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arXiv · 2104.02605

An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-Level Structural Information

Zejun Li·Zhongyu Wei·Zhihao Fan·Haijun Shan·Xuanjing Huang
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Abstract

In this paper, we focus on the problem of unsupervised image-sentence matching. Existing research explores to utilize document-level structural information to sample positive and negative instances for model training. Although the approach achieves positive results, it introduces a sampling bias and fails to distinguish instances with high semantic similarity. To alleviate the bias, we propose a new sampling strategy to select additional intra-document image-sentence pairs as positive or negative samples. Furthermore, to recognize the complex pattern in intra-document samples, we propose a Transformer based model to capture fine-grained features and implicitly construct a graph for each document, where concepts in a document are introduced to bridge the representation learning of images and sentences in the context of a document. Experimental results show the effectiveness of our approach to alleviate the bias and learn well-aligned multimodal representations.

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Zejun Li, Zhongyu Wei, Zhihao Fan, Haijun Shan, Xuanjing Huang. 2021-03-21. An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-Level Structural Information. https://arxiv.org/abs/2104.02605

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