Weakly Semi-Supervised Deep Learning for Multi-Label Image Annotation

Abstract

In this paper, we study leveraging both weakly labeled images and unlabeled images for multi-label image annotation. Motivated by the recent advance in deep learning, we propose an approach called we akly se mi-supervised d eep learning for multi-label image annotation (WeSed). In WeSed, a novel weakly weighted pairwise ranking loss is effectively utilized to handle weakly labeled images, while a triplet similarity loss is employed to harness unlabeled images. WeSed enables us to train deep convolutional neural network (CNN) with images from social networks where images are either only weakly labeled with several labels or unlabeled. We also design an efficient algorithm to sample high-quality image triplets from large image datasets to fine-tune the CNN. WeSed is evaluated on benchmark datasets for multi-label annotation. The experiments demonstrate the effectiveness of our proposed approach and show that the leverage of the weakly labeled images and unlabeled images leads to a significantly better performance.