Laviter: Learning Aligned Visual And Textual Representations Assisted By Image And Caption Generation

Shaikh Mohammad Abuzar, Ji Zhanghexuan, Moukheiber Dana, Shen Yan, Srihari Sargur, Gao Mingchen. Arxiv 2021

[Paper]    
Attention Mechanism Model Architecture Multimodal Models Pretraining Methods Training Techniques Transformer

Pre-training visual and textual representations from large-scale image-text pairs is becoming a standard approach for many downstream vision-language tasks. The transformer-based models learn inter and intra-modal attention through a list of self-supervised learning tasks. This paper proposes LAViTeR, a novel architecture for visual and textual representation learning. The main module, Visual Textual Alignment (VTA) will be assisted by two auxiliary tasks, GAN-based image synthesis and Image Captioning. We also propose a new evaluation metric measuring the similarity between the learnt visual and textual embedding. The experimental results on two public datasets, CUB and MS-COCO, demonstrate superior visual and textual representation alignment in the joint feature embedding space

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