This document proposes a framework called A-fast-RCNN that uses an adversarial network to generate hard positive examples for object detection training. It aims to address issues with occlusions, deformations, and lack of examples. The framework trains an Adversarial Sample Discovery Network (ASDN) to generate occluded and deformed examples, and an Adversarial Sample Transfer Network (ASTN) to refine localization. Evaluation on VOC2007 and VOC2012 datasets showed improved detection performance over fast R-CNN alone.

1. A-fast-RCNN:
Hard positive generation via adversary
for object detection
Du Min
2017.4.21

2. Idea
1. Occlusions
2. Deformations
3. No enough examples
Generate examples using GAN idea ---- adversary

3. Framework
occlusions deformations

4. Train --- ASDN
• before: train fast RCNN (10K)
• find ground truth for ASDN
• loss function
• train for 10k
• train jointly with
fast-RCNN

5. Train --- ASTN
• Only update localization network
• Train jointly with fast-RCNN

6. Result
VOC2007: 2.3%
VOC2012: 2.6%
Category-aware

7. Thanks for your attentions!