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

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.


Citation:

Stefanini, Matteo; Cornia, Marcella; Baraldi, Lorenzo; Cucchiara, Rita "A Novel Attention-based Aggregation Function to Combine Vision and Language" Proceedings of the 25th International Conference on Pattern Recognition, Milan, Italy, pp. 1212 -1219 , 10-15 January 2021, 2020 DOI: 10.1109/ICPR48806.2021.9413269

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