Papers › Learning to Count Objects in Natural Images for Visual Question Answering
Learning to Count Objects in Natural Images for Visual Question Answering
Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
Visual Question Answering (VQA) models have struggled with counting objects in natural images so far. We identify a fundamental problem due to soft attention in these models as a cause. To circumvent this problem, we propose a neural network component that allows robust counting from object proposals. Experiments on a toy task show the effectiveness of this component and we obtain state-of-the-art accuracy on the number category of the VQA v2 dataset without negatively affecting other categories, even outperforming ensemble models with our single model. On a difficult balanced pair metric, the component gives a substantial improvement in counting over a strong baseline by 6.6%.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Question Answering (VQA) | VQA v2 test-dev | DMN | Accuracy | 68.09 | #35 of 56 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v2 test-std | DMN | overall | 68.4 | #32 of 38 | Archive leaderboard | report |
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