Co- VQA : Answering by Interactive Sub Question Sequence (2022)

Abstract: Most existing approaches to Visual Question Answering (VQA) answer questions directly, however, people usually decompose a complex question into a sequence of simple sub questions and finally obtain the answer to the original question after answering the sub question sequence(SQS). By simulating the process, this paper proposes a conversation-based VQA (Co-VQA) framework, which consists of three components: Questioner, Oracle, and Answerer. Questioner raises the sub questions using an extending HRED model, and Oracle answers them one-by-one. An Adaptive Chain Visual Reasoning Model (ACVRM) for Answerer is also proposed, where the question-answer pair is used to update the visual representation sequentially. To perform supervised learning for each model, we introduce a well-designed method to build a SQS for each question on VQA 2.0 and VQA-CP v2 datasets. Experimental results show that our method achieves state-of-the-art on VQA-CP v2. Further analyses show that SQSs help build direct semantic connections between questions and images, provide question-adaptive variable-length reasoning chains, and with explicit interpretability as well as error traceability.

## introduction visual question answering @xcite requires to answer questions about images. it has to process visual and language information simultaneously, which is a basic ability of advanced agents. therefore, it has attracted more and more attention @xcite @xcite . the conventional approach @xcite for visual question answering (vqa) is to encode image and question separately and incorporate the representation of each modality into a joint representation. recently, with the proposal of transformer (vaswani et al., * xiaojie wang is the corresponding author. ## approach figure 2 shows the overall structure and data flows of co-vqa, where the questioner, the oracle, and the answerer are three major components. given an input image i and a question q, co-vqa aims to predict the correct answer from the candidate answer set a * . specifically, the questioner generates a new sq q t for the next round by combining the information in q, i and the dialogue history then, oracle produces appropriate answer a t for q t . after accomplishing the last round of sub question-answer pair, answerer utilizes the historical information accumulated throughout the process to obtain the final answer. in this section, we will introduce the three components in section 3.1-3.3. ## conclusions we propose a conversation-based vqa (co-vqa) framework which consists of questioner, oracle, and answerer. through internal conversation based on sqs, our model not only has explicit interpretability and traceability of answer errors but also can carry out question-adaptive variablelength reasoning chains. currently, questioner is relatively simple, and the quality still has a lot of room to improve. meanwhile, current sqs are only yes/no questions. for future work, we plan to explore how to more effectively generate more diverse and higher quality sqs, and look forward to better model performance.