Contrast and Mix (CoMix)
The repository contains the codes for the paper Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing part of Advances in Neural Information Processing Systems (NeurIPS) 2021.
Aadarsh Sahoo1, Rutav Shah1, Rameswar Panda2, Kate Saenko2,3, Abir Das1
1 IIT Kharagpur, 2 MIT-IBM Watson AI Lab, 3 Boston University
Fig. Temporal Contrastive Learning with Background Mixing and Target Pseudo-labels. Temporal contrastive loss (left) contrasts a single temporally augmented positive (same video, different speed) per anchor against rest of the videos in a mini-batch as negatives. Incorporating background mixing (middle) provides additional positives per anchor possessing same action semantics with a different background alleviating background shift across domains. Incorporating target pseudo-labels (right) additionally enhances the discriminabilty by contrasting the target videos with the same pseudo-label as positives against rest of the videos as negatives.
Preparing the Environment
Conda
Please use the comix_environment.yml file to create the conda environment comix as:
conda env create -f comix_environment.yml
Pip
Please use the requirements.txt file to install all the required dependencies as:
pip install -r requirements.txt
Data Directory Structure
All the datasets should be stored in the folder ./data following the convention ./data/
and it must be passed as an argument to base_dir=./data/
.
UCF - HMDB
For ucf_hmdb dataset with base_dir=./data/ucf_hmdb the structure would be as follows:
.
├── ...
├── data
│ ├── ucf_hmdb
│ │ ├── ucf_videos
| | | ├──
| | | | ├──
| | | | ├──
| | | | ├── ...
| | | ├──
| | | ├── ...
│ │ ├── hmdb_videos
| | ├── ucf_BG
| | └── hmdb_BG
│ └──
└──
Jester
For Jester dataset with base_dir=./data/jester the structure would be as follows
.
├── ...
├── data
│ ├── jester
| | ├── jester_videos
| | | ├──
| | | | ├──
| | | | ├──
| | | | ├── ...
| | | ├──
| | | ├── ...
| | ├── jester_BG
| | | ├──
| | | | ├──
| | | ├── ... └── └── └──
Epic-Kitchens
For Epic Kitchens dataset with base_dir=./data/epic_kitchens the structure would be as follows (we follow the same structure as in the original dataset) :
.
├── ...
├── data
│ ├── epic_kitchens
| | ├── epic_kitchens_videos
| | | ├── train
| | | | ├── D1
| | | | | ├──
| | | | | | ├──
| | | | | | ├──
| | | | | | ├── ...
| | | | | ├──
| | | | | ├── ...
| | | | ├── D2
| | | | └── D3
| | | └── test
└── └── └── epic_kitchens_BG
For using datasets stored in some other directories, please pass the parameter base_dir accordingly.
Background Extraction using Temporal Median Filtering
Please refer to the folder ./background_extraction for the codes to extract backgrounds using temporal median filtering.
Data
All the required split files are provided inside the directory ./video_splits.
The official download links for the datasets used for this paper are: [UCF-101] [HMDB-51] [Jester] [Epic Kitchens]
Training CoMix
Here are some of the sample and recomended commands to train CoMix for the transfer task of:
UCF -> HMDB from UCF-HMDB dataset:
CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --manual_seed 1 --dataset_name UCF-HMDB --src_dataset UCF --tgt_dataset HMDB --batch_size 8 --model_root ./checkpoints_ucf_hmdb --save_in_steps 500 --log_in_steps 50 --eval_in_steps 50 --pseudo_threshold 0.7 --warmstart_models True --num_iter_warmstart 4000 --num_iter_adapt 10000 --learning_rate 0.01 --learning_rate_ws 0.01 --lambda_bgm 0.1 --lambda_tpl 0.01 --base_dir ./data/ucf_hmdb
S -> T from Jester dataset:
CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --manual_seed 1 --dataset_name Jester --src_dataset S --tgt_dataset T --batch_size 8 --model_root ./checkpoints_jester --save_in_steps 500 --log_in_steps 50 --eval_in_steps 50 --pseudo_threshold 0.7 --warmstart_models True --num_iter_warmstart 4000 --num_iter_adapt 10000 --learning_rate 0.01 --learning_rate_ws 0.01 --lambda_bgm 0.1 --lambda_tpl 0.1 --base_dir ./data/jester
D1 -> D2 from Epic-Kitchens dataset:
CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --manual_seed 1 --dataset_name Epic-Kitchens --src_dataset D1 --tgt_dataset D2 --batch_size 8 --model_root ./checkpoints_epic_d1_d2 --save_in_steps 500 --log_in_steps 50 --eval_in_steps 50 --pseudo_threshold 0.7 --warmstart_models True --num_iter_warmstart 4000 --num_iter_adapt 10000 --learning_rate 0.01 --learning_rate_ws 0.01 --lambda_bgm 0.01 --lambda_tpl 0.01 --base_dir ./data/epic_kitchens
For detailed description regarding the arguments, use:
python main.py --help
Citing CoMix
If you use codes in this repository, consider citing CoMix. Thanks!
@article{sahoo2021contrast,
title={Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing},
author={Sahoo, Aadarsh and Shah, Rutav and Panda, Rameswar and Saenko, Kate and Das, Abir},
journal={Advances in Neural Information Processing Systems},
volume={34},
year={2021}
}
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