Visual Semantic Reasoning For Image-text Matching
2019 · Kunpeng Li, Yulun Zhang, Kai Li, et al.
Abstract
Image-text matching has been a hot research topic bridging the vision and language areas. It remains challenging because the current representation of image usually lacks global semantic concepts as in its corresponding text caption. To address this issue, we propose a simple and interpretable reasoning model to generate visual representation that captures key objects and semantic concepts of a scene. Specifically, we first build up connections between image regions and perform reasoning with Graph Convolutional Networks to generate features with semantic relationships. Then, we propose to use the gate and memory mechanism to perform global semantic reasoning on these relationship-enhanced features, select the discriminative information and gradually generate the representation for the whole scene. Experiments validate that our method achieves a new state-of-the-art for the image-text matching on MS-COCO and Flickr30K datasets. It outperforms the current best method by 6.8% relatively
Authors
(none)
Tags
Stats
Related papers
- Multi-modal Reasoning Graph For Scene-text Based Fine-grained Image Classification And Retrieval (2020)11.29
- Image-text Retrieval Via Preserving Main Semantics Of Vision (2023)10.22
- A Deep Local And Global Scene-graph Matching For Image-text Retrieval (2021)10.74
- Beyond Visual Semantics: Exploring The Role Of Scene Text In Image Understanding (2019)9.59
- Look, Imagine And Match: Improving Textual-visual Cross-modal Retrieval With Generative Models (2017)18.52
- Scene Graph Based Fusion Network For Image-text Retrieval (2023)4.52
- Stacked Cross Attention For Image-text Matching (2018)27.77
- Transformer Reasoning Network For Image-text Matching And Retrieval (2020)16.15