Abstract

Many computer vision tasks are template-based learning tasks in which multiple instances of a specific concept (e.g. multiple images of a subject’s face) are available at once to the learning algorithm. The template structure of the input data provides an opportunity for generating a robust and discriminative unified template-level representation that effectively exploits the inherent diversity of feature-level information across instances within a template. In contrast to other feature aggregation methods, we propose a new technique to dynamically predict weights that consider factors such as noise and redundancy in assessing the importance of image-level features and use those weights to appropriately aggregate the features into a single template-level representation. We present extensive experimental results on the MNIST, CIFAR10, UCF101, IJB-A, IJB-B, and Janus CS4 datasets to show that the new …

Date

2019

Authors

Zekun Li, Yue Wu, Wael Abd-Almageed, Prem Natarajan

Conference

Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part V 14

Pages

436-451

Publisher

Springer International Publishing