Training Region Based Object Detectors With Online Hard Example Mining Shrivastava Cvpr 2016

Info

Title: Training Region-based Object Detectors with Online Hard Example Mining
Task: Object Detection
Author: A. Shrivastava, A. Gupta, and R. Girshick
Date: Apr. 2016
Arxiv: 1604.03540
Published: CVPR 2016

Highlights & Drawbacks

Learning-based design for balancing examples for ROI in 2-stage detection network
Plug-in ready trick, easy to be integrated
Additional Parameters for Training

Motivation & Design

There is a 1:3 strategy in Faster-RCNN network, which samples negative ROIs(backgrounds) to balance the ratio for positive and negative data in a batch. It’s empirical and hand-designed(need additional effort when setting hyper-params).

(OHEM)Training Region-based Object Detectors with Online Hard Example Mining

The authors designed an additional sub-network to “learn” the sampling process for negative ROIs, forcing the network focus on ones which are similar to objects(the hard ones), such as backgrounds contain part of objects.

The ‘hard’ examples are defined using probability from detection head, which means that the sample network is exactly the classification network. In practice, the selecting range is set to [0.1, 0.5].