mlsanityData to 14 Oct 2026

Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

arXiv 1711.06526 ↗First posted 17 Nov 2017cs.CVcited by 72

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Abstract (arXiv)

In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects of interests, we propose a framework that incorporates knowledge graphs for describing the relationships between multiple labels. Our model learns an information propagation mechanism from the semantic label space, which can be applied to model the interdependencies between seen and unseen class labels. With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks. Compared to state-of-the-art approaches, comparable or improved performances can be achieved by our method.

Key facts. Cited by 72 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Nov 2017.

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Citing papers per year: 2021: 31; 2022: 13; 2023: 16; 2024: 10; 2025: 2.

72 papers.

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  1. 2021Graph Learning: A Surveycited by 141
  2. 2021A Comprehensive Survey of Scene Graphs: Generation and Applicationcited by 125
  3. 2022DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotationscited by 102
  4. 2021Contrastive Embedding for Generalized Zero-Shot Learningcited by 94
  5. 2021Knowledge-aware Zero-Shot Learning: Survey and Perspectivecited by 41
  6. 2021Zero-Shot Learning on 3D Point Cloud Objects and Beyondcited by 38
  7. 2022Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfercited by 33
  8. 2021Discriminative Region-based Multi-Label Zero-Shot Learningcited by 27
  9. 2021Distilling Holistic Knowledge with Graph Neural Networkscited by 26
  10. 2021Semantic Diversity Learning for Zero-Shot Multi-label Classificationcited by 24
  11. 2021Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Surveycited by 23
  12. 2023DualCoOp++: Fast and Effective Adaptation to Multi-Label Recognition with Limited Annotationscited by 22
38 more
  1. 2022Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attentioncited by 21
  2. 2021Generative Multi-Label Zero-Shot Learningcited by 19
  3. 2024Deep Learning for Multi-Label Learning: A Comprehensive Surveycited by 19
  4. 2021Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmarkcited by 17
  5. 2023Relational Multi-Task Learning: Modeling Relations between Data and Taskscited by 15
  6. 2024TAI++: Text as Image for Multi-Label Image Classification by Co-Learning Transferable Promptcited by 14
  7. 2022Graph Attention Transformer Network for Multi-Label Image Classificationcited by 11
  8. 2021What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Datacited by 10
  9. 2023Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph Generationcited by 10
  10. 2021Learning Visual Models using a Knowledge Graph as a Trainercited by 8
  11. 2023Sample-Efficient Learning of Novel Visual Conceptscited by 8
  12. 2023GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learningcited by 8
  13. 2024Data-free Multi-label Image Recognition via LLM-powered Prompt Tuningcited by 8
  14. 2021Survey of Image Based Graph Neural Networkscited by 7
  15. 2022A Survey on Visual Transfer Learning using Knowledge Graphscited by 7
  16. 2023Knowledge Restore and Transfer for Multi-label Class-Incremental Learningcited by 7
  17. 2023Bi-directional Distribution Alignment for Transductive Zero-Shot Learningcited by 7
  18. 2023Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognitioncited by 7
  19. 2024Organizing Unstructured Image Collections using Natural Languagecited by 7
  20. 2021Multi-layered Semantic Representation Network for Multi-label Image Classificationcited by 6
  21. 2021Benchmarking Knowledge-driven Zero-shot Learningcited by 6
  22. 2022Semantic Representation and Dependency Learning for Multi-Label Image Recognitioncited by 6
  23. 2022Disentangled Action Recognition with Knowledge Basescited by 6
  24. 2022Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Featurescited by 6
  25. 2023Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Frameworkcited by 6
  26. 2023ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrievalcited by 6
  27. 2021Cross Knowledge-based Generative Zero-Shot Learning Approach with Taxonomy Regularizationcited by 5
  28. 2021Graphonomy: Universal Image Parsing via Graph Reasoning and Transfercited by 5
  29. 2021GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passingcited by 5
  30. 2021Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographscited by 5
  31. 2021Dynamic Relevance Learning for Few-Shot Object Detectioncited by 5
  32. 2023Deep Partial Multi-Label Learning with Graph Disambiguationcited by 5
  33. 2021Learning Discriminative Representations for Multi-Label Image Recognitioncited by 4
  34. 2022Category-Agnostic 6D Pose Estimation with Conditional Neural Processescited by 4
  35. 2023BB-GCN: A Bi-modal Bridged Graph Convolutional Network for Multi-label Chest X-Ray Recognitioncited by 4
  36. 2023Co-Learning Meets Stitch-Up for Noisy Multi-label Visual Recognitioncited by 4
  37. 2024ReCon1M:A Large-scale Benchmark Dataset for Relation Comprehension in Remote Sensing Imagerycited by 3
  38. 2024Epsilon: Exploring Comprehensive Visual-Semantic Projection for Multi-Label Zero-Shot Learningcited by 3

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