Finding Beans in Burgers: Deep Semantic-Visual Embedding With Localization

Martin Engilberge, Louis Chevallier, Patrick Pérez, Matthieu Cord; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 3984-3993

Abstract


Several works have proposed to learn a two-path neural network that maps images and texts, respectively, to a same shared Euclidean space where geometry captures useful semantic relationships. Such a multi-modal embedding can be trained and used for various tasks, notably image captioning. In the present work, we introduce a new architecture of this type, with a visual path that leverages recent space-aware pooling mechanisms. Combined with a textual path which is jointly trained from scratch, our semantic-visual embedding offers a versatile model. Once trained under the supervision of captioned images, it yields new state-of-the-art performance on cross-modal retrieval. It also allows the localization of new concepts from the embedding space into any input image, delivering state-of-the-art result on the visual grounding of phrases.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Engilberge_2018_CVPR,
author = {Engilberge, Martin and Chevallier, Louis and Pérez, Patrick and Cord, Matthieu},
title = {Finding Beans in Burgers: Deep Semantic-Visual Embedding With Localization},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}