# Convolutional Low-Resolution Fine-Grained Classification

arXiv: https://arxiv.org/abs/1703.05393 · HTML: https://mlsanity.com/paper/1703.05393/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2017.

## Built on by (most cited first, 10 of 10)

- [A Comprehensive Review of Deep Learning-based Single Image Super-resolution](https://mlsanity.com/paper/2102.09351/) (2021)
- [Feature Separation and Recalibration for Adversarial Robustness](https://mlsanity.com/paper/2303.13846/) (2023)
- [Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions](https://mlsanity.com/paper/2106.03727/) (2021)
- [Resolution-Invariant Image Classification based on Fourier Neural Operators](https://mlsanity.com/paper/2304.01227/) (2023)
- [Convolutional Fine-Grained Classification with Self-Supervised Target Relation Regularization](https://mlsanity.com/paper/2208.01997/) (2022)
- [Optical Flow for Video Super-Resolution: A Survey](https://mlsanity.com/paper/2203.10462/) (2022)
- [PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images](https://mlsanity.com/paper/2402.12721/) (2024)
- [Enhancing Fine-Grained Classification for Low Resolution Images](https://mlsanity.com/paper/2105.00241/) (2021)
- [Performance Evaluation of Action Recognition Models on Low Quality Videos](https://mlsanity.com/paper/2204.09166/) (2022)
- [Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images](https://mlsanity.com/paper/2305.19063/) (2023)

## Abstract (arXiv)

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.