@inproceedings{yuefficientcaper,title={EfficientCAPER: An End-to-End Framework for Fast and Robust Category-Level Articulated Object Pose Estimation},author={Yu, Xinyi and Jiang, Haonan and Zhang, Li and Wu, Lin Yuanbo and Ou, Linlin and Liu, Liu},booktitle={Conference and Workshop on Neural Information Processing Systems},year={2025},}
Rethinking 3D Convolution in lp $-norm Space
Li Zhang, Yan Zhong, Jianan Wang, and 3 more authors
In Conference and Workshop on Neural Information Processing Systems, 2025
@inproceedings{zhangrethinking,title={Rethinking 3D Convolution in lp $-norm Space},author={Zhang, Li and Zhong, Yan and Wang, Jianan and Min, Zhe and Liu, Liu and others},booktitle={Conference and Workshop on Neural Information Processing Systems},year={2025},}
U-COPE: Taking a Further Step to Universal 9D Category-Level Object Pose Estimation
Li Zhang, Weiqing Meng, Yan Zhong, and 6 more authors
@inproceedings{zhang2025u,title={U-COPE: Taking a Further Step to Universal 9D Category-Level Object Pose Estimation},author={Zhang, Li and Meng, Weiqing and Zhong, Yan and Kong, Bin and Xu, Mingliang and Du, Jianming and Wang, Xue and Wang, Rujing and Liu, Liu},booktitle={European Conference on Computer Vision},pages={254--270},year={2025},organization={Springer},doi={10.1007/978-3-031-72684-2_15},}
2024
Gamma: Generalizable articulation modeling and manipulation for articulated objects
Qiaojun Yu, Junbo Wang, Wenhai Liu, and 5 more authors
In IEEE International Conference on Robotics and Automation, 2024
@inproceedings{yu2024gamma,title={Gamma: Generalizable articulation modeling and manipulation for articulated objects},author={Yu, Qiaojun and Wang, Junbo and Liu, Wenhai and Hao, Ce and Liu, Liu and Shao, Lin and Wang, Weiming and Lu, Cewu},booktitle={IEEE International Conference on Robotics and Automation},pages={5419--5426},year={2024},organization={IEEE},doi={10.1109/ICRA57147.2024.10610652},}
KPA-Tracker: Towards Robust and Real-Time Category-Level Articulated Object 6D Pose Tracking
Liu Liu, Anran Huang, Qi Wu, and 3 more authors
In the AAAI Conference on Artificial Intelligence, 2024
@inproceedings{liu2024kpa,title={KPA-Tracker: Towards Robust and Real-Time Category-Level Articulated Object 6D Pose Tracking},author={Liu, Liu and Huang, Anran and Wu, Qi and Guo, Dan and Yang, Xun and Wang, Meng},booktitle={the AAAI Conference on Artificial Intelligence},volume={38},number={4},pages={3684--3692},year={2024},doi={10.1609/aaai.v38i4.28158},}
RPMArt: Towards Robust Perception and Manipulation for Articulated Objects
Junbo Wang, Wenhai Liu, Qiaojun Yu, and 4 more authors
In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024
@inproceedings{wang2024rpmart,author={Wang, Junbo and Liu, Wenhai and Yu, Qiaojun and You, Yang and Liu, Liu and Wang, Weiming and Lu, Cewu},booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems},title={RPMArt: Towards Robust Perception and Manipulation for Articulated Objects},year={2024},pages={7270-7277},doi={10.1109/IROS58592.2024.10802368},}
Thermal-NeRF: Neural Radiance Fields from an Infrared Camera
Tianxiang Ye, Qi Wu, Junyuan Deng, and 6 more authors
In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024
@inproceedings{ye2024thermal,author={Ye, Tianxiang and Wu, Qi and Deng, Junyuan and Liu, Guoqing and Liu, Liu and Xia, Songpengcheng and Pang, Liang and Yu, Wenxian and Pei, Ling},booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems},title={Thermal-NeRF: Neural Radiance Fields from an Infrared Camera},year={2024},pages={1046-1053},doi={10.1109/IROS58592.2024.10802480},}
ICAF-4: An Integrated Framework of Category-level Articulated Object Perception and Manipulation for Embodied Intelligence
WenBo Xu, Li Zhang, Qiankun Li, and 3 more authors
@inproceedings{Xu_2024_BMVC,author={Xu, WenBo and Zhang, Li and Li, Qiankun and Wu, Qi and Wu, Lin Yuanbo and Liu, Liu},title={ICAF-4: An Integrated Framework of Category-level Articulated Object Perception and Manipulation for Embodied Intelligence},booktitle={British Machine Vision Conference},publisher={BMVA},year={2024},}
