Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning

• URL: http://arxiv.org/abs/2211.13437v2
• Date: Sun, 26 Mar 2023 13:59:36 GMT
• Title: Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning
• Authors: Yatai Ji, Rongcheng Tu, Jie Jiang, Weijie Kong, Chengfei Cai, Wenzhe Zhao, Hongfa Wang, Yujiu Yang, Wei Liu
• Abstract summary: Cross-modal alignment is essential for vision-language pre-training models. We propose a novel Semantic Completion Learning task to facilitate global-to-local alignment. We also present a flexible vision encoder, which enables our model to perform image-text and video-text multimodal tasks simultaneously.
• Score: 22.464424641734652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks have been proposed for VLP to further promote cross-modal interactions. The core idea of previous masked modeling tasks is to focus on reconstructing the masked tokens based on visible context for learning local-to-local alignment. However, most of them pay little attention to the global semantic features generated for the masked data, resulting in a limited cross-modal alignment ability of global representations. Therefore, in this paper, we propose a novel Semantic Completion Learning (SCL) task, complementary to existing masked modeling tasks, to facilitate global-to-local alignment. Specifically, the SCL task complements the missing semantics of masked data by capturing the corresponding information from the other modality, promoting learning more representative global features which have a great impact on the performance of downstream tasks. Moreover, we present a flexible vision encoder, which enables our model to perform image-text and video-text multimodal tasks simultaneously. Experimental results show that our proposed method obtains state-of-the-art performance on various vision-language benchmarks, such as visual question answering, image-text retrieval, and video-text retrieval.

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