[논문 요약27] [10] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. 88(2):303– 338, 2010. 8

[업데이트 2018.09.07 10:15]

1. 베이스 논문

[논문 요약17] Perceptual Generative Adversarial Networks for Small Object Detection

(http://arclab.tistory.com/172)

To evaluate the generalization capability of the proposed generator on more general and diverse object categories, we train the proposed detection pipeline with the generator network end-to-end on the union of the trainval set of PASCAL VOC 2007 and VOC 2012 [10], and evaluate it on the test set of VOC 2007 on the most challenging classes (i.e., boat, bottle, chair and plant) in which small instances are most common.

2. 레퍼런스 논문

[10] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. 88(2):303–338, 2010. 8

(http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.157.5766&rep=rep1&type=pdf)

3. 주요 내용 요약

3.1 레퍼런스 논문 주요 내용

PASCAL Visual Object Classes (VOC) challenge는 visual object category recognition 및 detection을 위한 벤치마크이며 표준화된 데이터셋 및 평가 방법을 제공합니다.

3.2 베이스 논문에서 인용한 내용

General small object detection을 위해 Pascal VOC 데이터셋을 사용하였습니다.