The Art of Camouflage: Few-shot Learning for Animal Detection and Segmentation
April 14, 2025 · View on GitHub
This repository is the official implementation of the paper entitled: The Art of Camouflage: Few-shot Learning for Animal Detection and Segmentation, IEEE Access, 2024.
Authors: Thanh-Danh Nguyen, Anh-Khoa Nguyen Vu, Nhat-Duy Nguyen, Vinh-Tiep Nguyen, Thanh Duc Ngo, Thanh-Toan Do, Minh-Triet Tran, Tam V. Nguyen*.
[Paper] [ArXiv] [Code] [Project Page]
Updates
[2024/7] We have released the checkpoints, visualization, and initial instructions for FS-CDIS⚡!
1. Environment Setup
Download and install Anaconda with the recommended version from Anaconda Homepage: Anaconda3-2019.03-Linux-x86_64.sh
git clone https://github.com/danhntd/FS-CDIS.git
cd FS-CDIS
curl -O https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh
bash Anaconda3-2019.03-Linux-x86_64.sh
After completing the installation, please create and initiate the workspace with the specific versions below. The experiments were conducted on a Linux server with a single GeForce RTX 2080Ti GPU, CUDA 10.1/10.2, Torch 1.7.
conda create --name FSCDIS python=3
conda activate FSCDIS
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.2 -c pytorch
This source code is based on Detectron2. Please refer to INSTALL.md for the pre-built or building Detectron2 from source.
After setting up the dependencies, use the command pip install -e . in this root to finish.
2. Data Preparation
Download the datasets
The proposed CAMO-FS is available at this link.
Register datasets
Detectron2 requires a step of data registration for those who want to use the external datasets (Detectron2 Docs), which is already prepared in this repository.
3. Training Pipeline
Our detailed proposals of instance triplet loss and instance memory storage:
Configurations
All configs can be found in the ./configs/ directory.
The whole script commands can be found in ./scripts/*.
Released checkpoints and results:
We provide the checkpoints of our final model:
| Model R-101 | FS-CDIS-ITL | mask AP | box AP | FS-CDIS-IMS | mask AP | box AP |
|---|---|---|---|---|---|---|
| 1-shot | link | 4.46 | 4.04 | link | 5.46 | 4.50 |
| 2-shot | link | 5.57 | 7.28 | link | 6.95 | 6.95 |
| 3-shot | link | 6.41 | 7.49 | link | 7.36 | 7.55 |
| 5-shot | link | 8.48 | 9.76 | link | 9.61 | 10.36 |
4. Visualization
Citation
Please use the following bibtex to cite this repository:
@article{nguyen2024art,
title={The Art of Camouflage: Few-shot Learning for Animal Detection and Segmentation},
author={Nguyen, Thanh-Danh and Vu, Anh-Khoa Nguyen and Nguyen, Nhat-Duy and Nguyen, Vinh-Tiep and Ngo, Thanh Duc and Do, Thanh-Toan and Tran, Minh-Triet and Nguyen, Tam V},
journal={IEEE Access},
volume={-},
pages={-},
year={2024},
publisher={IEEE}
}