Multi-criteria online frame-subset selection for autonomous vehicle videos
| Autor: | Ashwin Bhoyar, Sayan Mandal, Madhumita Bharde, Niloy Ganguly, Sourangshu Bhattacharya, Suparna Bhattacharya, Soumi Das |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
Active learning (machine learning) Context (language use) 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Artificial Intelligence Component (UML) 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 010306 general physics Selection (genetic algorithm) business.industry Deep learning Frame (networking) Task (computing) Signal Processing Active learning Benchmark (computing) 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business computer Software |
| Zdroj: | Pattern Recognition Letters. 133:349-355 |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2020.03.031 |
| Popis: | Data Subset selection for training learning models for a variety of tasks, has been widely studied in the literature of batch mode active learning. Recent works attempt to utilize the model specific signals in the deep learning context for computer vision tasks. Companies, in their bid to create safe autonomous driving models, train and test their models on billions of miles of driving data; not all of which may be valuable for a training task. In this paper, we study the problem of frame-subset selection from autonomous vehicle driving data, for the problem of semantic segmentation - which is a crucial component of the perception module in an autonomous driving system. We find that state of the art methods for deep active learning do not utilize pairwise similarity between incoming and existing frames. We explore both active learning settings, where labels for incoming points are not available, as well as frame selection settings and find that our method selects more valuable frames than only score-based frame subset selection, or frame subset selection without label information. We demonstrate the effectiveness of our method using DeeplabV3+ model on both benchmark as well as datasets generated by driving simulators. Our generated dataset and code will be made publicly available. |
| Databáze: | OpenAIRE |
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