MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding

• URL: http://arxiv.org/abs/2010.05379v1
• Date: Mon, 12 Oct 2020 00:43:52 GMT
• Title: MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding
• Authors: Qinxin Wang, Hao Tan, Sheng Shen, Michael W. Mahoney, Zhewei Yao
• Abstract summary: We present algorithms to model phrase-object relevance by leveraging fine-grained visual representations and visually-aware language representations. Experiments conducted on the widely-adopted Flickr30k dataset show a significant improvement over existing weakly-supervised methods.
• Score: 74.33171794972688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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