Similarity Reasoning and Filtration for Image-Text Matching
Keywords:Language and Vision, Multimodal Learning, Visual Reasoning & Symbolic Representations, Graph-based Machine Learning
AbstractImage-text matching plays a critical role in bridging the vision and language, and great progress has been made by exploiting the global alignment between image and sentence, or local alignments between regions and words. However, how to make the most of these alignments to infer more accurate matching scores is still underexplored. In this paper, we propose a novel Similarity Graph Reasoning and Attention Filtration (SGRAF) network for image-text matching. Specifically, the vector-based similarity representations are firstly learned to characterize the local and global alignments in a more comprehensive manner, and then the Similarity Graph Reasoning (SGR) module relying on one graph convolutional neural network is introduced to infer relation-aware similarities with both the local and global alignments. The Similarity Attention Filtration (SAF) module is further developed to integrate these alignments effectively by selectively attending on the significant and representative alignments and meanwhile casting aside the interferences of non-meaningful alignments. We demonstrate the superiority of the proposed method with achieving state-of-the-art performances on the Flickr30K and MSCOCO datasets, and the good interpretability of SGR and SAF with extensive qualitative experiments and analyses.
How to Cite
Diao, H., Zhang, Y., Ma, L., & Lu, H. (2021). Similarity Reasoning and Filtration for Image-Text Matching. Proceedings of the AAAI Conference on Artificial Intelligence, 35(2), 1218-1226. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16209
AAAI Technical Track on Computer Vision I