# Multi-Granularity Semantic Collaborative Reasoning Network for Visual Dialog
## 2 Related Work  
### Visual Dialog  
With the advancement of computing sciences and artificial intelligence, many researchers seek solutions to achieve truly intelligent artificial systems for complex real-life applications. Various machine learning techniques have been developed to tackle challenging real-world problems, such as unstructured data classification in social networks [11], sentiment analysis of Chinese short financial texts [12], and even energy conservation in communications [13]. Recently, many works have paid attention to handle multimodal cognitive problems by analyzing vision and language from real life. Compared to the majority of vision-language tasks, such as image captioning [1] and VQA [2], visual dialog [4], which involves multi-turn visual-grounded conversations, is more engaging and challenging. Two separate teams of researchers simultaneously published two types of visual dialog datasets. One is the Guess-what dataset that [PERSON] et al. [14] gathered. It is a goal-driven visual dialog. The questioner must correctly predict the visual target by asking a series of questions. The Oracle provides yes/no/n.a. answers. The other is a large-scale free-form visual dialog dataset called VisDial [4], where the questioner asks open-ended questions based on the image captions and dialog history to assist itself to better comprehend the visual contents; the answerer responds depending on the image and dialog history. In this paper, the second setting was used. Various technical vantage points were used to study visual dialog tasks. Three baselines were initially proposed to go with the VisDial dataset: the hierarchical recurrent encoder (HRE) [4] included a dialog-RNN sitting on top of a recurrent block, which was capable of selecting relevant history from previous rounds; the late fusion (LF) [4] directly concatenated individual representations of the image, dialog history, and question followed by a linear transformation; and the memory network (MN) [4] treated each prior question-answer pair as a fact and used a SoftMax to obtain probabilities over the stored facts. Figure 2: A diagram for proposed method MGSCRN. Most of the later techniques utilized various attention mechanisms. In each step of the reasoning process, ReDAN [6] calculated attention maps to the dialog history and visual objects. To manage the visual and textual information in a parallel and adaptive way, heterogeneous excitation-and-squeeze network (HESNet) [15] excavated multi-modal attention. Recursive visual attention (RvA) [16] updated visual attention while recursively browsing the dialog history to tackle the problem of visual reference resolution. In order to use the historical information in a balanced way, the reciprocal question representation learning network (RQRLN) [17] utilized transformer-based attention to reciprocally learn a new question representation through the intersections of two types of question representations with and without dialog history. Other approaches concentrated on combining neural networks with graphical structural representation to capture the semantic dependence between cross-modal information. A visual graph was built by aligning vision-language for graph inference (AVLGI) [18]. It was used to enhance visual features guided by textual features, and a scene graph was then created to incorporate external knowledge. To discover the partially relevant contexts, a context-aware graph (CAG) [19] constructed a dynamic graph structure, which could be iteratively updated by applying an adaptive top-K message transmission mechanism. Pretrained models were used in recent studies, and they performed admirably. [PERSON] et al. [20] initialized the encoder with BERT to fuse of dialog history and visual contents in order to better leverage the pretrained language representations. [PERSON] et al. [21] utilized two-stream vision-language pretrained models (i.e., ViLBERT) for this task to improve the interactions between vision and language.