BridgeTower

This repo is the official Pytorch implementation of "BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning".

Updates

• Feb. 2023: BridgeTower was integrated into Hugging Face - Transformers.
  • Model Hub, Code and Documentation are available.
  • Thanks to Anahita Bhiwandiwalla, Tiep Le and Shaoyen Tseng from Intel Labs for their great work!
• Nov. 2022: BridgeTower got accepted by AAAI'23. Code and checkpoints are released.
• Jun. 2022: We released the preprint version in Arxiv.
• May. 2022: BridgeTower (single model, 4M data) achieved 78.73% and 81.15% (base and large) on the VQAv2 Challenge test-std set.

Abstract

Vision-Language (VL) models with the Two-Tower architecture have dominated visual-language representation learning in recent years. Current VL models either use lightweight uni-modal encoders and learn to extract, align and fuse both modalities simultaneously in a deep cross-modal encoder, or feed the last-layer uni-modal representations from the deep pre-trained uni-modal encoders into the top cross-modal encoder. Both approaches potentially restrict vision-language representation learning and limit model performance. In this paper, we propose BridgeTower, which introduces multiple bridge layers that build a connection between the top layers of uni-modal encoders and each layer of the cross-modal encoder. This enables effective bottom-up cross-modal alignment and fusion between visual and textual representations of different semantic levels of pre-trained uni-modal encoders in the cross-modal encoder. Pre-trained with only 4M images, BridgeTower achieves state-of-the-art performance on various downstream vision-language tasks. In particular, on the VQAv2 test-std set, BridgeTower achieves an accuracy of 78.73%, outperforming the previous state-of-the-art model METER by 1.09% with the same pre-training data and almost negligible additional parameters and computational costs. Notably, when further scaling the model, BridgeTower achieves an accuracy of 81.15%, surpassing models that are pre-trained on orders-of-magnitude larger datasets.

Architecture

Main Results

Deployment

• Run setup.sh to set up the environment.
• [Optional] We use wandb to track experiments! Please remember to wandb login and paste your token before running the script.

Dataset Preparation

• We follow ViLT and use pyarrow to serialize the datasets. See here for details.
• For SNLI-VE dataset, we follow here.
• For VG-QA dataset, except the image-text pairs in VG got from here, image meta data, question answers data and coco split information also need to be downloaded.
• The final file structure of datasets are shown in setup.sh.

Checkpoints

• Pre-trained checkpoints on 4M data: BASE and LARGE

• Fine-tuned checkpoints for

  • Visual Question Answering on VQAv2: BASE, BASE(w/ VGQA), LARGE, LARGE(w/ VGQA)
  • Image-Text Retrieval on Flickr30k: BASE
  • Visual Entailment on SNLI-VE: BASE
  • Visual Reasoning on NLVR$^2$: BASE
  • Image-Text Retrieval on MSCOCO: BASE

• Here is an example for downloading a checkpoint.

  # download azcopy
  wget https://aka.ms/downloadazcopy-v10-linux
  tar -xvf downloadazcopy-v10-linux
  sudo cp ./azcopy_linux_amd64_*/azcopy /usr/bin/
  sudo chmod -R 777 /usr/bin/azcopy
  # azcopy copy [remote path] [local path]
  azcopy copy "https://chenfei.blob.core.windows.net/data/G/LCI/best_checkpoints/BridgeTower_pt_base.ckpt?sv=2020-10-02&st=2022-11-24T12%3A18%3A49Z&se=2027-11-25T12%3A18%3A00Z&sr=b&sp=r&sig=BJigddAMHfNUtQuTGH8bJUrzAO3LfaeSm48AXUqZngY%3D" "./BridgeTower_pt_base.ckpt"
  

Pre-training on Image-Text Datasets

# Pre-train BridgeTower Base Model
bash scripts/pre_train.sh
# Pre-train BridgeTower Large Model
bash scripts/pre_train_large.sh

Fine-tuning on Downstream VL Tasks

• VQAv2 Evaluation needs to submit the json file in the logs/ directory to eval.ai evaluation server to get the test-dev and/or test-std scores.

# Base Model on VQAv2 without VLP
bash scripts/ftfs_base_vqa.sh

# Large Model on VQAv2 without VLP
bash scripts/ftfs_large_vqa.sh

# Base Model on VQAv2 with VLP
bash scripts/ftfpt_base_vqa.sh

# Large Model on VQAv2 with VLP
bash scripts/ftfpt_large_vqa.sh

# Base Model on IRTR-Flickr30K with VLP (directly use ITM with multiple false texts)
bash scripts/ftfpt_base_irtr_f30k.sh

# Base Model on IRTR-Flickr30K with VLP (follow ALBEF to use ITC to sample hard negatives for ITM)
bash scripts/ftfpt_base_irtr_itm_itc_f30k.sh

# Base Model on SNLI-VE with VLP
bash scripts/ftfpt_base_snlive.sh

# Base Model on NLVR^2 with VLP
bash scripts/ftfpt_base_nlvr2.sh

# Base Model on IRTR-MSCOCO with VLP (follow ALBEF to use ITC to sample hard negatives for ITM)
bash scripts/ftfpt_base_irtr_itm_itc_coco.sh

Fine-tuning on Uni-Modal Tasks

# Base Model on CIFAR with VLP
bash scripts/ftfpt_base_cifar.sh

# Base Model on GLUE with VLP
bash scripts/ftfpt_base_glue.sh

Citation

@article{xu2022bridge,
  title={BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning},
  author={Xu, Xiao and Wu, Chenfei and Rosenman, Shachar and Lal, Vasudev and Che, Wanxiang and Duan, Nan},
  journal={arXiv preprint arXiv:2206.08657},
  year={2022}
}

Acknowledgement

We are highly grateful for the public code of the following papers, our code is partly based on them:

• Main Code: METER, ViLT
• Others: CLIP, ALBEF, BLIP