Research map / Label-efficient and robust learning / Recognition and long-tail
Order-Free RNN With Visual Attention for Multi-Label Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add visual attention to the confidence-ranked LSTM (ablation) | Order-Free RNN · NUS-WIDE test | C-F1: 54.5 -> 54.7 | Helped |
| Add visual attention to the confidence-ranked LSTM (ablation) | Order-Free RNN · NUS-WIDE test | O-F1: 70.2 -> 70.2 | No clear effect |
| Add visual attention to the confidence-ranked LSTM (ablation) | Order-Free RNN · MS-COCO validation | C-F1: 60.0 -> 62.1 | Helped |
| Add visual attention to the confidence-ranked LSTM (ablation) | Order-Free RNN · MS-COCO validation | O-F1: 66.2 -> 67.7 | Helped |
| Use automatically learned order rather than frequency-first order with attention (ablation) | Order-Free RNN · NUS-WIDE test | C-F1: 48.8 -> 54.7 | Helped |
| Use automatically learned order rather than frequency-first order with attention (ablation) | Order-Free RNN · MS-COCO validation | C-F1: 55.2 -> 62.1 | Helped |
| Use automatically learned order rather than rare-first order with attention (ablation) | Order-Free RNN · NUS-WIDE test | O-F1: 59.8 -> 70.2 | Helped |
| Use automatically learned order rather than rare-first order with attention (ablation) | Order-Free RNN · MS-COCO validation | C-F1: 58.0 -> 62.1 | Helped |
| Use automatically learned order rather than rare-first order with attention (ablation) | Order-Free RNN · MS-COCO validation | O-F1: 57.0 -> 67.7 | Helped |
| Use automatically learned order rather than frequency-first order with attention (ablation) | Order-Free RNN · NUS-WIDE test | O-F1: 65.5 -> 70.2 | Helped |
Closest papers by meaning
- CNN-RNN: A Unified Framework for Multi-Label Image Classification (CVPR 2016 · similarity 0.93)
- Orderless Recurrent Models for Multi-Label Classification (CVPR 2020 · similarity 0.92)
- Recurrent Attentional Reinforcement Learning for Multi-Label Image Recognition (AAAI 2018 · similarity 0.88)
- Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification (NeurIPS 2017 · similarity 0.88)
- Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning (CVPR 2025 · similarity 0.85)
- Enhancing Multi-Label Classification via Dynamic Label-Order Learning (AAAI 2024 · similarity 0.85)
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