# Convolutional Low-Resolution Fine-Grained Classification Source: [Convolutional Low-Resolution Fine-Grained Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:47:17.894Z Authors: Dingding Cai, Ke Chen, Yanlin Qian, Joni-Kristian K\\"am\\"ar\\"ainen Published: 2017-03-15T21:40:48.000Z ## Abstract 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. Extensive experiments on the Stanford Cars and Caltech-UCSD Birds 200-2011 benchmarks demonstrate that the proposed model consistently performs better than conventional convolutional net on classifying fine-grained object classes in low-resolution images. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()