RGPNet: A Real-Time General Purpose Semantic Segmentation

Autor:	Arani, Elahe, Marzban, Shabbir, Pata, Andrei, Zonooz, Bahram
Rok vydání:	2019
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We propose a real-time general purpose semantic segmentation architecture, RGPNet, which achieves significant performance gain in complex environments. RGPNet consists of a light-weight asymmetric encoder-decoder and an adaptor. The adaptor helps preserve and refine the abstract concepts from multiple levels of distributed representations between the encoder and decoder. It also facilitates the gradient flow from deeper layers to shallower layers. Our experiments demonstrate that RGPNet can generate segmentation results in real-time with comparable accuracy to the state-of-the-art non-real-time heavy models. Moreover, towards green AI, we show that using an optimized label-relaxation technique with progressive resizing can reduce the training time by up to 60% while preserving the performance. We conclude that RGPNet obtains a better speed-accuracy trade-off across multiple datasets. Comment: Accepted at IEEE Winter Conference on Applications of Computer Vision (WACV, 2021)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1912.01394 Zobrazit plný text záznamu View this record from Arxiv