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

• URL: http://arxiv.org/abs/2004.13073v2
• Date: Mon, 13 Jul 2020 12:22:38 GMT
• Title: A Novel Attention-based Aggregation Function to Combine Vision and Language
• Authors: Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
• Abstract summary: 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. We test our approach on image-text matching and visual question answering, building fair comparisons with other reduction choices.
• Score: 55.7633883960205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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