High-throughput spike detection and refined segmentation for wheat Fusarium Head Blight in complex field environments
Qiong Zhou, Ziliang Huang, Liu Liu, and 5 more authors
@inproceedings{liu2023category,title={Category-level articulated object 9d pose estimation via reinforcement learning},author={Liu, Liu and Du, Jianming and Wu, Hao and Yang, Xun and Liu, Zhenguang and Hong, Richang and Wang, Meng},booktitle={Proceedings of the 31st ACM International Conference on Multimedia},pages={728--736},year={2023},doi={10.1145/3581783.3611852},}
Reaper: Articulated Object 6d Pose Estimation with Deep Reinforcement Learning
Liu Liu, Qi Wu, Zhendong Xue, and 2 more authors
In IEEE International Conference on Image Processing, 2023
@inproceedings{liu2023reaper,title={Reaper: Articulated Object 6d Pose Estimation with Deep Reinforcement Learning},author={Liu, Liu and Wu, Qi and Xue, Zhendong and Qian, Sucheng and Li, Rui},booktitle={IEEE International Conference on Image Processing},pages={21--25},year={2023},organization={IEEE},doi={10.1109/ICIP49359.2023.10223108},}
Cellular Network Traffic Prediction Based on Correlation ConvLSTM and Self-Attention Network
Xuesen Ma, Biao Zheng, Gonghui Jiang, and 1 more author
@article{ma2023cellular,title={Cellular Network Traffic Prediction Based on Correlation ConvLSTM and Self-Attention Network},author={Ma, Xuesen and Zheng, Biao and Jiang, Gonghui and Liu, Liu},journal={IEEE Communications Letters},volume={27},number={7},pages={1909--1912},year={2023},publisher={IEEE},doi={10.1109/LCOMM.2023.3275327},}
2022
Akb-48: A real-world articulated object knowledge base
Liu Liu, Wenqiang Xu, Haoyuan Fu, and 4 more authors
In IEEE Conference on Computer Vision and Pattern Recognition, 2022
@inproceedings{liu2022akb,title={Akb-48: A real-world articulated object knowledge base},author={Liu, Liu and Xu, Wenqiang and Fu, Haoyuan and Qian, Sucheng and Yu, Qiaojun and Han, Yang and Lu, Cewu},booktitle={IEEE Conference on Computer Vision and Pattern Recognition},pages={14809--14818},year={2022},doi={10.1109/CVPR52688.2022.01439},}
Oakink: A large-scale knowledge repository for understanding hand-object interaction
Lixin Yang, Kailin Li, Xinyu Zhan, and 4 more authors
In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022
@inproceedings{yang2022oakink,title={Oakink: A large-scale knowledge repository for understanding hand-object interaction},author={Yang, Lixin and Li, Kailin and Zhan, Xinyu and Wu, Fei and Xu, Anran and Liu, Liu and Lu, Cewu},booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},pages={20953--20962},year={2022},doi={10.1109/CVPR52688.2022.02028},}
@article{liu2022toward,title={Toward real-world category-level articulation pose estimation},author={Liu, Liu and Xue, Han and Xu, Wenqiang and Fu, Haoyuan and Lu, Cewu},journal={IEEE Transactions on Image Processing},volume={31},pages={1072--1083},publisher={IEEE},year={2022},doi={10.1109/TIP.2021.3138644},}
When pansharpening meets graph convolution network and knowledge distillation
Keyu Yan, Man Zhou, Liu Liu, and 2 more authors
IEEE Transactions on Geoscience and Remote Sensing, 2022
@article{yan2022pansharpening,title={When pansharpening meets graph convolution network and knowledge distillation},author={Yan, Keyu and Zhou, Man and Liu, Liu and Xie, Chengjun and Hong, Danfeng},journal={IEEE Transactions on Geoscience and Remote Sensing},volume={60},pages={1--15},year={2022},publisher={IEEE},doi={10.1109/TGRS.2022.3168192},}
Towards densely clustered tiny pest detection in the wild environment
@article{du2022towards,title={Towards densely clustered tiny pest detection in the wild environment},author={Du, Jianming and Liu, Liu and Li, Rui and Jiao, Lin and Xie, Chengjun and Wang, Rujing},journal={Neurocomputing},volume={490},pages={400--412},year={2022},publisher={Elsevier},doi={10.1016/j.neucom.2021.12.012},}
A global activated feature pyramid network for tiny pest detection in the wild
