Beyond Context: Exploring Semantic Similarity for Tiny Face Detection
This addresses the challenge of detecting small, variable faces in cluttered scenes for computer vision applications, representing an incremental advance.
The paper tackles the problem of tiny face detection by exploiting semantic similarity among predicted targets to boost detectors, demonstrating improvements on three benchmark datasets.
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.