Previous existing visual question answering (VQA) systems commonly use graph neural networks(GNNs) to extract visual relationships such as semantic relations or spatial relations. However, studies that use GNNs typically ignore the importance of each
relation and simply concatenate outputs from multiple relation encoders. In this paper, we propose a novel layer architecture that fuses multiple visual relations through an attention mechanism to address this issue. Specifically, we develop a model that uses question embedding and joint embedding of the encoders to obtain dynamic attention weights with regard to the type of questions. Using the learnable attention weights, the proposed model can efficiently use the necessary visual relation features for a given question. Experimental results on the VQA 2.0 dataset demonstrate that the proposed model outperforms existing graph attention network-based architectures. Additionally, we visualize the attention weight and show that the proposed model assigns a higher weight to relations that are more relevant to the question.
This study sheds light on the effects of COVID-19 in the particular field of Computational Linguistics and Natural Language Processing within Artificial Intelligence. We provide an inter-sectional study on gender, contribution, and experience that co
nsiders one school year (from August 2019 to August 2020) as a pandemic year. August is included twice for the purpose of an inter-annual comparison. While the trend in publications increased with the crisis, the results show that the ratio between female and male publications decreased. This only helps to reduce the importance of the female role in the scientific contributions of computational linguistics (it is now far below its peak of 0.24). The pandemic has a particularly negative effect on the production of female senior researchers in the first position of authors (maximum work), followed by the female junior researchers in the last position of authors (supervision or collaborative work).
