ManagerTower: Aggregating the Insights of Uni-Modal Experts for
  Vision-Language Representation Learning
Xiao Xu Bei Li Chenfei Wu Shao-Yen Tseng Anahita Bhiwandiwalla Shachar Rosenman Vasudev Lal Wanxiang Che Nan Duan
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 https://github.com/LooperXX/ManagerTower.