【CVPR'2023 Highlight🔥】Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning
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The implementation of CVPR 2023 Highlight (Top 10%) paper Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning.
In this paper, we creatively model video-text as game players with multivariate cooperative game theory to wisely handle the uncertainty during fine-grained semantic interaction with diverse granularity, flexible combination, and vague intensity.
If you find this paper useful, please consider staring 🌟 this repo and citing 📑 our paper:
@inproceedings{jin2023video,
title={Video-text as game players: Hierarchical banzhaf interaction for cross-modal representation learning},
author={Jin, Peng and Huang, Jinfa and Xiong, Pengfei and Tian, Shangxuan and Liu, Chang and Ji, Xiangyang and Yuan, Li and Chen, Jie},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2472--2482},
year={2023}
}
💡 I also have other text-video retrieval projects that may interest you ✨.
DiffusionRet: Generative Text-Video Retrieval with Diffusion Model
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- [2023/10/15]: We release our pre-trained estimator weights. If you want to apply a to other tasks, you can initialize a new estimator with the weights we provide. If you want better performance, you can train the estimator with a smaller learning rate and more epochs.
- [2023/10/11]: We release code for Banzhaf Interaction estimator. Recommended running parameters will be provided shortly, and we will also release our pre-trained estimator weights.
- [2023/10/08]: I am working on the code for Banzhaf Interaction estimator, which is expected to be released soon.
- [2023/06/28]: Release code for reimplementing the experiments in the paper.
- [2023/03/28]: Our HBI has been selected as a Highlight paper at CVPR 2023! (Top 2.5% of 9155 submissions).
- [2023/02/28]: We will release the code asap. (I am busy with other DDLs. After that, I will open the source code as soon as possible. Please understand.)
demo_github.mp4
conda create -n HBI python=3.9
conda activate HBI
pip install -r requirements.txt
pip install torch==1.8.1+cu102 torchvision==0.9.1+cu102 -f https://download.pytorch.org/whl/torch_stable.htmlcd HBI/models
wget https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt
# wget https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt
# wget https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt| Datasets | Google Cloud | Baidu Yun | Peking University Yun |
|---|---|---|---|
| MSR-VTT | Download | Download | Download |
| MSVD | Download | Download | Download |
| ActivityNet | TODO | Download | Download |
| DiDeMo | TODO | Download | Download |
Train the estimator according to the label generated by the BanzhafInteraction in HBI/models/banzhaf.py.
The training code is provided in banzhaf_estimator.py. We provide our trained weights, and if you want to apply a to other tasks, you can initialize a new estimator with the weights we provide.
We have tested the performance of Estimator_1e-2_epoch6 with R@1 of 48.2 (log) on the MSR-VTT dataset. If you want better performance, you can train the estimator with a smaller learning rate and more epochs.
| Models | Google Cloud | Baidu Yun | Peking University Yun | log |
|---|---|---|---|---|
| Estimator_1e-2_epoch1 | Download | Download | Download | log |
| Estimator_1e-2_epoch2 | Download | Download | Download | log |
| Estimator_1e-2_epoch3 | Download | Download | Download | log |
| Estimator_1e-2_epoch4 | Download | Download | Download | log |
| Estimator_1e-2_epoch5 | Download | Download | Download | log |
| Estimator_1e-2_epoch6 | Download | Download | Download | log |
CUDA_VISIBLE_DEVICES=0,1,2,3 \
python -m torch.distributed.launch \
--master_port 2502 \
--nproc_per_node=4 \
banzhaf_estimator.py \
--do_train 1 \
--workers 8 \
--n_display 1 \
--epochs 10 \
--lr 1e-2 \
--coef_lr 1e-3 \
--batch_size 128 \
--batch_size_val 128 \
--anno_path data/MSR-VTT/anns \
--video_path ${DATA_PATH}/MSRVTT_Videos \
--datatype msrvtt \
--max_words 24 \
--max_frames 12 \
--video_framerate 1 \
--output_dir ${OUTPUT_PATH} | Checkpoint | Google Cloud | Baidu Yun | Peking University Yun |
|---|---|---|---|
| MSR-VTT | Download | Download | Download |
| ActivityNet | Download | Download | Download |
CUDA_VISIBLE_DEVICES=0,1 \
python -m torch.distributed.launch \
--master_port 2502 \
--nproc_per_node=2 \
main_retrieval.py \
--do_eval 1 \
--workers 8 \
--n_display 50 \
--batch_size_val 128 \
--anno_path data/MSR-VTT/anns \
--video_path ${DATA_PATH}/MSRVTT_Videos \
--datatype msrvtt \
--max_words 24 \
--max_frames 12 \
--video_framerate 1 \
--init_model ${CHECKPOINT_PATH} \
--output_dir ${OUTPUT_PATH} CUDA_VISIBLE_DEVICES=0,1 \
python -m torch.distributed.launch \
--master_port 2502 \
--nproc_per_node=2 \
main_retrieval.py \
--do_train 1 \
--workers 8 \
--n_display 50 \
--epochs 5 \
--lr 1e-4 \
--coef_lr 1e-3 \
--batch_size 128 \
--batch_size_val 128 \
--anno_path data/MSR-VTT/anns \
--video_path ${DATA_PATH}/MSRVTT_Videos \
--datatype msrvtt \
--max_words 24 \
--max_frames 12 \
--video_framerate 1 \
--estimator ${ESTIMATOR_PATH} \
--output_dir ${OUTPUT_PATH} \
--kl 2 \
--skl 1CUDA_VISIBLE_DEVICES=0,1 \
python -m torch.distributed.launch \
--master_port 2502 \
--nproc_per_node=2 \
main_retrieval.py \
--do_eval 1 \
--workers 8 \
--n_display 50 \
--batch_size_val 128 \
--anno_path ${DATA_PATH}/ActivityNet \
--video_path ${DATA_PATH}/ActivityNet/Activity_Videos \
--datatype activity \
--max_words 64 \
--max_frames 64 \
--video_framerate 1 \
--init_model ${CHECKPOINT_PATH} \
--output_dir ${OUTPUT_PATH} CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python -m torch.distributed.launch \
--master_port 2502 \
--nproc_per_node=8 \
main_retrieval.py \
--do_train 1 \
--workers 8 \
--n_display 10 \
--epochs 10 \
--lr 1e-4 \
--coef_lr 1e-3 \
--batch_size 128 \
--batch_size_val 128 \
--anno_path ${DATA_PATH}/ActivityNet \
--video_path ${DATA_PATH}/ActivityNet/Activity_Videos \
--datatype activity \
--max_words 64 \
--max_frames 64 \
--video_framerate 1 \
--estimator ${ESTIMATOR_PATH} \
--output_dir ${OUTPUT_PATH} \
--kl 2 \
--skl 1CUDA_VISIBLE_DEVICES=0,1 \
python -m torch.distributed.launch \
--master_port 2502 \
--nproc_per_node=2 \
main_vqa.py \
--do_eval \
--num_thread_reader=8 \
--train_csv data/MSR-VTT/qa/train.jsonl \
--val_csv data/MSR-VTT/qa/test.jsonl \
--data_path data/MSR-VTT/qa/train_ans2label.json \
--features_path ${DATA_PATH}/MSRVTT_Videos \
--max_words 32 \
--max_frames 12 \
--batch_size_val 16 \
--datatype msrvtt \
--expand_msrvtt_sentences \
--feature_framerate 1 \
--freeze_layer_num 0 \
--slice_framepos 2 \
--loose_type \
--linear_patch 2d \
--init_model ${CHECKPOINT_PATH} \
--output_dir ${OUTPUT_PATH}CUDA_VISIBLE_DEVICES=0,1 \
python -m torch.distributed.launch \
--master_port 2502 \
--nproc_per_node=2 \
main_vqa.py \
--do_train \
--num_thread_reader=8 \
--epochs=5 \
--batch_size=32 \
--n_display=50 \
--train_csv data/MSR-VTT/qa/train.jsonl \
--val_csv data/MSR-VTT/qa/test.jsonl \
--data_path data/MSR-VTT/qa/train_ans2label.json \
--features_path ${DATA_PATH}/MSRVTT_Videos \
--lr 1e-4 \
--max_words 32 \
--max_frames 12 \
--batch_size_val 16 \
--datatype msrvtt \
--expand_msrvtt_sentences \
--feature_framerate 1 \
--coef_lr 1e-3 \
--freeze_layer_num 0 \
--slice_framepos 2 \
--loose_type \
--linear_patch 2d \
--estimator ${ESTIMATOR_PATH} \
--output_dir ${OUTPUT_PATH} \
--kl 2 \
--skl 1Our code is based on EMCL, CLIP, CLIP4Clip and DRL. We sincerely appreciate for their contributions.
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