mlsanityData to 7 Oct 2026

Graph Attention Transformer Network for Multi-Label Image Classification

arXiv 2203.04049 ↗First posted 8 Mar 2022cs.CVcited by 11

Not on the map. Why?

Abstract (arXiv)

Multi-label classification aims to recognize multiple objects or attributes from images. However, it is challenging to learn from proper label graphs to effectively characterize such inter-label correlations or dependencies. Current methods often use the co-occurrence probability of labels based on the training set as the adjacency matrix to model this correlation, which is greatly limited by the dataset and affects the model's generalization ability. In this paper, we propose a Graph Attention Transformer Network (GATN), a general framework for multi-label image classification that can effectively mine complex inter-label relationships. First, we use the cosine similarity based on the label word embedding as the initial correlation matrix, which can represent rich semantic information. Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain. Our extensive experiments have demonstrated that our proposed methods can achieve state-of-the-art performance on three datasets.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 39 of its 62 references that are in the map. First posted on arXiv 8 Mar 2022.

Builds on

39 of 62 references.

References · 39

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  6. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  7. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  8. 2017Graph Attention Networkscited by 8,854
  9. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  10. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  11. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  12. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
27 more
  1. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  2. 2019Heterogeneous Graph Attention Networkcited by 732
  3. 2015LINE: Large-scale Information Network Embeddingcited by 582
  4. 2019Graph Transformer Networkscited by 520
  5. 2019Billion-scale semi-supervised learning for image classificationcited by 232
  6. 2016CNN-RNN: A Unified Framework for Multi-label Image Classificationcited by 189
  7. 2020General Multi-label Image Classification with Transformerscited by 126
  8. 2021Query2Label: A Simple Transformer Way to Multi-Label Classificationcited by 126
  9. 2021A Survey of Visual Transformerscited by 117
  10. 2016Deep Label Distribution Learning with Label Ambiguitycited by 104
  11. 2019Attention in Natural Language Processingcited by 100
  12. 2017Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classificationcited by 85
  13. 2017Multi-label Image Recognition by Recurrently Discovering Attentional Regionscited by 83
  14. 2021ML-Decoder: Scalable and Versatile Classification Headcited by 78
  15. 2017Multi-Label Zero-Shot Learning with Structured Knowledge Graphscited by 72
  16. 2020Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognitioncited by 69
  17. 2019Multi-Label Classification with Label Graph Superimposingcited by 65
  18. 2014CNN: Single-label to Multi-labelcited by 62
  19. 2020Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoderscited by 47
  20. 2019Orderless Recurrent Models for Multi-label Classificationcited by 46
  21. 2017Recurrent Attentional Reinforcement Learning for Multi-label Image Recognitioncited by 44
  22. 2015Exploit Bounding Box Annotations for Multi-label Object Recognitioncited by 39
  23. 2018Chest X-rays Classification: A Multi-Label and Fine-Grained Problemcited by 31
  24. 2017Order-Free RNN with Visual Attention for Multi-Label Classificationcited by 26
  25. 2016Semantic Regularisation for Recurrent Image Annotationcited by 14
  26. 2018Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classificationcited by 4
  27. 2019Learning Category Correlations for Multi-label Image Recognition with Graph Networkscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 6; 2025: 2; 2026: 1.

11 papers.

  1. 2024Deep Learning for Multi-Label Learning: A Comprehensive Surveycited by 18
  2. 2024Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detectioncited by 18
  3. 2022Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classificationcited by 4
  4. 2023Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examplescited by 2
  5. 2024Category-Adaptive Cross-Modal Semantic Refinement and Transfer for Open-Vocabulary Multi-Label Recognitioncited by 1
  6. 2024Learning Semantic-Aware Representation in Visual-Language Models for Multi-Label Recognition with Partial Labelscited by 1
  7. 2025Improving Multi-Label Contrastive Learning by Leveraging Label Distributioncited by 1
  8. 2025DART: Dual Adaptive Refinement Transfer for Open-Vocabulary Multi-Label Recognitioncited by 1
  9. 2024A Consistent Lebesgue Measure for Multi-label Learningcited by 0
  10. 2024Modeling Multi-modal Cross-interaction for Multi-label Few-shot Image Classification Based on Local Feature Selectioncited by 0
  11. 2026Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlookcited by 0