Matching images and text with multi-modal tensor fusion and re-ranking

Matching images and text with multi-modal tensor fusion and re-ranking

Wang, Tan (University of Electronic Science and Technology of China)
Hanjalic, A. (TU Delft Intelligent Systems)
Xu, Xing (University of Electronic Science and Technology of China)
Shen, Heng Tao (University of Electronic Science and Technology of China)
Yang, Yang (University of Electronic Science and Technology of China)
Song, Jingkuan (University of Electronic Science and Technology of China)

Intelligent Systems

2019-10-15

A major challenge in matching images and text is that they have intrinsically different data distributions and feature representations. Most existing approaches are based either on embedding or classification, the first one mapping image and text instances into a common embedding space for distance measuring, and the second one regarding image-text matching as a binary classification problem. Neither of these approaches can, however, balance the matching accuracy and model complexity well. We propose a novel framework that achieves remarkable matching performance with acceptable model complexity. Specifically, in the training stage, we propose a novel Multi-modal Tensor Fusion Network (MTFN) to explicitly learn an accurate image-text similarity function with rank-based tensor fusion rather than seeking a common embedding space for each image-text instance. Then, during testing, we deploy a generic Cross-modal Re-ranking (RR) scheme for refinement without requiring additional training procedure. Extensive experiments on two datasets demonstrate that our MTFN-RR consistently achieves the state-of-the-art matching performance with much less time complexity.

Cross-modal re-ranking
Image-text matching
Tensor fusion

http://resolver.tudelft.nl/uuid:9f197186-0b19-4057-812c-4ef1db17c5db

https://doi.org/10.1145/3343031.3350875

Association for Computing Machinery (ACM)

9781450368896

MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia

27th ACM International Conference on Multimedia, MM 2019, 2019-10-21 → 2019-10-25, Nice, France

Accepted author manuscript

Institutional Repository

conference paper

© 2019 Tan Wang, A. Hanjalic, Xing Xu, Heng Tao Shen, Yang Yang, Jingkuan Song