TallyQA: Answering Complex Counting Questions

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

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.

Published
2019-07-17
Section
AAAI Technical Track: Vision