# Multi-Granularity Semantic Collaborative Reasoning Network for Visual Dialog
## 6 Conclusions  
In this paper, we proposed the MGSCRN visual dialog system. The QAAR module performs collaborative reasoning while leveraging a variety of granular semantic representations of a new question and an image's dialog history to capture and analyze historical information. The VAAA module performs collaborative reasoning to facilitate visual objects' grounding. We empirically validated our novel method on VisDial v1.0, VisPro, and VisDialConv datasets, achieving outstanding prediction accuracy and task efficacy.  
In terms of applications, the proposed model is a magnificent implementation of AI-supported knowledge management and plays a crucial role in human-machine interaction, such as dealing with robotics applications and AI assistants. In terms of the limitations of the proposed method, our method fed the entire dialog history into the model, disregarding the dialog history's temporal relationship and discussed topic changes, when modeling the interactions between the current question and dialog history. By memorizing the topic changes, the model may capture the historical information more accurately as the conversation moves forward. Note that some of the questions can be answered by simply seeing the image without access to the history information. The proposed model may be unable to correctly answer these questions due to excessive memory of irrelevant history information. In addition, the accuracy of locating target objects may be decreased because our model does not consider the positional relationships between visual objects.  
In the future, we will consider the temporal relationship between many rounds of the dialog history to determine how the discussed topic changes, so that the model can more precisely capture the background knowledge relevant to the current question. To answer questions that are unrelated to dialog history, how to use dialog history in a balanced manner is another challenge. Additionally, we expect to incorporate spatial relationships between visual objects by understanding the prior dialog history.  
Conceptualization, H.Z. and X.W.; methodology, H.Z.; software, H.Z. and S.J.; validation, H.Z. and S.J.; formal analysis, H.Z. and X.L; writing--original draft preparation, H.Z.; writing--review and editing, H.Z., X.W., X.L. and S.J. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by the National Natural Science Foundation of China (No. 62076032).  
Not applicable.  
Figure 6: Examples of error cases from the VisDial v1.0 validation set. The visual objects in the red box are selected by the proposed model. The errors can be categorized into three groups based on the reasons: (1) common-sense reasoning, (2) the image not containing the target visual objects, and (3) keyword matching. (**a,b**) belong to the first group, (**c**) belong to second, and (**d**) belong to third, respectively.  
Data Availability Statement: Publicly available datasets were analyzed in this study. The data can be found here: [[https://visualdialog.org/data](https://visualdialog.org/data)]([https://visualdialog.org/data](https://visualdialog.org/data)) (accessed on 3 August 2022).