no code implementations • ICCV 2021 • Jan P. Klopp, Keng-Chi Liu, Shao-Yi Chien, Liang-Gee Chen
This hybrid solution has a small scope of only 10s or 100s of frames and allows for a low complexity both on the encoding and the decoding side.
no code implementations • CVPR 2021 • Jan P. Klopp, Keng-Chi Liu, Liang-Gee Chen, Shao-Yi Chien
Recent research suggests that generative adversarial networks have the ability to overcome this limitation and serve as a multi-modal loss, especially for textures.
no code implementations • 5 Oct 2020 • Po-Hsiang Yu, Sih-Sian Wu, Jan P. Klopp, Liang-Gee Chen, Shao-Yi Chien
We investigate pruning and quantization for deep neural networks.
no code implementations • 19 Oct 2019 • Jan P. Klopp, Liang-Gee Chen, Shao-Yi Chien
The method can be applied to any existing video codec to increase coding gains while its low computational footprint allows for an application under resource-constrained conditions.
no code implementations • ICCV 2019 • Keng-Chi Liu, Yi-Ting Shen, Jan P. Klopp, Liang-Gee Chen
Our proposed two-stage integration more than halves the gap towards fully supervised methods when compared to previous state-of-the-art in transfer learning.