Visual Recognition in Very Low-Quality Settings: Delving Into the Power of Pre-Training
DOI:
https://doi.org/10.1609/aaai.v32i1.12131Keywords:
neural network, deep learning, image recognitionAbstract
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.
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Published
2018-04-29
How to Cite
Cheng, B., Liu, D., Wang, Z., Zhang, H., & Huang, T. (2018). Visual Recognition in Very Low-Quality Settings: Delving Into the Power of Pre-Training. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.12131
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Student Abstract Track