  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.
