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

TL;DR

LAViTeR is a novel model that enhances visual and textual representations by integrating image synthesis and captioning tasks, leading to better alignment in joint embeddings for vision-language applications.

Contribution

The paper introduces LAViTeR, which combines alignment with auxiliary GAN-based image synthesis and captioning tasks for improved cross-modal representation learning.

Findings

Superior alignment on CUB and MS-COCO datasets

Effective joint embedding of visual and textual features

Enhanced performance in downstream vision-language tasks

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