Fine-grained classification of low-resolution image
Cai, Dingding (2017)
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Successful fine-grained image classification methods learn subtle details between visually similar (sub-)categories, but the problem becomes significantly more challenging if the details are missing due to low resolution. Alternatively, encouraged by the recent success of Fully Convolutional Neural Network (FCNN) architectures in single image super-resolution, we propose a novel Resolution-Aware Classification Neural Network (RACNN). More precisely, we combine convolutional image super-resolution and convolutional fine-grained classification together in an end-to-end cascade manner, which first improves the resolution of low-resolution images and then recognises objects in the images. Extensive experiments on the Stanford Cars, Caltech-UCSD Birds 200-2011 and Oxford 102 Category Flowers benchmarks demonstrate that the proposed model consistently performs better than conventional convolutional models on categorising fine-grained object classes in low-resolution images.