Research map / Vision-language and multimodal / Human and embodied modeling
Text Matching as Image Recognition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use hierarchical convolution over a word-level matching matrix | MatchPyramid · MSRP | accuracy: 69.90 -> 75.94 | Helped |
| use hierarchical convolution over a word-level matching matrix | MatchPyramid · Paper citation matching | accuracy: 86.48 -> 88.73 | Helped |
| use an indicator matching matrix instead of dot-product similarities (ablation) | MatchPyramid · MSRP | accuracy: 75.94 -> 75.77 | Hurt |
| use cosine similarity instead of dot-product similarities (ablation) | MatchPyramid · MSRP | accuracy: 75.94 -> 75.13 | Hurt |
| use an indicator matching matrix instead of dot-product similarities (ablation) | MatchPyramid · Paper citation matching | F1: 82.86 -> 44.71 | Hurt |
| use cosine similarity instead of dot-product similarities (ablation) | MatchPyramid · Paper citation matching | accuracy: 88.73 -> 86.65 | Hurt |
| include word-vector norms in the matching similarity (ablation) | MatchPyramid · Paper citation matching | accuracy: 86.65 -> 88.73 | Helped |
Closest papers by meaning
- Expressing Objects Just Like Words: Recurrent Visual Embedding for Image-Text Matching (AAAI 2020 · similarity 0.88)
- An End-to-End TextSpotter With Explicit Alignment and Attention (CVPR 2018 · similarity 0.87)
- Similarity Reasoning and Filtration for Image-Text Matching (AAAI 2021 · similarity 0.86)
- Composing Object Relations and Attributes for Image-Text Matching (CVPR 2024 · similarity 0.84)
- Expanding the Scope of Negatives: Boosting Image-Text Matching with Negatives Distribution Guided Learning (AAAI 2025 · similarity 0.84)
- CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching (NeurIPS 2024 · similarity 0.83)
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