Liu Liu, Rujing Wang, Chengjun Xie, and 3 more authors
@article{liu2022global,title={A global activated feature pyramid network for tiny pest detection in the wild},author={Liu, Liu and Wang, Rujing and Xie, Chengjun and Li, Rui and Wang, Fangyuan and Qi, Long},journal={Machine Vision and Applications},volume={33},number={5},pages={76},year={2022},publisher={Springer},doi={10.1007/s00138-022-01310-0},}
A multi-branch convolutional neural network with density map for aphid counting
Rui Li, Rujing Wang, Chengjun Xie, and 8 more authors
@article{li2022multi,title={A multi-branch convolutional neural network with density map for aphid counting},author={Li, Rui and Wang, Rujing and Xie, Chengjun and Chen, Hongbo and Long, Qi and Liu, Liu and Zhang, Jie and Chen, Tianjiao and Hu, Haiying and Jiao, Lin and others},journal={Biosystems Engineering},volume={213},pages={148--161},year={2022},publisher={Elsevier},doi={10.1016/j.biosystemseng.2021.11.020},}
Fast location and segmentation of high-throughput damaged soybean seeds with invertible neural networks
Ziliang Huang, Rujing Wang, Qiong Zhou, and 4 more authors
Journal of the Science of Food and Agriculture, 2022
@article{huang2022fast,title={Fast location and segmentation of high-throughput damaged soybean seeds with invertible neural networks},author={Huang, Ziliang and Wang, Rujing and Zhou, Qiong and Teng, Yue and Zheng, Shijian and Liu, Liu and Wang, Liusan},journal={Journal of the Science of Food and Agriculture},volume={102},number={11},pages={4854--4865},year={2022},publisher={Wiley Online Library},doi={10.1002/jsfa.11848},}
MSR-RCNN: a multi-class crop pest detection network based on a multi-scale super-resolution feature enhancement module
Yue Teng, Jie Zhang, Shifeng Dong, and 2 more authors
@article{teng2022msr,title={MSR-RCNN: a multi-class crop pest detection network based on a multi-scale super-resolution feature enhancement module},author={Teng, Yue and Zhang, Jie and Dong, Shifeng and Zheng, Shijian and Liu, Liu},journal={Frontiers in Plant Science},volume={13},pages={810546},year={2022},publisher={Frontiers Media SA},doi={10.3389/fpls.2022.810546},}
2021
OMAD: Object Model with Articulated Deformations for Pose Estimation and Retrieval
@article{xue2021omad,title={OMAD: Object Model with Articulated Deformations for Pose Estimation and Retrieval},author={Xue, Han and Liu, Liu and Xu, Wenqiang and Fu, Haoyuan and Lu, Cewu},year={2021},booktitle={British Machine Vision Conference},}
Learning region-guided scale-aware feature selection for object detection
Liu Liu, Rujing Wang, Chengjun Xie, and 4 more authors
@article{liu2021learning,title={Learning region-guided scale-aware feature selection for object detection},author={Liu, Liu and Wang, Rujing and Xie, Chengjun and Li, Rui and Wang, Fangyuan and Zhou, Man and Teng, Yue},journal={Neural Computing and Applications},volume={33},pages={6389--6403},year={2021},publisher={Springer},doi={10.1109/TII.2020.2995208},}
ReinforceNet: A reinforcement learning embedded object detection framework with region selection network
Man Zhou, Rujing Wang, Chengjun Xie, and 4 more authors
@article{zhou2021reinforcenet,title={ReinforceNet: A reinforcement learning embedded object detection framework with region selection network},author={Zhou, Man and Wang, Rujing and Xie, Chengjun and Liu, Liu and Li, Rui and Wang, Fangyuan and Li, Dengshan},journal={Neurocomputing},volume={443},pages={369--379},year={2021},publisher={Elsevier},doi={10.1016/j.neucom.2021.02.073},}
Reinforcedet: Object Detection By Integrating Reinforcement Learning With Decoupled Pipeline
Man Zhou, Liu Liu, and Rujing Wang
In IEEE International Conference on Image Processing, 2021
@inproceedings{zhou2021reinforcedet,title={Reinforcedet: Object Detection By Integrating Reinforcement Learning With Decoupled Pipeline},author={Zhou, Man and Liu, Liu and Wang, Rujing},booktitle={IEEE International Conference on Image Processing},pages={2778--2782},year={2021},organization={IEEE},doi={10.1109/ICIP42928.2021.9506038},}
