Open Access
| Issue |
ITM Web Conf.
Volume 47, 2022
2022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|
|
|---|---|---|
| Article Number | 02003 | |
| Number of page(s) | 10 | |
| Section | Algorithm Optimization and Application | |
| DOI | https://doi.org/10.1051/itmconf/20224702003 | |
| Published online | 23 June 2022 | |
- Turk M A, Pentland A P. Recognition in face space[C]//Intelligent Robots and Computer Vision IX: Algorithms and Techniques. International Sociey for Optics and Photonics, 1991, 1381: 43-54. [Google Scholar]
- Gkioxari G, Hariharan B, Girshick R, et al. R-CNNs for Pose Estimation and Action Detection[J]. Computer ence, 2014. [Google Scholar]
- Girshick R. Fast R-CNN[J]. arXiv e-prints, 2015. [Google Scholar]
- Ren S, He K, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. 2017. [Google Scholar]
- Purkait P, Zhao C, Zach C. SPP-Net: Deep Absolute Pose Regression with Synthetic Views[C]// British Machine Vision Conference(BMVC 2018). 2017. [Google Scholar]
- Redmon J, Divvala S, Girshick R, et al. You Only Look Once: Unified, Real-Time Object Detection[J]. IEEE, 2016. [Google Scholar]
- Redmon J, Farhadi A. YOLO9000: Better, Faster, Stronger[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2017:6517-6525. [Google Scholar]
- Redmon J, Farhadi A. YOLOv3: An Incremental Improvement[J]. arXiv e-prints, 2018. [Google Scholar]
- Bochkovskiy A, Wang C Y, Liao H. YOLOv4: Optimal Speed and Accuracy of Object Detection[J]. 2020. [Google Scholar]
- Ge Z, Liu S, Wang F, et al. Yolox: Exceeding yolo series in 2021[J]. arXiv preprint arXiv:2107.08430, 2021. [Google Scholar]
- Wang X, Xie L, Dong C, et al. Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data[J]. 2021. [Google Scholar]
- Dong, Image Super-Resolution Using Deep Convolutional Networks. 2016. [Google Scholar]
- J Kim, Shin M J, D Kim, et al. Performance Comparison of SRCNN, VDSR, and SRDenseNet Deep Learning Models in Embedded Autonomous Driving Platforms[C]// 2021 International Conference on Information Networking (ICOIN). 2021. [Google Scholar]
- Radford A, Metz L, Chintala S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[J]. Computer ence, 2015. [Google Scholar]
- Kim J, Lee J K, Lee K M. Accurate Image Super-Resolution Using Very Deep Convolutional Networks[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2016. [Google Scholar]
- Jie H, Li S, Gang S. Squeeze-and-Excitation Networks [J]. IEEE, 2018. [Google Scholar]
- Wang Q, Wu B, Zhu P, et al. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).IEEE, 2020. [Google Scholar]
- Liu Y, Wang Y, Wang S, et al. CBNet: A Novel Composite Backbone Network Architecture for Object Detection[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7):11653-11660. [CrossRef] [Google Scholar]
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