Abstract
Regions-with-convolutional-neural-network (RCNN) is now a commonly employed object detection pipeline. Its main steps, i.e., proposal generation and convolutional neural network (CNN) feature extraction, have been intensively investigated. We focus on the last step of the system to aggregate thousands of scored box proposals into final object prediction, which we call proposal decimation. We show this step can be enhanced with a very simple box aggregation function by considering statistical properties of proposals with respect to ground truth objects. Our method is with extremely light-weight computation, while it yields an improvement of 3.7% in mAP on PASCAL VOC 2007 test. We explain why it works using some statistics in this paper. © 2015 IEEE.
Keywords
DecimationComputer scienceConvolutional neural networkObject detectionPascal (unit)Artificial intelligenceFeature extractionComputationAggregate (composite)Focus (optics)Object (grammar)Pattern recognition (psychology)Pipeline (software)Artificial neural networkData miningAlgorithmComputer vision
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Publication Info
- Year
- 2015
- Type
- article
- Volume
- 2
- Pages
- 2569-2577
- Citations
- 21
- Access
- Closed
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Cite This
Shu Liu,
Cewu Lu,
Jiaya Jia
(2015).
Box Aggregation for Proposal Decimation: Last Mile of Object Detection.
, 2
, 2569-2577.
https://doi.org/10.1109/iccv.2015.295
Identifiers
- DOI
- 10.1109/iccv.2015.295