# Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization

arXiv: https://arxiv.org/abs/1710.01168 · HTML: https://mlsanity.com/paper/1710.01168/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2017.

## Built on by (most cited first, 9 of 9)

- [Fine-Grained Image Analysis with Deep Learning: A Survey](https://mlsanity.com/paper/2111.06119/) (2021)
- [Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment](https://mlsanity.com/paper/2210.01439/) (2022)
- [SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization](https://mlsanity.com/paper/2208.14607/) (2022)
- [Fine-Grained Zero-Shot Object Detection](https://mlsanity.com/paper/2507.10358/) (2025)
- [Fine-Grained Few Shot Learning with Foreground Object Transformation](https://mlsanity.com/paper/2109.05719/) (2021)
- [Long-tailed Recognition by Learning from Latent Categories](https://mlsanity.com/paper/2206.01010/) (2022)
- [Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization](https://mlsanity.com/paper/2403.10298/) (2024)
- [Improving Deep Representation Learning via Auxiliary Learnable Target Coding](https://mlsanity.com/paper/2305.18680/) (2023)
- [CausalFSFG: Rethinking Few-Shot Fine-Grained Visual Categorization from Causal Perspective](https://mlsanity.com/paper/2512.21617/) (2025)

## Abstract (arXiv)

Fine-grained image classification is to recognize hundreds of subcategories in each basic-level category. Existing methods employ discriminative localization to find the key distinctions among subcategories. However, they generally have two limitations: (1) Discriminative localization relies on region proposal methods to hypothesize the locations of discriminative regions, which are time-consuming. (2) The training of discriminative localization depends on object or part annotations, which are heavily labor-consuming. It is highly challenging to address the two key limitations simultaneously, and existing methods only focus on one of them. Therefore, we propose a weakly supervised discriminative localization approach (WSDL) for fast fine-grained image classification to address the two limitations at the same time, and its main advantages are: (1) n-pathway end-to-end discriminative localization network is designed to improve classification speed, which simultaneously localizes multiple different discriminative regions for one image to boost classification accuracy, and shares full-image convolutional features generated by region proposal network to accelerate the process of generating region proposals as well as reduce the computation of convolutional operation. (2) Multi-level attention guided localization learning is proposed to localize discriminative regions with different focuses automatically, without using object and part annotations, avoiding the labor consumption. Different level attentions focus on different characteristics of the image, which are complementary and boost the classification accuracy. Both are jointly employed to simultaneously improve classification speed and eliminate dependence on object and part annotations. Compared with state-of-the-art methods on 2 widely-used fine-grained image classification datasets, our WSDL approach achieves the best performance.