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Learning Intelligent Dialogs for Bounding-Box Annotation

Ksenia Konyushkova
Chris Lampert
Vittorio Ferrari
CVPR (2018) (to appear)


We introduce Intelligent Annotation Dialogs for bound- ing box annotation. We train an agent to automatically choose a sequence of actions for a human annotator to pro- duce a bounding box in a minimal amount of time. Specifi- cally, we consider two actions: box verification [34], where the annotator verifies a box generated by an object detector, and manual box drawing. We explore two kinds of agents, one based on predicting the probability that a box will be positively verified, and the other based on reinforcement learning. We demonstrate that (1) our agents are able to learn efficient annotation strategies in several scenarios, automatically adapting to the difficulty of an input image, the desired quality of the boxes, the strenght of the detector, and other factors; (2) in all scenarios the resulting annota- tion dialogs speed up annotation compated to manual box drawing alone and box verification alone, while also out- performing any fixed combination of verification and draw- ing in most scenarios; (3) in a realistic scenario where the detector is iteratively re-trained, our agents evolve a series of strategies that reflect the shifting trade-off between veri- fication and drawing as the detector grows stronger.

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