1. Missing important related works
The related works are not well-reviewed. To be specific, only two papers of 2020 are discussed and recent papers of 2021 are missing. For example, the following papers are very close to the topic this paper addresses:
Dynamic Fusion With Intra-and Inter-Modality Attention Flow for Visual Question Answering, CVPR 2019.
Deep Modular Co-Attention Networks for Visual Question Answering, CVPR 2019.
Uniter: Universal image-text representation learning, ECCV 2020.
Visualbert: A simple and performant baseline for vision and language.
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision.
2. Compared baselines are not advanced enough.
VQA is a very popular research topic and plenty of approaches are benchmarked on the VQA-v2 dataset and the GQA dataset. However, many recent advanced methods are missed. For example, only one method after 2019 is compared in Table 3.
3. Missing some details of model implementation.
What are the models used to extract entity-level, noun phrase level, and sentence level features in section 3.1.2?
4. This paper is not well organized.
The layout of this paper is a bit rushed. For example,
The font size of some annotations of Figure1 and Figure 2 is relatively small. And these two figures are not drawn explicitly enough.
Table 2 is inserted wrongly inside of a paragraph.
Top two lines on page 6 are in the wrong format.
4. This paper is not well organized. The layout of this paper is a bit rushed. For example, The font size of some annotations of Figure1 and Figure 2 is relatively small. And these two figures are not drawn explicitly enough. Table 2 is inserted wrongly inside of a paragraph. Top two lines on page 6 are in the wrong format.