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

2018-02-08

Proceedings of the AAAI Conference on Artificial Intelligence, 32

Volume

Thirty-Second AAAI Conference on Artificial Intelligence 2018

Student Abstract Track

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.

10.1609/aaai.v32i1.12131