Convolutional Low-Resolution Fine-Grained Classification

15 Mar 2017Dingding CaiKe ChenYanlin QianJoni-Kristian Kämäräinen

Successful fine-grained image classification methods learn subtle details between visually similar (sub-)classes, but the problem becomes significantly more challenging if the details are missing due to low resolution. Encouraged by the recent success of Convolutional Neural Network (CNN) architectures in image classification, we propose a novel resolution-aware deep model which combines convolutional image super-resolution and convolutional fine-grained classification into a single model in an end-to-end manner... (read more)

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