Attend in groups: a weakly-supervised deep learning framework for learning from web data

Understand

Large-scale datasets have driven the rapid development of deep neural networks for visual recognition.

• However, annotating a massive dataset is expensive and time-consuming.
• Web images and their labels are, in comparison, much easier to obtain, but direct training on such automatically harvested images can lead to unsatisfactory performance, because the noisy labels of Web images adversely affect the learned recognition models.
• To address this drawback we propose an end-to-end weakly-supervised deep learning framework which is robust to the label noise in Web images.