# Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification

arXiv: https://arxiv.org/abs/2002.03353 · HTML: https://mlsanity.com/paper/2002.03353/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2020.

## Built on by (most cited first, 18 of 18)

- [A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges](https://mlsanity.com/paper/2206.13732/) (2022)
- [Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification](https://mlsanity.com/paper/2101.06635/) (2021)
- [Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment](https://mlsanity.com/paper/2210.01439/) (2022)
- [SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization](https://mlsanity.com/paper/2209.02109/) (2022)
- [Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition](https://mlsanity.com/paper/2107.06538/) (2021)
- [Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2108.08063/) (2021)
- [Progressive Co-Attention Network for Fine-grained Visual Classification](https://mlsanity.com/paper/2101.08527/) (2021)
- [Convolutional Fine-Grained Classification with Self-Supervised Target Relation Regularization](https://mlsanity.com/paper/2208.01997/) (2022)
- [Self-Distribution Binary Neural Networks](https://mlsanity.com/paper/2103.02394/) (2021)
- [Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification](https://mlsanity.com/paper/2106.03432/) (2021)
- [A Task-aware Dual Similarity Network for Fine-grained Few-shot Learning](https://mlsanity.com/paper/2210.12348/) (2022)
- [TSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency](https://mlsanity.com/paper/2110.05182/) (2021)
- [Shaping Visual Representations with Attributes for Few-Shot Recognition](https://mlsanity.com/paper/2112.06398/) (2021)
- [Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification](https://mlsanity.com/paper/2106.10920/) (2021)
- [Fine-grained Classification of Solder Joints with α-skew Jensen-Shannon Divergence](https://mlsanity.com/paper/2209.09857/) (2022)
- [IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks](https://mlsanity.com/paper/2210.02637/) (2022)
- [WaveNets: Wavelet Channel Attention Networks](https://mlsanity.com/paper/2211.02695/) (2022)
- [ShuffleMix: Improving Representations via Channel-Wise Shuffle of Interpolated Hidden States](https://mlsanity.com/paper/2305.18684/) (2023)

## Abstract (arXiv)

Classifying the sub-categories of an object from the same super-category (e.g. bird species, car and aircraft models) in fine-grained visual classification (FGVC) highly relies on discriminative feature representation and accurate region localization. Existing approaches mainly focus on distilling information from high-level features. In this paper, however, we show that by integrating low-level information (e.g. color, edge junctions, texture patterns), performance can be improved with enhanced feature representation and accurately located discriminative regions. Our solution, named Attention Pyramid Convolutional Neural Network (AP-CNN), consists of a) a pyramidal hierarchy structure with a top-down feature pathway and a bottom-up attention pathway, and hence learns both high-level semantic and low-level detailed feature representation, and b) an ROI guided refinement strategy with ROI guided dropblock and ROI guided zoom-in, which refines features with discriminative local regions enhanced and background noises eliminated. The proposed AP-CNN can be trained end-to-end, without the need of additional bounding box/part annotations. Extensive experiments on three commonly used FGVC datasets (CUB-200-2011, Stanford Cars, and FGVC-Aircraft) demonstrate that our approach can achieve state-of-the-art performance. Code available at urlhttp://dwz1.cc/ci8so8a