An experimental study of the vision-bottleneck in VQA
This work addresses a specific bottleneck in VQA for researchers, but it is incremental as it focuses on optimizing existing components rather than introducing new paradigms.
The paper tackles the vision-bottleneck in Visual Question Answering (VQA) by experimentally studying the quantity and quality of visual objects and their integration methods, highlighting the importance of tailored vision approaches for the task.
As in many tasks combining vision and language, both modalities play a crucial role in Visual Question Answering (VQA). To properly solve the task, a given model should both understand the content of the proposed image and the nature of the question. While the fusion between modalities, which is another obviously important part of the problem, has been highly studied, the vision part has received less attention in recent work. Current state-of-the-art methods for VQA mainly rely on off-the-shelf object detectors delivering a set of object bounding boxes and embeddings, which are then combined with question word embeddings through a reasoning module. In this paper, we propose an in-depth study of the vision-bottleneck in VQA, experimenting with both the quantity and quality of visual objects extracted from images. We also study the impact of two methods to incorporate the information about objects necessary for answering a question, in the reasoning module directly, and earlier in the object selection stage. This work highlights the importance of vision in the context of VQA, and the interest of tailoring vision methods used in VQA to the task at hand.