Visual Recognition in Very Low-Quality Settings: Delving Into the Power of Pre-Training

Bowen Cheng, Ding Liu, Zhangyang Wang, Haichao Zhang, Thomas S. Huang

Abstract: Visual recognition from very low-quality images is an extremely challenging task with great practical values. While deep networks have been extensively applied to low-quality image restoration and high-quality image recognition tasks respectively, few works have been done on the important problem of recognition from very low-quality images.This paper presents a degradation-robust pre-training approach on improving deep learning models towards this direction. Extensive experiments on different datasets validate the effectiveness of our proposed method.