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    <abstract>Phrase localization is a task that studies the mapping from textual phrases to regions of an image. Given difficulties in annotating phrase-to-object datasets at scale, we develop a Multimodal Alignment Framework (MAF) to leverage more widely-available caption-image datasets, which can then be used as a form of weak supervision. We first present algorithms to model phrase-object relevance by leveraging fine-grained visual representations and visually-aware language representations. By adopting a contrastive objective, our method uses information in caption-image pairs to boost the performance in weakly-supervised scenarios. Experiments conducted on the widely-adopted Flickr30k dataset show a significant improvement over existing weakly-supervised methods. With the help of the visually-aware language representations, we can also improve the previous best unsupervised result by 5.56%. We conduct ablation studies to show that both our novel model and our weakly-supervised strategies significantly contribute to our strong results.</abstract>
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%0 Conference Proceedings
%T MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding
%A Wang, Qinxin
%A Tan, Hao
%A Shen, Sheng
%A Mahoney, Michael
%A Yao, Zhewei
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-maf
%X Phrase localization is a task that studies the mapping from textual phrases to regions of an image. Given difficulties in annotating phrase-to-object datasets at scale, we develop a Multimodal Alignment Framework (MAF) to leverage more widely-available caption-image datasets, which can then be used as a form of weak supervision. We first present algorithms to model phrase-object relevance by leveraging fine-grained visual representations and visually-aware language representations. By adopting a contrastive objective, our method uses information in caption-image pairs to boost the performance in weakly-supervised scenarios. Experiments conducted on the widely-adopted Flickr30k dataset show a significant improvement over existing weakly-supervised methods. With the help of the visually-aware language representations, we can also improve the previous best unsupervised result by 5.56%. We conduct ablation studies to show that both our novel model and our weakly-supervised strategies significantly contribute to our strong results.
%R 10.18653/v1/2020.emnlp-main.159
%U https://aclanthology.org/2020.emnlp-main.159
%U https://doi.org/10.18653/v1/2020.emnlp-main.159
%P 2030-2038

Markdown (Informal)

[MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding](https://aclanthology.org/2020.emnlp-main.159) (Wang et al., EMNLP 2020)

• MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding (Wang et al., EMNLP 2020)

ACL

• Qinxin Wang, Hao Tan, Sheng Shen, Michael Mahoney, and Zhewei Yao. 2020. MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2030–2038, Online. Association for Computational Linguistics.