A Novel Attention-based Aggregation Function to Combine Vision and Language

Attention-based Aggregation Function

Abstract

The joint understanding of vision and language has been recently gaining a lot of attention in both the Computer Vision and Natural Language Processing communities, with the emergence of tasks such as image captioning, image-text matching, and visual question answering. As both images and text can be encoded as sets or sequences of elements -like regions and words- proper reduction functions are needed to transform a set of encoded elements into a single response, like a classification or similarity score. In this paper, we propose a novel fully-attentive reduction method for vision and language. Specifically, our approach computes a set of scores for each element of each modality employing a novel variant of cross-attention, and performs a learnable and cross-modal reduction, which can be used for both classification and ranking. We test our approach on image-text matching and visual question answering, building fair comparisons with other reduction choices, on both COCO and VQA 2.0 datasets. Experimentally, we demonstrate that our approach leads to a performance increase on both tasks. Further, we conduct ablation studies to validate the role of each component of the approach.

Publication
International Conference on Pattern Recognition - ICPR 2020

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Model in details:

pipeline

Full Paper: pdf

Please cite with the following BibTeX:

@article{stefanini2020novel,
  title={A Novel Attention-based Aggregation Function to Combine Vision and Language},
  author={Stefanini, Matteo and Cornia, Marcella and Baraldi, Lorenzo and Cucchiara, Rita},
  journal={arXiv preprint arXiv:2004.13073},
  year={2020}
}
Matteo Stefanini
Matteo Stefanini
PhD Candidate in Artificial Intelligence - TEDx Organizer

I’m a (deep) learner who loves freedom. Working on Deep Learning, Vision and Language and interested in applying AI to Genomics and Biomedical fields. Driven to inspire people.