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Canadian Journal of Remote Sensing
Journal canadien de télédétection
Volume 50, 2024 - Issue 1
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Research Article

Active Reinforcement Learning for the Semantic Segmentation of Urban Images

Apprentissage par renforcement actif pour la segmentation sémantique des images urbaines

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Article: 2374788 | Received 09 Feb 2024, Accepted 27 Jun 2024, Published online: 30 Jul 2024

References

  • Bachman, P., Sordoni, A., and Trischler, A. 2017. “Learning Algorithms for Active Learning.” doi:10.48550/arXiv.1708.00088.
  • Casanova, A., Pinheiro, P. O., Rostamzadeh, N., and Pal, C. J. 2020. “Reinforcement Active Learning for Semantic Segmentation.” doi:10.48550/arXiv.2002.06583.
  • Chen, B., Gong, C., and Yang, J. 2017. “Importance-Aware Semantic Segmentation for Autonomous Driving System.” Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Melbourne, Australia, pp. 1504–1510. doi:10.24963/ijcai.2017/208.
  • Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. 2016. “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40(No. 4): pp. 834–848. doi:10.1109/TPAMI.2017.2699184.
  • Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. 2017. “Rethinking Atrous Convolution for Semantic Image Segmentation.” ArXiv170605587 Cs.
  • Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. 2018. “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.” ArXiv180202611 Cs.
  • Contardo, G., Denoyer, L., and Artières, T. 2017. “A meta-learning approach to one-step active learning.” arXiv preprint arXiv:1706.08334.
  • Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. 2016. “The Cityscapes Dataset for Semantic Urban Scene Understanding.” ArXiv160401685 Cs.
  • Ebert, S., Fritz, M., and Schiele, B. 2012. “RALF: A Reinforced Active Learning Formulation for Object Class Recognition.” Presented at the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Providence, RI, pp. 3626–3633. doi:10.1109/CVPR.2012.6248108.
  • Gal, Y., Islam, R., and Ghahramani, Z. 2017. “Deep Bayesian Active Learning with Image Data.” ArXiv170302910 Cs Stat.
  • Ganegedara, T. 2018. “Intuitive Guide to Understanding KL Divergence.” https://towardsdatascience.com/light-on-math-machine-learning-intuitive-guide-to-understanding-kl-divergence-2b382ca2b2a8
  • Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., Zhang, T., Gao, S., and Liu, J. 2019. “CE-Net: Context Encoder Network for 2D Medical Image Segmentation.” IEEE Transactions on Medical Imaging, Vol. 38(No. 10): pp. 2281–2292. doi:10.1109/TMI.2019.2903562.
  • Guan, P., Cao, Z., Chen, E., Liang, S., Tan, M., and Yu, J. 2020. “A real-time semantic visual SLAM approach with points and objects.” International Journal of Advanced Robotic Systems, Vol. 17(No. 1): pp. 172988142090544. doi:10.1177/1729881420905443.
  • Han, D., Huong, P., and Cheng, S. 2023. “Enhancing Semantic Segmentation through Reinforced Active Learning: Combating Dataset Imbalances and Bolstering Annotation Efficiency.” Journal of Electronic & Information Systems, Vol. 5(No. 2): pp. 45–60. doi:10.30564/jeis.v5i2.6063.
  • He, K., Zhang, X., Ren, S., and Sun, J. 2015. “Deep Residual Learning for Image Recognition.” ArXiv151203385 Cs.
  • Hesamian, M.H., Jia, W., He, X., and Kennedy, P. 2019. “Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges.” Journal of Digital Imaging, Vol. 32(No. 4): pp. 582–596. doi:10.1007/s10278-019-00227-x.
  • Hochreiter, S., and Schmidhuber, J. 1997. “Long Short-term Memory.” Neural Computation, Vol. 9(No. 8): pp. 1735–1780. doi:10.1162/neco.1997.9.8.1735.
  • Joshi, A. J., Porikli, F., and Papanikolopoulos, N. 2009. “Multi-Class Active Learning for Image Classification.” Presented at the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), IEEE, Miami, FL, pp. 2372–2379. doi:10.1109/CVPR.2009.5206627.
  • Kampffmeyer, M., Salberg, A.-B., and Jenssen, R. 2016. “Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks.” Presented at the 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, Las Vegas, NV, USA, pp. 680–688. doi:10.1109/CVPRW.2016.90.
  • Konyushkova, K., Sznitman, R., and Fua, P. 2019. “Discovering General-Purpose Active Learning Strategies.” ArXiv181004114 Cs Stat.
  • Konyushkova, K., Sznitman, R., and Fua, P. 2017. “Learning Active Learning from Data.” Advances in Neural Information Processing Systems, Vol. 30, pp. 4225–4235.
  • Lake, B.M., Salakhutdinov, R., and Tenenbaum, J.B. 2019. “The Omniglot challenge: a 3- year progress report.” Current Opinion in Behavioral Sciences, Vol. 29 pp. 97–104. doi:10.1016/j.cobeha.2019.04.007.
  • Li, X., and Guo, Y. 2013. “Adaptive Active Learning for Image Classification.” Presented at the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Portland, OR, USA, pp. 859–866. doi:10.1109/CVPR.2013.116.
  • Lim, S.H., Xu, H., and Mannor, S. 2016. “Reinforcement Learning in Robust Markov Decision Processes.” Mathematics of Operations Research, Vol. 41(No. 4): pp. 1325–1353. doi:10.1287/moor.2016.0779.
  • Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. 2017. “Feature Pyramid Networks for Object Detection.” Presented at the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Honolulu, HI, pp. 936–944. doi:10.1109/CVPR.2017.106.
  • Liu, M., Buntine, W., and Haffari, G. 2018. “Learning How to Actively Learn: A Deep Imitation Learning Approach.” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Melbourne, Australia, pp. 1874–1883. doi:10.18653/v1/P18-1174.
  • Mackowiak, R., Lenz, P., Ghori, O., Diego, F., Lange, O., and Rother, C. 2018. “CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation.” ArXiv181009726 Cs.
  • Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. 2013. “Playing Atari with Deep Reinforcement Learning.” ArXiv13125602 Cs.
  • Mohapatra, S., Yogamani, S., Gotzig, H., Milz, S., and Mader, P. 2021. “BEVDetNet: Bird’s Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving.” ArXiv210410780 Cs.
  • Pang, K., Dong, M., Wu, Y., and Hospedales, T. 2018. “Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning.” arXiv preprint arXiv:1806.04798.
  • Richter, S. R., Vineet, V., Roth, S., and Koltun, V. 2016. “Playing for Data: Ground Truth from Computer Games.” ArXiv160802192 Cs.
  • Sener, O., and Savarese, S. 2018. “Active Learning for Convolutional Neural Networks: A Core-Set Approach.” arXiv preprint arXiv:1708.00489.
  • Sutton, R. S., and Barto, A. G. 2018. Reinforcement learning: An introduction. Cambridge, MA: The MIT Press.
  • Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., and Roy, A. 2021. “A Reinforced Active Learning Algorithm for Semantic Segmentation in Complex Imaging.” IEEEAccess, Digital Object Identifier. doi:10.1109/ACCESS.2021.3136647.
  • van Hasselt, H., Guez, A., and Silver, D. 2015. “Deep Reinforcement Learning with DoubleQ-Learning.” ArXiv,abs/1509.06461.
  • Woodward, M., and Finn, C. 2017. “Active One-shot Learning.” arXiv preprint arXiv:1702.06559.