PyTorch official implementation of "Multi-Modal RGB-D Scene Recognition Across Domains" in Workshop on Multi-Task Learning in Computer Vision (DeepMTL), ICCV 2021.
We introduce a benchmark testbed for a novel unsupervised domain adaptation problem. We revisited the SUNRGB-D dataset, identifying a subset of scene classes shared among four different 3D cameras with different acquisition properties. Each camera is considered as an RGB-D domain.
Setup
- Download the complete SUNRGB-D dataset from this link.
- Use the proposed txt files in the "subsets" folder.
- To run the model, execute the following code:
python train.py- Configs DATA_DIR_TRAIN, DATA_DIR_TRAIN_2 and DATA_DIR_VAL must be changed according to the domains
- With Realsense, FILTER_BEDROOM must be set to True and NUM_CLASSES to 9
Requirements
- Cuda 10.1
- Python 3.7.7
- Torch 1.0.0
- Torchvision 0.2.1
- Other Python Requirements in requirements.txt
Acknowledgement
Code in this repository has been written starting from Translate-to-Recognize
Citation
To cite, please use the following reference:
@inproceedings{FerreriBucciTommasi2021,
title={Multi-Modal RGB-D Scene Recognition Across Domains},
author={Andrea Ferreri, Silvia Bucci, Tatiana Tommasi},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
year={2021}
}