# Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition

arXiv: https://arxiv.org/abs/1712.07465 · HTML: https://mlsanity.com/paper/1712.07465/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2017.

## Built on by (most cited first, 30 of 44)

- [Query2Label: A Simple Transformer Way to Multi-Label Classification](https://mlsanity.com/paper/2107.10834/) (2021)
- [Spatial Dual-Modality Graph Reasoning for Key Information Extraction](https://mlsanity.com/paper/2103.14470/) (2021)
- [Transformer-based Dual Relation Graph for Multi-label Image Recognition](https://mlsanity.com/paper/2110.04722/) (2021)
- [Structured Semantic Transfer for Multi-Label Recognition with Partial Labels](https://mlsanity.com/paper/2112.10941/) (2021)
- [Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels](https://mlsanity.com/paper/2203.02172/) (2022)
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- [PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification](https://mlsanity.com/paper/2307.09066/) (2023)
- [Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels](https://mlsanity.com/paper/2205.11131/) (2022)
- [Deep Learning for Multi-Label Learning: A Comprehensive Survey](https://mlsanity.com/paper/2401.16549/) (2024)
- [Multi-Label Image Classification with Contrastive Learning](https://mlsanity.com/paper/2107.11626/) (2021)
- [Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark](https://mlsanity.com/paper/2103.10895/) (2021)
- [Improving Face-Based Age Estimation with Attention-Based Dynamic Patch Fusion](https://mlsanity.com/paper/2112.10167/) (2021)
- [Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration](https://mlsanity.com/paper/2407.06844/) (2024)
- [Graph Attention Transformer Network for Multi-Label Image Classification](https://mlsanity.com/paper/2203.04049/) (2022)
- [CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise](https://mlsanity.com/paper/2105.07338/) (2021)
- [Activation Space Selectable Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2408.08338/) (2024)
- [End-to-End Supervised Multilabel Contrastive Learning](https://mlsanity.com/paper/2307.03967/) (2023)
- [Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training](https://mlsanity.com/paper/2404.06287/) (2024)
- [Multi-layered Semantic Representation Network for Multi-label Image Classification](https://mlsanity.com/paper/2106.11596/) (2021)
- [Semantic Representation and Dependency Learning for Multi-Label Image Recognition](https://mlsanity.com/paper/2204.03795/) (2022)
- [Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels](https://mlsanity.com/paper/2205.13092/) (2022)
- [Rethink, Revisit, Revise: A Spiral Reinforced Self-Revised Network for Zero-Shot Learning](https://mlsanity.com/paper/2112.00410/) (2021)
- [Learning Discriminative Representations for Multi-Label Image Recognition](https://mlsanity.com/paper/2107.11159/) (2021)
- [SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image Classification](https://mlsanity.com/paper/2311.15200/) (2023)
- [Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision](https://mlsanity.com/paper/2211.12716/) (2022)
- [Text as Image: Learning Transferable Adapter for Multi-Label Classification](https://mlsanity.com/paper/2312.04160/) (2023)
- [HSVLT: Hierarchical Scale-Aware Vision-Language Transformer for Multi-Label Image Classification](https://mlsanity.com/paper/2407.16244/) (2024)
- [Learning Disentangled Label Representations for Multi-label Classification](https://mlsanity.com/paper/2212.01461/) (2022)
- [Deep Dependency Networks for Multi-Label Classification](https://mlsanity.com/paper/2302.00633/) (2023)

## Abstract (arXiv)

Recognizing multiple labels of images is a fundamental but challenging task in computer vision, and remarkable progress has been attained by localizing semantic-aware image regions and predicting their labels with deep convolutional neural networks. The step of hypothesis regions (region proposals) localization in these existing multi-label image recognition pipelines, however, usually takes redundant computation cost, e.g., generating hundreds of meaningless proposals with non-discriminative information and extracting their features, and the spatial contextual dependency modeling among the localized regions are often ignored or over-simplified. To resolve these issues, this paper proposes a recurrent attention reinforcement learning framework to iteratively discover a sequence of attentional and informative regions that are related to different semantic objects and further predict label scores conditioned on these regions. Besides, our method explicitly models long-term dependencies among these attentional regions that help to capture semantic label co-occurrence and thus facilitate multi-label recognition. Extensive experiments and comparisons on two large-scale benchmarks (i.e., PASCAL VOC and MS-COCO) show that our model achieves superior performance over existing state-of-the-art methods in both performance and efficiency as well as explicitly identifying image-level semantic labels to specific object regions.