zhuang ,  Mingkui Tan ,  and  Chunhua Shen
    In Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
    Oral Presentation (top 4% of all submitted papers)
  15. Deep transferring quantization
    Zheng Xie* ,  Zhiquan Wen* ,  Jing Liu* ,  Zhiqiang Liu ,  Xixian Wu ,  and  Mingkui Tan
    In European Conference on Computer Vision (ECCV) , 2020
  16. Generative low-bitwidth data free quantization
    Shoukai Xu* ,  Haokun Li* ,  Bohan Zhuang* ,  Jing Liu ,  Jiezhang Cao ,  Chuangrun Liang ,  and  Mingkui Tan
    In European Conference on Computer Vision (ECCV) , 2020
  17. Discrimination-aware Channel Pruning for Deep Neural Networks
    Zhuangwei Zhuang* ,  Mingkui Tan* ,  Bohan Zhuang* ,  Jing Liu* ,  Yong Guo ,  Qingyao Wu ,  Junzhou Huang ,  and  Jinhui Zhu
    In Conference on Neural Information Processing Systems (NeurIPS) , 2018