An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-level Structural Information
Keywords:Language Grounding & Multi-modal NLP
AbstractIn 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.
How to Cite
Li, Z., Wei, Z., Fan, Z., Shan, H., & Huang, X. (2021). An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-level Structural Information. Proceedings of the AAAI Conference on Artificial Intelligence, 35(15), 13324-13332. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17573
AAAI Technical Track on Speech and Natural Language Processing II