mlsanityData to 7 Oct 2026

TallyQA: Answering Complex Counting Questions

arXiv 1810.12440 ↗First posted 29 Oct 2018cs.CVcited by 152

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Cited by 79 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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 state-of-the-art results compared to baseline and recent systems on both TallyQA and the HowMany-QA benchmark.

Key facts. Cited by 152 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Oct 2018.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 12; 2024: 32; 2025: 57; 2026: 43.

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