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Reducing human efforts in video segmentation annotation with reinforcement learning
- Source :
- Neurocomputing. 405:247-258
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- Manual annotation of video segmentation datasets requires an immense amount of human effort, thus, reduction of human annotation costs is an active topic of research. While many papers deal with the propagation of masks through frames of a video, only a few results attempt to optimize annotation task selection. In this paper we present a deep learning based solution to the latter problem and train it using Reinforcement Learning. Our approach utilizes a modified version of the Dueling Deep Q-Network sharing weight parameters across the temporal axis of the video. This technique enables the trained agent to select annotation tasks from the whole video. We evaluate our annotation task selection method by means of a hierarchical supervoxel segmentation based mask propagation algorithm.
- Subjects :
- 0209 industrial biotechnology
Computer science
business.industry
Cognitive Neuroscience
Deep learning
02 engineering and technology
Machine learning
computer.software_genre
Computer Science Applications
Task (project management)
Reduction (complexity)
Annotation
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Selection (linguistics)
Reinforcement learning
020201 artificial intelligence & image processing
Segmentation
Artificial intelligence
business
computer
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 405
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........d002de605682eebff396c3873a0c9a9b