mlsanityData to 7 Oct 2026

CNN-RNN: A Unified Framework for Multi-label Image Classification

arXiv 1604.04573 ↗First posted 15 Apr 2016cs.CV cs.LG cs.NEcited by 189

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Abstract (arXiv)

While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image. Traditional approaches to multi-label image classification learn independent classifiers for each category and employ ranking or thresholding on the classification results. These techniques, although working well, fail to explicitly exploit the label dependencies in an image. In this paper, we utilize recurrent neural networks (RNNs) to address this problem. Combined with CNNs, the proposed CNN-RNN framework learns a joint image-label embedding to characterize the semantic label dependency as well as the image-label relevance, and it can be trained end-to-end from scratch to integrate both information in a unified framework. Experimental results on public benchmark datasets demonstrate that the proposed architecture achieves better performance than the state-of-the-art multi-label classification model

Key facts. Cited by 189 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2016.

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Citing papers per year: 2021: 46; 2022: 34; 2023: 34; 2024: 37; 2025: 29; 2026: 9.

189 papers.

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  1. 2021Query2Label: A Simple Transformer Way to Multi-Label Classificationcited by 126
  2. 2022DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotationscited by 102
  3. 2021Can multi-label classification networks know what they don't know?cited by 100
  4. 2021Multi-Label Learning from Single Positive Labelscited by 81
  5. 2021Residual Attention: A Simple but Effective Method for Multi-Label Recognitioncited by 52
  6. 2022Texts as Images in Prompt Tuning for Multi-Label Image Recognitioncited by 51
  7. 2021Transformer-based Dual Relation Graph for Multi-label Image Recognitioncited by 41
  8. 2021Structured Semantic Transfer for Multi-Label Recognition with Partial Labelscited by 41
  9. 2022Large Loss Matters in Weakly Supervised Multi-Label Classificationcited by 38
  10. 2025MAT-Agent: Adaptive Multi-Agent Training Optimizationcited by 34
  11. 2022Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfercited by 33
  12. 2021Modeling Multi-Label Action Dependencies for Temporal Action Localizationcited by 32
38 more
  1. 2021Discriminative Region-based Multi-Label Zero-Shot Learningcited by 27
  2. 2021Multi-label Classification with Partial Annotations using Class-aware Selective Losscited by 25
  3. 2022Acknowledging the Unknown for Multi-label Learning with Single Positive Labelscited by 24
  4. 2021sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classificationcited by 23
  5. 2023DualCoOp++: Fast and Effective Adaptation to Multi-Label Recognition with Limited Annotationscited by 22
  6. 2021Meta-Learning for Multi-Label Few-Shot Classificationcited by 21
  7. 2022Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attentioncited by 21
  8. 2023PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classificationcited by 21
  9. 2021Generative Multi-Label Zero-Shot Learningcited by 19
  10. 2022Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labelscited by 19
  11. 2023Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learningcited by 19
  12. 2023Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariancecited by 19
  13. 2021Multi-Label Image Classification with Contrastive Learningcited by 18
  14. 2024Deep Learning for Multi-Label Learning: A Comprehensive Surveycited by 18
  15. 2021Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmarkcited by 17
  16. 2022AGCN: Augmented Graph Convolutional Network for Lifelong Multi-label Image Recognitioncited by 16
  17. 2022ZLPR: A Novel Loss for Multi-label Classificationcited by 16
  18. 2021Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classificationcited by 15
  19. 2021Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attentioncited by 15
  20. 2022Statistical Deep Learning for Spatial and Spatio-Temporal Datacited by 15
  21. 2023Relational Multi-Task Learning: Modeling Relations between Data and Taskscited by 15
  22. 2023LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognitioncited by 15
  23. 2021Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunitiescited by 14
  24. 20233DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challengecited by 14
  25. 2024TAI++: Text as Image for Multi-Label Image Classification by Co-Learning Transferable Promptcited by 14
  26. 2025Test-Time Backdoor Detection for Object Detection Modelscited by 14
  27. 2021Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregationcited by 13
  28. 2023SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Predictioncited by 13
  29. 2023SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigmcited by 13
  30. 2024Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibrationcited by 13
  31. 2021GM-MLIC: Graph Matching based Multi-Label Image Classificationcited by 12
  32. 2022Multi-Label Continual Learning using Augmented Graph Convolutional Networkcited by 12
  33. 2022Graph Attention Transformer Network for Multi-Label Image Classificationcited by 11
  34. 2023Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classificationcited by 11
  35. 2024Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classificationcited by 11
  36. 2025Long-tailed Recognition with Model Rebalancingcited by 10
  37. 2021Evaluating Multi-label Classifiers with Noisy Labelscited by 9
  38. 2021HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoderscited by 9

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