Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts

• URL: http://arxiv.org/abs/2111.08276v1
• Date: Tue, 16 Nov 2021 07:55:26 GMT
• Title: Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
• Authors: Yan Zeng, Xinsong Zhang, Hang Li
• Abstract summary: We argue that the use of object detection may not be suitable for vision language pre-training. This paper proposes a new method called X-VLM to perform multi-grained vision language pre-training'
• Score: 14.808701042367401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most existing methods in vision language pre-training rely on object-centric features extracted through object detection, and make fine-grained alignments between the extracted features and texts. We argue that the use of object detection may not be suitable for vision language pre-training. Instead, we point out that the task should be performed so that the regions of `visual concepts' mentioned in the texts are located in the images, and in the meantime alignments between texts and visual concepts are identified, where the alignments are in multi-granularity. This paper proposes a new method called X-VLM to perform `multi-grained vision language pre-training'. Experimental results show that X-VLM consistently outperforms state-of-the-art methods in many downstream vision language tasks.

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