LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption Generation

Abstract

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

15 pages, 10 Figures, 5 Tables. Accepted for Oral Presentation at Irish Machine Vision and Image Processing Conference Proceedings (IMVIP), 2024

References in corpus (5)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Conditional Generative Adversarial Nets
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Generative Adversarial Text to Image Synthesis
• Large-Scale Adversarial Training for Vision-and-Language Representation Learning