Reciprocal Attention Fusion for Visual Question Answering
Table 3: Ablation Study on VQAv2 val-set.
['Cat.', 'Methods', 'Val-set']
We perform an extensive ablation study of the proposed model on VQAv2 Goyal et al. This ablation study helps to better understand the contribution of different components of our model towards the overall performance on the VQA task. The objective of this ablation study is to show that when the language features are combined with image- grid and object level visual features, the accuracy of the high level visual reasoning task (i.e. VQA) increases in contrast to only combining language with image or object level features. We observe RAF-I achieve comparable performance in this category. In Category II, RAF-O model extracts only 36 object level features but outperforms the models in Category I. Anderson et al. When we combine image and object level features together in Category III, we observe that the best results are obtained. This proves our hypothesis that the questions relate to both objects, object parts and local attributes, which should be attended for jointly an improved VQA performance.