@article{qiao2025unified,author={Qiao, Yajun and Miao, Lingjuan and Zhou, Zhiqiang and Ming, Qi and Wang, Yuhao},journal={IEEE Transactions on Geoscience and Remote Sensing},title={Unified Five-distance Bounding Box Representation for Remote Sensing Oriented Object Detection},year={2025},volume={},number={},pages={1-1},publisher={IEEE},}
2024
Gradient Calibration Loss for Fast and Accurate Oriented Bounding Box Regression
Qi Ming, Lingjuan Miao, Zhiqiang Zhou, Junjie Song, and Aleksandra Pizurica
IEEE Transactions on Geoscience and Remote Sensing, vol.62, no., pp.1-15, 2024
@article{ming2024gradient,author={Ming, Qi and Miao, Lingjuan and Zhou, Zhiqiang and Song, Junjie and Pizurica, Aleksandra},journal={IEEE Transactions on Geoscience and Remote Sensing},title={Gradient Calibration Loss for Fast and Accurate Oriented Bounding Box Regression},year={2024},volume={62},number={},pages={1-15},publisher={IEEE},keywords={Object detection;Convergence;Remote sensing;Detectors;Training;Feature extraction;Proposals;Convolutional neural network;gradient analysis;loss function;oriented object detection},}
Not All Boxes Are Equal: Learning to Optimize Bounding Boxes With Discriminative Distributions in Optical Remote Sensing Images
Qi Ming, Lingjuan Miao, Zhiqiang Zhou, Nicolas Vercheval, and Aleksandra Pižurica
IEEE Transactions on Geoscience and Remote Sensing, vol.62, no., pp.1-14, 2024
@article{ming2024not,author={Ming, Qi and Miao, Lingjuan and Zhou, Zhiqiang and Vercheval, Nicolas and Pižurica, Aleksandra},journal={IEEE Transactions on Geoscience and Remote Sensing},title={Not All Boxes Are Equal: Learning to Optimize Bounding Boxes With Discriminative Distributions in Optical Remote Sensing Images},year={2024},volume={62},number={},pages={1-14},publisher={IEEE},keywords={Detectors;Remote sensing;Feature extraction;Object detection;Training;Optimization;Location awareness;Anchor refinement;bounding box regression;convolutional neural networks (CNNs);feature alignment;object detection},}
2023
Towards accurate medical image segmentation with gradient-optimized dice loss
Qi Ming, and Xiaowu Xiao
IEEE Signal Processing Letters, vol.31, pp.191–195, 2023
@article{ming2023towards,title={Towards accurate medical image segmentation with gradient-optimized dice loss},author={Ming, Qi and Xiao, Xiaowu},journal={IEEE Signal Processing Letters},volume={31},pages={191--195},year={2023},publisher={IEEE},}
Task Interleaving and Orientation Estimation for High-precision Oriented Object Detection in Aerial Images
Qi Ming, Lingjuan Miao, Zhiqiang Zhou, Junjie Song, Yunpeng Dong, and 1 more author
ISPRS Journal of Photogrammetry and Remote Sensing, vol.196, pp.241-255, 2023
@article{ming2023task,title={Task Interleaving and Orientation Estimation for High-precision Oriented Object Detection in Aerial Images},author={Ming, Qi and Miao, Lingjuan and Zhou, Zhiqiang and Song, Junjie and Dong, Yunpeng and Yang, Xue},journal={ISPRS Journal of Photogrammetry and Remote Sensing},volume={196},pages={241-255},year={2023},publisher={Elsevier},issn={0924-2716},}
Deep Dive into Gradients: Better Optimization for 3D Object Detection with Gradient-Corrected IoU Supervision
Qi Ming, Lingjuan Miao, Zhe Ma, Lin Zhao, Zhiqiang Zhou, and 3 more authors
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.5136–5145, 2023
@inproceedings{ming2023deep,title={Deep Dive into Gradients: Better Optimization for 3D Object Detection with Gradient-Corrected IoU Supervision},author={Ming, Qi and Miao, Lingjuan and Ma, Zhe and Zhao, Lin and Zhou, Zhiqiang and Huang, Xuhui and Chen, Yuanpei and Guo, Yufei},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},pages={5136--5145},year={2023},}
A novel object detector based on high-quality rotation proposal generation and adaptive angle optimization
Yajun Qiao, Lingjuan Miao, Zhiqiang Zhou, and Qi Ming
IEEE Transactions on Geoscience and Remote Sensing, vol.61, pp.1–15, 2023
@article{qiao2023novel,title={A novel object detector based on high-quality rotation proposal generation and adaptive angle optimization},author={Qiao, Yajun and Miao, Lingjuan and Zhou, Zhiqiang and Ming, Qi},journal={IEEE Transactions on Geoscience and Remote Sensing},volume={61},pages={1--15},year={2023},publisher={IEEE},}
