# Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition

arXiv: https://arxiv.org/abs/2007.01755 · HTML: https://mlsanity.com/paper/2007.01755/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 30 of 50)

- [Query2Label: A Simple Transformer Way to Multi-Label Classification](https://mlsanity.com/paper/2107.10834/) (2021)
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- [Transformer-based Dual Relation Graph for Multi-label Image Recognition](https://mlsanity.com/paper/2110.04722/) (2021)
- [Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels](https://mlsanity.com/paper/2203.02172/) (2022)
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- [CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representation](https://mlsanity.com/paper/2406.14830/) (2024)
- [Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification](https://mlsanity.com/paper/2307.09715/) (2023)
- [GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning](https://mlsanity.com/paper/2309.00923/) (2023)
- [Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training](https://mlsanity.com/paper/2404.06287/) (2024)
- [Semantic Representation and Dependency Learning for Multi-Label Image Recognition](https://mlsanity.com/paper/2204.03795/) (2022)
- [Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation](https://mlsanity.com/paper/2205.10986/) (2022)
- [Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels](https://mlsanity.com/paper/2205.13092/) (2022)
- [Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features](https://mlsanity.com/paper/2208.09562/) (2022)
- [Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learning](https://mlsanity.com/paper/2212.01261/) (2022)
- [Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains](https://mlsanity.com/paper/2301.04494/) (2023)
- [Semantic Contrastive Bootstrapping for Single-positive Multi-label Recognition](https://mlsanity.com/paper/2307.07680/) (2023)
- [Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification](https://mlsanity.com/paper/2209.06585/) (2022)
- [SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image Classification](https://mlsanity.com/paper/2311.15200/) (2023)
- [All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP)](https://mlsanity.com/paper/2206.08653/) (2022)
- [Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision](https://mlsanity.com/paper/2211.12716/) (2022)
- [GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition](https://mlsanity.com/paper/2308.14378/) (2023)
- [Epsilon: Exploring Comprehensive Visual-Semantic Projection for Multi-Label Zero-Shot Learning](https://mlsanity.com/paper/2408.12253/) (2024)
- [Knowledge Distillation from Single to Multi Labels: an Empirical Study](https://mlsanity.com/paper/2303.08360/) (2023)
- [NOAH: Learning Pairwise Object Category Attentions for Image Classification](https://mlsanity.com/paper/2402.02377/) (2024)
- [AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated Learning](https://mlsanity.com/paper/2403.08383/) (2024)
- [Dual-View Alignment Learning with Hierarchical-Prompt for Class-Imbalance Multi-Label Classification](https://mlsanity.com/paper/2509.17747/) (2025)
- [Object-Aware Self-supervised Multi-Label Learning](https://mlsanity.com/paper/2205.07028/) (2022)
- [Learning Disentangled Label Representations for Multi-label Classification](https://mlsanity.com/paper/2212.01461/) (2022)

## Abstract (arXiv)

Multi-label image recognition is a practical and challenging task compared to single-label image classification. However, previous works may be suboptimal because of a great number of object proposals or complex attentional region generation modules. In this paper, we propose a simple but efficient two-stream framework to recognize multi-category objects from global image to local regions, similar to how human beings perceive objects. To bridge the gap between global and local streams, we propose a multi-class attentional region module which aims to make the number of attentional regions as small as possible and keep the diversity of these regions as high as possible. Our method can efficiently and effectively recognize multi-class objects with an affordable computation cost and a parameter-free region localization module. Over three benchmarks on multi-label image classification, we create new state-of-the-art results with a single model only using image semantics without label dependency. In addition, the effectiveness of the proposed method is extensively demonstrated under different factors such as global pooling strategy, input size and network architecture. Code has been made available at~urlhttps://github.com/gaobb/MCAR.