InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting
Hao-Shu Fang, Jianhua Sun, Runzhong Wang, Minghao Gou, Yong-Lu Li,, Cewu Lu

TL;DR
InstaBoost introduces a simple, efficient data augmentation technique for instance segmentation that leverages pixel redundancy and location probability maps to improve Mask R-CNN performance without increasing computational cost.
Contribution
The paper proposes a novel augmentation method using existing annotations and location maps to enhance instance segmentation accuracy.
Findings
Improves Mask R-CNN performance by 1.7 mAP on COCO
Increases Pascal VOC mAP by 3.3 with jittering
Boosts R101-Mask R-CNN from 35.7 to 37.9 mAP
Abstract
Instance segmentation requires a large number of training samples to achieve satisfactory performance and benefits from proper data augmentation. To enlarge the training set and increase the diversity, previous methods have investigated using data annotation from other domain (e.g. bbox, point) in a weakly supervised mechanism. In this paper, we present a simple, efficient and effective method to augment the training set using the existing instance mask annotations. Exploiting the pixel redundancy of the background, we are able to improve the performance of Mask R-CNN for 1.7 mAP on COCO dataset and 3.3 mAP on Pascal VOC dataset by simply introducing random jittering to objects. Furthermore, we propose a location probability map based approach to explore the feasible locations that objects can be placed based on local appearance similarity. With the guidance of such map, we boost the…
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Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Multimodal Machine Learning Applications
MethodsRegion Proposal Network · Average Pooling · Step Decay · InstaBoost · Feature Pyramid Network · Cascade R-CNN · Residual Connection · *Communicated@Fast*How Do I Communicate to Expedia? · 1x1 Convolution · Batch Normalization
