This is the homepage of Few-Shot Multi-Agent Perception (FS-MAP).
- Chenyou Fan, Junjie Hu, Jianwei Huang. "Few-Shot Multi-Agent Perception." In 29th ACM International Conference on Multimedia (ACM MM'21).
We use AirSim dataset to perform few-shot segmentation task. We modify this dataset to form few-shot learning tasks. We provide direct download link below.
- The code is modified upon DeepEMD. Please properly cite their excellent work if you use this code in research.
- We provide self-contained implementation in the following section.
- Download dataset from Google Drive link, extract
airsim-mrmps-data
into./dataset/
folder. - Download our trained models from Google Drive link, extract to
./results
folder, checkresults/seg/meta
folders. - Check our split in
configs/split_save_files.pkl
python train.py --ph=0 --is_seg=1 --pretrain_dir=results/seg/pre_train
python train.py --ph=1 --is_seg=1 --pretrain_dir=results/seg/pre_train
- check results/seg/meta and find the latest checkpoint dir, to replace XXX
- set "--shot=5" to test 5-shot case
python test.py --is_seg=1 --model_dir=XXXX --loop=0
python test.py --is_seg=1 --model_dir=XXXX --loop=0 --shot=5
to use our trained models, download as above mentioned, and execute
python test.py --is_seg=1 --model_dir=results/seg/meta/loop0
python test.py --is_seg=1 --model_dir=results/seg/meta/loop0_st5 --shot=5
Please cite our work if you use this code.
@inproceedings{fan2021fsmap,
title={Few-Shot Multi-Agent Perception},
author={Fan, Chenyou and Hu, Junjie and Huang, Jianwei},
booktitle={ACM MultiMedia},
year={2021}
}
Please also properly cite the following excellent work in research.
Python = 3.8 PyTorch = 1.7+ [here]
GPU training with 4G+ memory, testing with 2G+ memory.
pip install scikit-learn pretrainedmodels