TA-Student VQA: Multi-Agents Training by Self-Questioning

Peixi Xiong, Ying Wu

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


There are two main challenges in Visual Question Answering (VQA). The first one is that each model obtains its strengths and shortcomings when applied to several questions; what is more, the 'ceiling effect' for specific questions is difficult to overcome with simple consecutive training. The second challenge is that even the state-of-the-art dataset is of large scale, questions targeted at a single image are off in format and lack diversity in content. We introduce our self-questioning model with multi-agent training: TA-student VQA. This framework differs from standard VQA algorithms by involving question-generating mechanisms and collaborative learning questions between question-answering agents. Thus, TA-student VQA overcomes the limitation of the content diversity and format variation of questions and improves the overall performance of multiple question-answering agents. We evaluate our model on VQA-v2, which outperforms algorithms without such mechanisms. In addition, TA-student VQA achieves a greater model capacity, allowing it to answer more generated questions in addition to those in the annotated datasets.

Original languageEnglish (US)
Article number9157453
Pages (from-to)10062-10072
Number of pages11
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 2020
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
Duration: Jun 14 2020Jun 19 2020

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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