Convolutional neural network based automatic pest monitoring system using hand-held mobile image analysis towards non-site-specific wild environment
Fangyuan Wang, Rujing Wang, Chengjun Xie, and 3 more authors
@article{wang2021convolutional,title={Convolutional neural network based automatic pest monitoring system using hand-held mobile image analysis towards non-site-specific wild environment},author={Wang, Fangyuan and Wang, Rujing and Xie, Chengjun and Zhang, Jie and Li, Rui and Liu, Liu},journal={Computers and Electronics in Agriculture},volume={187},pages={106268},year={2021},publisher={Elsevier},doi={10.1016/j.compag.2021.106268},}
2020
Deep learning based automatic multiclass wild pest monitoring approach using hybrid global and local activated features
Liu Liu, Chengjun Xie, Rujing Wang, and 5 more authors
@article{liu2020deep,title={Deep learning based automatic multiclass wild pest monitoring approach using hybrid global and local activated features},author={Liu, Liu and Xie, Chengjun and Wang, Rujing and Yang, Po and Sudirman, Sud and Zhang, Jie and Li, Rui and Wang, Fangyuan},journal={IEEE Transactions on Industrial Informatics},volume={17},number={11},pages={7589--7598},year={2020},publisher={IEEE},doi={10.1007/s00521-020-05400-w},}
Fpha-afford: A domain-specific benchmark dataset for occluded object affordance estimation in human-object-robot interaction
Liu Liu, Wenqiang Xu, Cewu Lu, and 1 more author
In IEEE International Conference on Image Processing, 2020
@inproceedings{liu2020fpha,title={Fpha-afford: A domain-specific benchmark dataset for occluded object affordance estimation in human-object-robot interaction},author={Liu, Liu and Xu, Wenqiang and Lu, Cewu and others},booktitle={IEEE International Conference on Image Processing},pages={1416--1420},year={2020},organization={IEEE},doi={10.1109/ICIP40778.2020.9190733},}
An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild
Yushan Zhao, Liu Liu, Chengjun Xie, and 4 more authors
@article{zhao2020effective,title={An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild},author={Zhao, Yushan and Liu, Liu and Xie, Chengjun and Wang, Rujing and Wang, Fangyuan and Bu, Yingqiao and Zhang, Shunxiang},journal={Applied Soft Computing},volume={89},pages={106128},year={2020},publisher={Elsevier},doi={10.1016/j.asoc.2020.106128},}
Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition
Fangyuan Wang, Rujing Wang, Chengjun Xie, and 2 more authors
@article{wang2020fusing,title={Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition},author={Wang, Fangyuan and Wang, Rujing and Xie, Chengjun and Yang, Po and Liu, Liu},journal={Computers and Electronics in Agriculture},volume={169},pages={105222},year={2020},publisher={Elsevier},doi={10.1016/j.compag.2020.105222},}
2019
Deep learning based automatic approach using hybrid global and local activated features towards large-scale multi-class pest monitoring
Liu Liu, Rujing Wang, Chengjun Xie, and 4 more authors
In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019
@inproceedings{liu2019deep,title={Deep learning based automatic approach using hybrid global and local activated features towards large-scale multi-class pest monitoring},author={Liu, Liu and Wang, Rujing and Xie, Chengjun and Yang, Po and Sudirman, S and Wang, F and Li, R},booktitle={2019 IEEE 17th International Conference on Industrial Informatics (INDIN)},volume={1},pages={1507--1510},year={2019},doi={10.1109/INDIN41052.2019.8972026},}
A coarse-to-fine network for aphid recognition and detection in the field
Rui Li, Rujing Wang, Chengjun Xie, and 4 more authors
@article{li2019coarse,title={A coarse-to-fine network for aphid recognition and detection in the field},author={Li, Rui and Wang, Rujing and Xie, Chengjun and Liu, Liu and Zhang, Jie and Wang, Fangyuan and Liu, Wancai},journal={Biosystems engineering},volume={187},pages={39--52},year={2019},publisher={Elsevier},doi={10.1016/j.biosystemseng.2019.08.013},}
