@inproceedings{40d6b16a499f4f45a8dd29d4001cfe48,
title = "Beyond Context: Exploring Semantic Similarity for Tiny Face Detection",
abstract = "Tiny face detection aims to find faces with high degrees of variability in scale, resolution and occlusion in cluttered scenes. Due to the very little information available on tiny faces, it is not sufficient to detect them merely based on the information presented inside the tiny bounding boxes or their context. In this paper, we propose to exploit the semantic similarity among all predicted targets in each image to boost current face detectors. To this end, we present a novel framework to model semantic similarity as pairwise constraints within the metric learning scheme, and then refine our predictions with the semantic similarity by utilizing the graph cut techniques. Experiments conducted on three widely-used benchmark datasets have demonstrated the improvement over the-state-of-the-arts gained by applying this idea.",
keywords = "Graph-cut, Metric learning, Semantic information, Tiny face detection",
author = "Yue Xi and Jiangbin Zheng and Xiangjian He and Wenjing Jia and Hanhui Li",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 25th IEEE International Conference on Image Processing, ICIP 2018 ; Conference date: 07-10-2018 Through 10-10-2018",
year = "2018",
month = aug,
day = "29",
doi = "10.1109/ICIP.2018.8451426",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "1907--1911",
booktitle = "2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings",
address = "United States",
}