Papers › Deep Modular Co-Attention Networks for Visual Question Answering
Deep Modular Co-Attention Networks for Visual Question Answering
Zhou Yu, Jun Yu, Yuhao Cui, DaCheng Tao, Qi Tian
Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective `co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, most successful attempts at co-attention learning have been achieved by using shallow models, and deep co-attention models show little improvement over their shallow counterparts. In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth. Each MCA layer models the self-attention of questions and images, as well as the guided-attention of images jointly using a modular composition of two basic attention units. We quantitatively and qualitatively evaluate MCAN on the benchmark VQA-v2 dataset and conduct extensive ablation studies to explore the reasons behind MCAN's effectiveness. Experimental results demonstrate that MCAN significantly outperforms the previous state-of-the-art. Our best single model delivers 70.63% overall accuracy on the test-dev set. Code is available at https://github.com/MILVLG/mcan-vqa.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | SQA3D | MCAN | AnswerExactMatch (Question Answering) | 43.42 | #7 of 7 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v2 test-dev | MCANed-6 | Accuracy | 70.63 | #29 of 56 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v2 test-std | MCANed-6 | overall | 70.9 | #25 of 38 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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