One-Click Annotation with Guided Hierarchical Object Detection
arXiv:1810.00609
Abstract
The increase in data collection has made data annotation an interesting and valuable task in the contemporary world. This paper presents a new methodology for quickly annotating data using click-supervision and hierarchical object detection. The proposed work is semi-automatic in nature where the task of annotations is split between the human and a neural network. We show that our improved method of annotation reduces the time, cost and mental stress on a human annotator. The research also highlights how our method performs better than the current approach in different circumstances such as variation in number of objects, object size and different datasets. Our approach also proposes a new method of using object detectors making it suitable for data annotation task. The experiment conducted on PASCAL VOC dataset revealed that annotation created from our approach achieves a mAP of 0.995 and a recall of 0.903. The Our Approach has shown an overall improvement by 8.5%, 18.6% in mean average precision and recall score for KITTI and 69.6%, 36% for CITYSCAPES dataset. The proposed framework is 3-4 times faster as compared to the standard annotation method.
References in corpus (11)
- Prototypical Networks for Few-shot Learning
- Focal Loss for Dense Object Detection
- The Cityscapes Dataset for Semantic Urban Scene Understanding
- Few-Shot Learning with Graph Neural Networks
- Few-Shot Learning with Metric-Agnostic Conditional Embeddings
- Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning
- Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
- Annotating Object Instances with a Polygon-RNN
- Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm
- Training object class detectors with click supervision
- Learning Intelligent Dialogs for Bounding Box Annotation