Nov 17, 2017 · Our framework can well match images and sentences with complex content, and achieve the state-of-the-art cross-modal retrieval results on MSCOCO dataset.
This work proposes to incorporate generative processes into the cross-modal feature embedding, through which it is able to learn not only the global ...
Our framework can well match images and sentences with complex content, and achieve the state-of-the-art cross-modal retrieval results on MSCOCO dataset.
Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models (CVPR 2018 Spotlight). Presenter: Yongxin (Richard) Wang. Page ...
Nov 17, 2017 · Textual-visual cross-modal retrieval has been a hot research topic in both computer vision and natural language processing communities.
Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with. Generative Models. Jiuxiang Gu Jianfei Cai Shafiq Joty. Li Niu. Gang Wang. Page 2 ...
Extensive experiments show that our framework can well match images and sentences with complex content, and achieve the state-of-the-art cross-modal retrieval ...
[8] proposed a method to improve cross-modal retrieval using generative modeling, which constructs textual representations as visual features and then ...
Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models. Jiuxiang Gu, Jianfei Cai, Shafiq Joty, Li Niu, Gang Wang.
People also ask
What is the difference between generative model and retrieval model?
What do generative models learn?
Li Niu. Latest. Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models (Proceedings of the IEEE Conference on ...