TallyQA: Answering Complex Counting Questions

Authors

  • Manoj Acharya Rochester Institute of Technology
  • Kushal Kafle Rochester Institute of Technology
  • Christopher Kanan Rochester Institute of Technology

DOI:

https://doi.org/10.1609/aaai.v33i01.33018076

Abstract

Most counting questions in visual question answering (VQA) datasets are simple and require no more than object detection. Here, we study algorithms for complex counting questions that involve relationships between objects, attribute identification, reasoning, and more. To do this, we created TallyQA, the world’s largest dataset for open-ended counting. We propose a new algorithm for counting that uses relation networks with region proposals. Our method lets relation networks be efficiently used with high-resolution imagery. It yields stateof-the-art results compared to baseline and recent systems on both TallyQA and the HowMany-QA benchmark.

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Published

2019-07-17

How to Cite

Acharya, M., Kafle, K., & Kanan, C. (2019). TallyQA: Answering Complex Counting Questions. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 8076-8084. https://doi.org/10.1609/aaai.v33i01.33018076

Issue

Section

AAAI Technical Track: Vision