Within-network ensemble for face attributes classification

Face attributes classification is drawing attention as a research topic with applications in multiple domains, such as video surveillance and social media analysis. In this work, we propose to train attributes in groups based on their localization (head, eyes, nose, cheek, mouth, shoulder, and general areas) in an end-to-end framework considering the correlations between the different attributes. Furthermore, a novel ensemble learning technique is introduced within the network itself that reduces the time of training compared to ensemble of several models. Our approach outperforms the state-of-the-art of the attributes with an average improvement of almost 0.60% and 0.48% points, on the public CELEBA and LFWA datasets, respectively

Cross-stitch Networks for Multi-task Learning

Multi-task learning in Convolutional Networks has displayed remarkable success in the field of recognition. This success can be largely attributed to learning shared representations from multiple supervisory tasks. However, existing multi-task approaches rely on enumerating multiple network architectures specific to the tasks at hand, that do not generalize. In this paper, we propose a principled approach to learn shared representations in ConvNets using multi-task learning. Specifically, we propose a new sharing unit: "cross-stitch" unit. These units combine the activations from multiple networks and can be trained end-to-end. A network with cross-stitch units can learn an optimal combination of shared and task-specific representations. Our proposed method generalizes across multiple tasks and shows dramatically improved performance over baseline methods for categories with few training examples.Comment: To appear in CVPR 2016 (Spotlight