  Important people detection is to automatically detect the individuals who
play the most important roles in a social event image, which requires the
designed model to understand a high-level pattern. However, existing methods
rely heavily on supervised learning using large quantities of annotated image
samples, which are more costly to collect for important people detection than
for individual entity recognition (eg, object recognition). To overcome this
problem, we propose learning important people detection on partially annotated
images. Our approach iteratively learns to assign pseudo-labels to individuals
in un-annotated images and learns to update the important people detection
model based on data with both labels and pseudo-labels. To alleviate the
pseudo-labelling imbalance problem, we introduce a ranking strategy for
pseudo-label estimation, and also introduce two weighting strategies: one for
weighting the confidence that individuals are important people to strengthen
the learning on important people and the other for neglecting noisy unlabelled
images (ie, images without any important people). We have collected two
large-scale datasets for evaluation. The extensive experimental results clearly
confirm the efficacy of our method attained by leveraging unlabelled images for
improving the performance of important people detection.
