ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning

• URL: http://arxiv.org/abs/2306.00103v1
• Date: Wed, 31 May 2023 18:23:57 GMT
• Title: ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning
• Authors: Xiao Xu, Bei Li, Chenfei Wu, Shao-Yen Tseng, Anahita Bhiwandiwalla, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan
• Abstract summary: Two-Tower Vision-Language (VL) models have shown promising improvements on various downstream tasks. We propose ManagerTower, a novel VL model architecture that gathers and combines the insights of pre-trained uni-modal experts at different levels.
• Score: 73.47165576175541
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/LooperXX/ManagerTower.

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