Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution
| Autor: | Debajyoti Dhar, Saumyaa Shah, Litu Rout, S. Manthira Moorthi |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Monte Carlo method Perspective (graphical) Computer Science - Computer Vision and Pattern Recognition Sampling (statistics) Space (commercial competition) Superresolution Action (philosophy) Reinforcement learning Artificial intelligence business Parametric statistics |
| Zdroj: | CVPR Workshops |
| Popis: | In the past few years supervised and adversarial learning have been widely adopted in various complex computer vision tasks. It seems natural to wonder whether another branch of artificial intelligence, commonly known as Reinforcement Learning (RL) can benefit such complex vision tasks. In this study, we explore the plausible usage of RL in super resolution of remote sensing imagery. Guided by recent advances in super resolution, we propose a theoretical framework that leverages the benefits of supervised and reinforcement learning. We argue that a straightforward implementation of RL is not adequate to address ill-posed super resolution as the action variables are not fully known. To tackle this issue, we propose to parameterize action variables by matrices, and train our policy network using Monte-Carlo sampling. We study the implications of parametric action space in a model-free environment from theoretical and empirical perspective. Furthermore, we analyze the quantitative and qualitative results on both remote sensing and non-remote sensing datasets. Based on our experiments, we report considerable improvement over state-of-the-art methods by encapsulating supervised models in a reinforcement learning framework. Computer Vision and Pattern Recognition (CVPR) Workshop on Large Scale Computer Vision for Remote Sensing Imagery |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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