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

• URL: http://arxiv.org/abs/2109.04993v4
• Date: Tue, 01 Oct 2024 16:54:57 GMT
• Title: LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption Generation
• Authors: Mohammad Abuzar Hashemi, Zhanghexuan Li, Mihir Chauhan, Yan Shen, Abhishek Satbhai, Mir Basheer Ali, Mingchen Gao, Sargur Srihari,
• Abstract summary: 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. The experimental results on two public datasets, CUB and MS-COCO, demonstrate superior visual and textual representation alignment.
• Score: 5.064384692591668
• License:
• 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

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