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Multi-Label Image Recognition with Graph Convolutional Networks

arXiv 1904.03582 ↗First posted 7 Apr 2019cs.CV cs.LGcited by 320

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Abstract (arXiv)

The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model the label dependencies to improve the recognition performance. To capture and explore such important dependencies, we propose a multi-label classification model based on Graph Convolutional Network (GCN). The model builds a directed graph over the object labels, where each node (label) is represented by word embeddings of a label, and GCN is learned to map this label graph into a set of inter-dependent object classifiers. These classifiers are applied to the image descriptors extracted by another sub-net, enabling the whole network to be end-to-end trainable. Furthermore, we propose a novel re-weighted scheme to create an effective label correlation matrix to guide information propagation among the nodes in GCN. Experiments on two multi-label image recognition datasets show that our approach obviously outperforms other existing state-of-the-art methods. In addition, visualization analyses reveal that the classifiers learned by our model maintain meaningful semantic topology.

Key facts. Cited by 320 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 74; 2022: 66; 2023: 79; 2024: 48; 2025: 31; 2026: 22.

320 papers.

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2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
2021Query2Label: A Simple Transformer Way to Multi-Label Classificationcited by 126
2021Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labelscited by 117
2022DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotationscited by 102
2021Learning to Predict Visual Attributes in the Wildcited by 95
2022Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisitedcited by 87
2021Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labelingcited by 84
2021Multi-Label Learning from Single Positive Labelscited by 81
2021Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detectioncited by 79
2021ML-Decoder: Scalable and Versatile Classification Headcited by 78
2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
2021Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identificationcited by 63

38 more

2022Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labelscited by 63
2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
2021Residual Attention: A Simple but Effective Method for Multi-Label Recognitioncited by 52
2022Texts as Images in Prompt Tuning for Multi-Label Image Recognitioncited by 51
2021Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentationcited by 47
2024Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Taskscited by 47
2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
2021Transformer-based Dual Relation Graph for Multi-label Image Recognitioncited by 41
2021Structured Semantic Transfer for Multi-Label Recognition with Partial Labelscited by 41
2022Simplicial Attention Networkscited by 40
2023CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classificationcited by 39
2022Large Loss Matters in Weakly Supervised Multi-Label Classificationcited by 38
2025MAT-Agent: Adaptive Multi-Agent Training Optimizationcited by 34
2022Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfercited by 33
2021Modeling Multi-Label Action Dependencies for Temporal Action Localizationcited by 32
2023FoodLMM: A Versatile Food Assistant using Large Multi-modal Modelcited by 32
2021Simple and Robust Loss Design for Multi-Label Learning with Missing Labelscited by 30
2022Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labelscited by 29
2021RTIC: Residual Learning for Text and Image Composition using Graph Convolutional Networkcited by 27
2021Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videoscited by 27
2021Discriminative Region-based Multi-Label Zero-Shot Learningcited by 27
2023Generalized Simplicial Attention Neural Networkscited by 27
2021Multi-label Classification with Partial Annotations using Class-aware Selective Losscited by 25
2021Semantic Diversity Learning for Zero-Shot Multi-label Classificationcited by 24
2022i-Code: An Integrative and Composable Multimodal Learning Frameworkcited by 24
2023Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labelscited by 24
2023Context Understanding in Computer Vision: A Surveycited by 23
2022Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognitioncited by 22
2022One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancementcited by 22
2023DualCoOp++: Fast and Effective Adaptation to Multi-Label Recognition with Limited Annotationscited by 22
2023Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challengecited by 22
2021MlTr: Multi-label Classification with Transformercited by 21
2022Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attentioncited by 21
2022Simplicial Attention Neural Networkscited by 21
2023PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classificationcited by 21
2021Generative Multi-Label Zero-Shot Learningcited by 19
2021SketchLattice: Latticed Representation for Sketch Manipulationcited by 19
2022Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labelscited by 19

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