In recent years, image super-resolution (SR) methods using convolutional neural networks (CNNs) have achieved successful results. Nevertheless, it is often difficult to apply them in resource-constrained environments due to the requirement of heavy computation and huge storage capacity. To address this issue, we propose an efficient network model for SR, called LarvaNet. First, we investigate a number of architectural factors for a baseline model and find optimal settings in terms of performance, number of parameters, and running time. Based on that, we design our model using a multi-exit architecture. Our experiments show that the proposed method achieves state-of-the-art SR performance with a reasonable number of parameters and running time. We also show that the multi-exit architecture of the proposed model allows us to control the trade-off between resource consumption and SR performance by selecting which exit point to be used.
|Title of host publication||Computer Vision – ECCV 2020 Workshops, Proceedings|
|Editors||Adrien Bartoli, Andrea Fusiello|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||14|
|Publication status||Published - 2020|
|Event||Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom|
Duration: 2020 Aug 23 → 2020 Aug 28
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||Workshops held at the 16th European Conference on Computer Vision, ECCV 2020|
|Period||20/8/23 → 20/8/28|
Bibliographical noteFunding Information:
Acknowledgement. This work was supported by the IITP grant funded by the Korea government (MSIT) (R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding) and the Artificial Intelligence Graduate School Program (Yonsei University, 2020-0-01361).
© 2020, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)