@inproceedings{xu-etal-2023-managertower,
    title = "{M}anager{T}ower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning",
    author = "Xu, Xiao  and
      Li, Bei  and
      Wu, Chenfei  and
      Tseng, Shao-Yen  and
      Bhiwandiwalla, Anahita  and
      Rosenman, Shachar  and
      Lal, Vasudev  and
      Che, Wanxiang  and
      Duan, Nan",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.811",
    doi = "10.18653/v1/2023.acl-long.811",
    pages = "14507--14525",
    abstract = "Two-Tower Vision-Language (VL) models have shown promising improvements on various downstream VL tasks. Although the most advanced work improves performance by building bridges between encoders, it suffers from ineffective layer-by-layer utilization of uni-modal representations and cannot flexibly exploit different levels of uni-modal semantic knowledge. In this work, we propose ManagerTower, a novel VL model architecture that gathers and combines the insights of pre-trained uni-modal experts at different levels. The managers introduced in each cross-modal layer can adaptively aggregate uni-modal semantic knowledge to facilitate more comprehensive cross-modal alignment and fusion. ManagerTower outperforms previous strong baselines both with and without Vision-Language Pre-training (VLP). With only 4M VLP data, ManagerTower achieves superior performances on various downstream VL tasks, especially 79.15{\%} accuracy on VQAv2 Test-Std, 86.56{\%} IR@1 and 95.64{\%} TR@1 on Flickr30K. Code and checkpoints are available at \url{https://github.com/LooperXX/ManagerTower}.",
}

Two-Tower Vision-Language (VL) models have shown promising improvements on various downstream VL tasks. Although the most advanced work improves performance by building bridges between encoders, it suffers from ineffective layer-by-layer utilization of uni-modal representations and cannot flexibly exploit different levels of uni-modal semantic knowledge. In this work, we propose ManagerTower, a novel VL model architecture that gathers and combines the insights of pre-trained uni-modal experts at different levels. The managers introduced in each cross-modal layer can adaptively aggregate uni-modal semantic knowledge to facilitate more comprehensive cross-modal alignment and fusion. ManagerTower outperforms previous strong baselines both with and without Vision-Language Pre-training (VLP). With only 4M VLP data, ManagerTower achieves superior performances on various downstream VL tasks, especially 79.15{\%} accuracy on VQAv2 Test-Std, 86.56{\%} IR@1 and 95.64{\%} TR@1 on Flickr30K. Code and checkpoints are available at \url{https://github.com/LooperXX/ManagerTower}.
Xu, Xiao Li, Bei Wu, Chenfei Tseng, Shao-Yen Bhiw iwalla, Anahita Rosenman, Shachar Lal, Vasudev Che, Wanxiang Duan, Nan
ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning