MultiModal-Collaborative (MMC) Learning Framework for integrating RGB and Thermal spectral modalities

This is the official code for NeurIPS 2021 Machine Learning for Autonomous Driving Workshop Paper, "Does Thermal data make the detection systems more reliable?" by Shruthi Gowda, Elahe Arani and Bahram Zonooz.

Methodology

Architecture

Detection Head : SSD
Detection Backbone : Resnet (CNN-based) or DEiT (Transformer-based)

MMC framework

MMC framework has multiple versions

KD.ENABLE: True
KD.ENABLE_DML: True

1. MMC (Base Version) : Det Loss + DML Loss 
    KD.DISTILL_TYPE : KL, AT, L2, L2B
    KL (KL divergence), AT (Attention loss), L2 (L2 norm at head layer), L2B (L2 norm of backbone features)
   
2. MMC v1 (Reconstruction) : Det Loss + DML Loss + Recon Loss
    KD.AUX_RECON = True
    KD.AUX_RECON_MODE = "normal"

3. MMC v2 (Cross Reconstruction) : Det Loss + DML Loss + Cross Recon Loss
    KD.AUX_RECON = True
    KD.AUX_RECON_MODE = "cross"

We also try other techniques for comparison

Fusion
1. Input Fusion
    KD.CONCAT_INPUT
2. Feature Fusion
    KD.CONCAT_FEATURES
    CONCAT_LAYERS

Installation

You can prepare the environment using:

pip install -r requirements.txt

You can build the project using the following script:

./build {conda_env_name}

Datasets

Two datasets "FLIR" and "KAIST" are used in this repo

FLIR : https://www.flir.eu/oem/adas/adas-dataset-form/

KAIST : https://soonminhwang.github.io/rgbt-ped-detection/

Running

Train

There are 2 networks, one receiving RGB images and one receiving thermal images. Both require different config files.

python train.py --config-file <thermal-config-file> --teacher-config-file <rgb-config-file>

Test

For evaluation only one network is used - the first network (RGB or Teacher network)

python test.py --config-file <config-file> --ckpt <model_final.pth> 

Model Checkpoints

Cite Our Work

If you find the code useful in your research, please consider citing our paper:

@article{gowda2021does,
  title={Does Thermal data make the detection systems more reliable?},
  author={Gowda, Shruthi and Zonooz, Bahram and Arani, Elahe},
  journal={arXiv e-prints},
  pages={arXiv--2111},
  year={2021}
}

License

This project is licensed under the terms of the MIT license.