Optimized Point Set Representation for Oriented Object Detection in Remote-Sensing Images
@article{song2023optimized,title={Optimized Point Set Representation for Oriented Object Detection in Remote-Sensing Images},author={Song, Junjie and Miao, Lingjuan and Zhou, Zhiqiang and Ming, Qi and Dong, Yunpeng},journal={IEEE Geoscience and Remote Sensing Letters},volume={20},pages={1--5},year={2023},publisher={IEEE},}
2022
Fine-grained object detection in remote sensing images via adaptive label assignment and refined-balanced feature pyramid network
@article{song2022fine,title={Fine-grained object detection in remote sensing images via adaptive label assignment and refined-balanced feature pyramid network},author={Song, Junjie and Miao, Lingjuan and Ming, Qi and Zhou, Zhiqiang and Dong, Yunpeng},journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},volume={16},pages={71--82},year={2022},publisher={IEEE},}
2021
CFC-Net: A critical feature capturing network for arbitrary-oriented object detection in remote-sensing images
Qi Ming, Lingjuan Miao, Zhiqiang Zhou, and Yunpeng Dong
IEEE Transactions on Geoscience and Remote Sensing, vol.60, pp.1–14, 2021
@article{ming2021cfc,title={CFC-Net: A critical feature capturing network for arbitrary-oriented object detection in remote-sensing images},author={Ming, Qi and Miao, Lingjuan and Zhou, Zhiqiang and Dong, Yunpeng},journal={IEEE Transactions on Geoscience and Remote Sensing},volume={60},pages={1--14},year={2021},publisher={IEEE},}
Dynamic anchor learning for arbitrary-oriented object detection
Qi Ming, Zhiqiang Zhou, Lingjuan Miao, Hongwei Zhang, and Linhao Li
In Proceedings of the AAAI conference on artificial intelligence, vol.35, no.3, pp.2355–2363, 2021
@inproceedings{ming2021dynamic,title={Dynamic anchor learning for arbitrary-oriented object detection},author={Ming, Qi and Zhou, Zhiqiang and Miao, Lingjuan and Zhang, Hongwei and Li, Linhao},booktitle={Proceedings of the AAAI conference on artificial intelligence},volume={35},number={3},pages={2355--2363},year={2021},}
Optimization for arbitrary-oriented object detection via representation invariance loss
@article{ming2021optimization,title={Optimization for arbitrary-oriented object detection via representation invariance loss},author={Ming, Qi and Miao, Lingjuan and Zhou, Zhiqiang and Yang, Xue and Dong, Yunpeng},journal={IEEE Geoscience and Remote Sensing Letters},volume={19},pages={1--5},year={2021},publisher={IEEE},}
Sparse label assignment for oriented object detection in aerial images
Qi Ming, Lingjuan Miao, Zhiqiang Zhou, Junjie Song, and Xue Yang
@article{ming2021sparse,title={Sparse label assignment for oriented object detection in aerial images},author={Ming, Qi and Miao, Lingjuan and Zhou, Zhiqiang and Song, Junjie and Yang, Xue},journal={Remote Sensing},volume={13},number={14},pages={2664},year={2021},publisher={MDPI},}
Learning high-precision bounding box for rotated object detection via kullback-leibler divergence
Xue Yang, Xiaojiang Yang, Jirui Yang, Qi Ming, Wentao Wang, and 2 more authors
Advances in Neural Information Processing Systems, vol.34, pp.18381–18394, 2021
@article{yang2021learning,title={Learning high-precision bounding box for rotated object detection via kullback-leibler divergence},author={Yang, Xue and Yang, Xiaojiang and Yang, Jirui and Ming, Qi and Wang, Wentao and Tian, Qi and Yan, Junchi},journal={Advances in Neural Information Processing Systems},volume={34},pages={18381--18394},year={2021},}
Rethinking rotated object detection with gaussian wasserstein distance loss
Xue Yang, Junchi Yan, Qi Ming, Wentao Wang, Xiaopeng Zhang, and 1 more author
In International conference on machine learning, pp.11830–11841, 2021
@inproceedings{yang2021rethinking,title={Rethinking rotated object detection with gaussian wasserstein distance loss},author={Yang, Xue and Yan, Junchi and Ming, Qi and Wang, Wentao and Zhang, Xiaopeng and Tian, Qi},booktitle={International conference on machine learning},pages={11830--11841},year={2021},organization={PMLR},}