Efficient Semantic Segmentation using Gradual Grouping
| Autor: | Manu Mathew, Nikitha Vallurupalli, Girish Varma, Sriharsha Annamaneni, Soyeb Nagori, C. V. Jawahar |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
050210 logistics & transportation Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) 05 social sciences Computer Science - Computer Vision and Pattern Recognition Pattern recognition 02 engineering and technology Image segmentation Convolution 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence business Encoder Sparse matrix |
| Zdroj: | CVPR Workshops |
| DOI: | 10.48550/arxiv.1806.08522 |
| Popis: | Deep CNNs for semantic segmentation have high memory and run time requirements. Various approaches have been proposed to make CNNs efficient like grouped, shuffled, depth-wise separable convolutions. We study the effectiveness of these techniques on a real-time semantic segmentation architecture like ERFNet for improving run time by over 5X. We apply these techniques to CNN layers partially or fully and evaluate the testing accuracies on Cityscapes dataset. We obtain accuracy vs parameters/FLOPs trade offs, giving accuracy scores for models that can run under specified runtime budgets. We further propose a novel training procedure which starts out with a dense convolution but gradually evolves towards a grouped convolution. We show that our proposed training method and efficient architecture design can improve accuracies by over 8% with depth wise separable convolutions applied on the encoder of ERFNet and attaching a light weight decoder. This results in a model which has a 5X improvement in FLOPs while only suffering a 4% degradation in accuracy with respect to ERFNet. Comment: CVPR 2018 Workshop paper |
| Databáze: | OpenAIRE |
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