PAPN

Abstract

In this paper, we mitigate the problem of Self-Supervised Learning (SSL) for fine-grained representation learning, aimed at distinguishing subtle differences within highly similar subordinate categories. Our preliminary analysis shows that SSL, especially the multi-stage alignment strategy, performs well on generic categories but struggles with fine-grained distinctions. To overcome this limitation, we propose a prototype-based contrastive learning module with stage-wise progressive augmentation. Unlike previous methods, our stage-wise progressive augmentation adapts data augmentation across stages to better suit SSL on fine-grained datasets. The prototype-based contrastive learning module captures both holistic and partial patterns, extracting global and local image representations to enhance feature discriminability. Experiments on popular fine-grained benchmarks for classification and retrieval tasks demonstrate the effectiveness of our method, and extensive ablation studies confirm the superiority of our proposals.

Dataset

You can download the dataset from the following websites:

Dataset	URL
FGVC Aircraft	https://www.kaggle.com/datasets/seryouxblaster764/fgvc-aircraft
CUB200-2011	https://www.kaggle.com/datasets/wenewone/cub2002011
Stanford Cars	https://www.kaggle.com/datasets/rickyyyyyyy/torchvision-stanford-cars

Then you should run the following commands:

cd PAPN
python ./utils/convert_cub.py -p path_of_cub.zip
python ./utils/convert_car.py -p path_of_car.zip
python ./utils/convert_air.py -p path_of_air.zip

The final datasets structure are as follows:

PAPN
|-- dataset
    |-- cub
        |-- train/
        |-- test/
    |-- car
        |-- train/
        |-- test/
    |-- air
        |-- train/
        |-- test/

Run the code

• The experiments are carried out on Ubuntu 18.04.6 LTS, utilizing four GeForce RTX 3090 GPUs, each with 24GB of memory. The CUDA version employed is 11.6.
• Install the required packages:

pip install -r requirements.txt

• The running commands for training:

python main_train.py -c configs/papn_air.yaml
python main_train.py -c configs/papn_car.yaml
python main_train.py -c configs/papn_cub.yaml

• The running commands for linear probing and retrieval:

python main_lincls.py -c configs/papn_air.yaml
python main_lincls.py -c configs/papn_car.yaml
python main_lincls.py -c configs/papn_cub.yaml