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arXiv · 2106.01804

E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning

Haiyang Xu·Ming Yan·Chenliang Li·Bin Bi·Songfang Huang·Wenming Xiao·Fei Huang
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Abstract

Vision-language pre-training (VLP) on large-scale image-text pairs has achieved huge success for the cross-modal downstream tasks. The most existing pre-training methods mainly adopt a two-step training procedure, which firstly employs a pre-trained object detector to extract region-based visual features, then concatenates the image representation and text embedding as the input of Transformer to train. However, these methods face problems of using task-specific visual representation of the specific object detector for generic cross-modal understanding, and the computation inefficiency of two-stage pipeline. In this paper, we propose the first end-to-end vision-language pre-trained model for both V+L understanding and generation, namely E2E-VLP, where we build a unified Transformer framework to jointly learn visual representation, and semantic alignments between image and text. We incorporate the tasks of object detection and image captioning into pre-training with a unified Transformer encoder-decoder architecture for enhancing visual learning. An extensive set of experiments have been conducted on well-established vision-language downstream tasks to demonstrate the effectiveness of this novel VLP paradigm.

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BibTeXRIS

Haiyang Xu, Ming Yan, Chenliang Li, Bin Bi, Songfang Huang, Wenming Xiao, Fei Huang. 2021-06-03. E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning. https://arxiv.org/abs/2106.01804

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