Research map / Label-efficient and robust learning / Recognition and long-tail
Recurrent Attentional Reinforcement Learning for Multi-Label Image Recognition
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use learned recurrent attention regions instead of randomly selected locations (ablation) | Recurrent attention model · VOC07 | mAP (%): 89.0 -> 90.2 | Helped |
| Use learned recurrent attention regions instead of object proposals (ablation) | Recurrent attention model · VOC07 | mAP (%): 88.6 -> 90.2 | Helped |
| Add an LSTM to localization to capture dependencies across attended regions (ablation) | Region localization · VOC07 | mAP (%): 89.6 -> 91.3 | Helped |
| Add LSTM-based classification of attended regions (ablation) | LSTM-based region localization · VOC07 | mAP (%): 90.0 -> 91.3 | Helped |
| Use multiple variable-scale and variable-aspect-ratio regions per location instead of one region (ablation) | Recurrent attention model · VOC07 | mAP (%): 90.9 -> 91.3 | Helped |
| Increase recursive iterations from 1 to 5 (ablation) | Recurrent attention model · VOC07 | mAP (%): 90.9 -> 91.3 | Helped |
| Increase recursive iterations from 5 to 10 (ablation) | Recurrent attention model · VOC07 | mAP (%): 91.3 -> 91.3 | No clear effect |
| Combine predictions across input scales 512 and 640 | Recurrent attention model · VOC07 | mAP (%): 91.3 -> 92.0 | Helped |
Closest papers by meaning
- Saliency-based Sequential Image Attention with Multiset Prediction (NeurIPS 2017 · similarity 0.90)
- A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning (CVPR 2020 · similarity 0.88)
- Order-Free RNN With Visual Attention for Multi-Label Classification (AAAI 2018 · similarity 0.88)
- Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning (CVPR 2025 · similarity 0.87)
- Recover and Match: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport (CVPR 2025 · similarity 0.85)
- Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning (ICML 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.