Research map / Vision-language and multimodal / Visual question answering
Dual Attention Networks for Multimodal Reasoning and Matching
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ResNet image features instead of VGG image features (ablation) | DAN · VQA test-dev open-ended | All accuracy: 62.0 -> 64.3 | Helped |
| Use ResNet image features instead of VGG image features (ablation) | DAN · Flickr30K test image-to-text | R@1: 41.4 -> 55.0 | Helped |
| Jointly attend to image regions and text words through multiple steps | DAN · VQA test-dev open-ended | All accuracy: 61.8 -> 64.3 | Helped |
| Use dual attention for cross-modal matching | DAN · Flickr30K test image-to-text | R@1: 40.3 -> 55.0 | Helped |
Closest papers by meaning
- Where Does Vision Meet Language? Understanding and Refining Visual Fusion in MLLMs via Contrastive Attention (CVPR 2026 · similarity 0.89)
- Seeing is Understanding: Unlocking Causal Attention into Modality-Mutual Attention for Multimodal LLMs (ICML 2026 · similarity 0.89)
- From Narrow to Panoramic Vision: Attention-Guided Cold-Start Reshapes Multimodal Reasoning (ICLR 2026 · similarity 0.87)
- Unveiling Visual Perception in Language Models: An Attention Head Analysis Approach (CVPR 2025 · similarity 0.87)
- Seeing What Matters: A Training-Free Self-Guided Framework for Multimodal Detail Perception and Reasoning (CVPR 2026 · similarity 0.87)
- Remodeling Semantic Relationships in Vision-Language Fine-Tuning (AAAI 2026 · similarity 0.87)
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