# Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition

arXiv: https://arxiv.org/abs/2102.09875 · HTML: https://mlsanity.com/paper/2102.09875/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 47 references that are in the map.
- First posted on arXiv 19 Feb 2021.

## Builds on (most cited first, 26 of 26)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Bilinear CNNs for Fine-grained Visual Recognition](https://mlsanity.com/paper/1504.07889/) (2015)
- [Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization](https://mlsanity.com/paper/1704.04232/) (2017)
- [Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning](https://mlsanity.com/paper/1806.06193/) (2018)
- [Adversarial Complementary Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1804.06962/) (2018)
- [Part-based R-CNNs for Fine-grained Category Detection](https://mlsanity.com/paper/1407.3867/) (2014)
- [Learning to Navigate for Fine-grained Classification](https://mlsanity.com/paper/1809.00287/) (2018)
- [Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches](https://mlsanity.com/paper/2003.03836/) (2020)
- [Learning Attentive Pairwise Interaction for Fine-Grained Classification](https://mlsanity.com/paper/2002.10191/) (2020)
- [Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up](https://mlsanity.com/paper/1903.02827/) (2019)
- [Learning Deep Bilinear Transformation for Fine-grained Image Representation](https://mlsanity.com/paper/1911.03621/) (2019)
- [Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization](https://mlsanity.com/paper/1909.11378/) (2019)
- [Bird Species Categorization Using Pose Normalized Deep Convolutional Nets](https://mlsanity.com/paper/1406.2952/) (2014)
- [The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition](https://mlsanity.com/paper/1511.06789/) (2015)
- [Dynamic Computational Time for Visual Attention](https://mlsanity.com/paper/1703.10332/) (2017)
- [Two-Phase Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1708.02108/) (2017)
- [Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition](https://mlsanity.com/paper/2004.02684/) (2020)
- [Classification-Specific Parts for Improving Fine-Grained Visual Categorization](https://mlsanity.com/paper/1909.07075/) (2019)
- [Unifying Deep Local and Global Features for Image Search](https://mlsanity.com/paper/2001.05027/) (2020)
- [Weakly Supervised Localization using Deep Feature Maps](https://mlsanity.com/paper/1603.00489/) (2016)
- [Generalized orderless pooling performs implicit salient matching](https://mlsanity.com/paper/1705.00487/) (2017)
- [End-to-end Learning of a Fisher Vector Encoding for Part Features in Fine-grained Recognition](https://mlsanity.com/paper/2007.02080/) (2020)
- [Fine-grained pose prediction, normalization, and recognition](https://mlsanity.com/paper/1511.07063/) (2015)

## Built on by (most cited first, 11 of 11)

- [TransFG: A Transformer Architecture for Fine-grained Recognition](https://mlsanity.com/paper/2103.07976/) (2021)
- [Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments](https://mlsanity.com/paper/2106.03917/) (2021)
- [Fine-Grained Predicates Learning for Scene Graph Generation](https://mlsanity.com/paper/2204.02597/) (2022)
- [Fine-grained Visual Classification with High-temperature Refinement and Background Suppression](https://mlsanity.com/paper/2303.06442/) (2023)
- [Adaptive Fine-Grained Predicates Learning for Scene Graph Generation](https://mlsanity.com/paper/2207.04602/) (2022)
- [A Novel Plug-in Module for Fine-Grained Visual Classification](https://mlsanity.com/paper/2202.03822/) (2022)
- [Global-Local Similarity for Efficient Fine-Grained Image Recognition with Vision Transformers](https://mlsanity.com/paper/2407.12891/) (2024)
- [Salient Mask-Guided Vision Transformer for Fine-Grained Classification](https://mlsanity.com/paper/2305.07102/) (2023)
- [Conviformers: Convolutionally guided Vision Transformer](https://mlsanity.com/paper/2208.08900/) (2022)
- [Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN Models](https://mlsanity.com/paper/2210.04613/) (2022)
- [An attention-driven hierarchical multi-scale representation for visual recognition](https://mlsanity.com/paper/2110.12178/) (2021)

## Abstract (arXiv)

Fine-grained image recognition is very challenging due to the difficulty of capturing both semantic global features and discriminative local features. Meanwhile, these two features are not easy to be integrated, which are even conflicting when used simultaneously. In this paper, a retrieval-based coarse-to-fine framework is proposed, where we re-rank the TopN classification results by using the local region enhanced embedding features to improve the Top1 accuracy (based on the observation that the correct category usually resides in TopN results). To obtain the discriminative regions for distinguishing the fine-grained images, we introduce a weakly-supervised method to train a box generating branch with only image-level labels. In addition, to learn more effective semantic global features, we design a multi-level loss over an automatically constructed hierarchical category structure. Experimental results show that our method achieves state-of-the-art performance on three benchmarks: CUB-200-2011, Stanford Cars, and FGVC Aircraft. Also, visualizations and analysis are provided for better understanding.