Papers Tutorials Talks Blogs Datasets & Benchmarks
Multi-label Image Recognition
Pub. Title Links
TPAMI [KGGR] Knowledge-Guided Multi-Label Few-Shot Learning for General Image RecognitionPDF
TIP [SST] SST: Spatial and Semantic Transformers for Multi-label Image RecognitionPDF
TNNLS [FDDS] Multilabel Convolutional Network With Feature Denoising and Details SupplementPDF
arxiv
[GATN] Graph Attention Transformer Network for Multi-Label Image ClassificationPDF
Pub. Title Links
TPAMI [P-GCN] Learning Graph Convolutional Networks for Multi-Label Recognition and ApplicationsPDF
TIP [MCAR] Learning to Discover Multi-Class Attentional Regions for Multi-Label Image RecognitionPDF /Code
CVPR [C-Trans] General Multi-label Image Classification with TransformersPDF /Code
ICCV [TDRG] Transformer-based Dual Relation Graph for Multi-label Image RecognitionPDF /Code
ICCV [ASL] Asymmetric Loss For Multi-Label ClassificationPDF /Code
ICCV [CSRA] Residual Attention: A Simple but Effective Method for Multi-Label RecognitionPDF /Code
ICCV [TkML-AP] TkML-AP: Adversarial Attacks to Top-k Multi-Label LearningPDF
AAAI [DSDL] Deep Semantic Dictionary Learning for Multi-label Image ClassificationPDF /Code
AAAI [MGTN] Modular Graph Transformer Networks for Multi-Label Image ClassificationPDF /Code
IJCAI [GM-MLIC] GM-MLIC: Graph Matching based Multi-Label Image ClassificationPDF
ACM MM [M3TR] M3TR: Multi-modal Multi-label Recognition with TransformerPDF /Code
arxiv
MlTr: Multi-label Classification with Transformer
PDF /Code
arxiv
Query2Label: A Simple Transformer Way to Multi-Label Classification
PDF /Code
arxiv
Multi-layered Semantic Representation Network for Multi-label Image Classification
PDF
arxiv
Contrast Learning Visual Attention for Multi Label Classification
PDF
arxiv
Learning Discriminative Representations for Multi-Label Image Recognition
PDF
arxiv
Fine-Grained AutoAugmentation for Multi-Label Classification
PDF
Pub. Title Links
TMM [DER] Disentangling, Embedding and Ranking Label Cues for Multi-Label Image RecognitionPDF
TMM [TS-GCN] Joint Input and Output Space Learning for Multi-Label Image ClassificationPDF
CVPR [PLA] Orderless_Recurrent_Models_for_Multi-Label_ClassificationPDF /Code
CVPR Don’t Judge an Object by Its Context: Learning to Overcome Contextual Bias
PDF /Code
ECCV [ADD-GCN] Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image RecognitionPDF /Code
AAAI [KSSNet] Multi-Label Classification with Label Graph SuperimposingPDF /Code
AAAI Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification
PDF
ACM MM [SGTN] Privacy-Preserving Visual Content Tagging using Graph Transformer NetworksPDF /Code
ACM MM [AdaHGNN] AdaHGNN: Adaptive Hypergraph Neural Networks for Multi-Label Image ClassificationPDF
arxiv
[IA-GCN] Instance-Aware Graph Convolutional Network for Multi-Label ClassificationPDF
Pub. Title Links
CVPR [ML-GCN] Multi-Label Image Recognition with Graph Convolutional NetworksPDF /Code
CVPR [VAC] Visual Attention Consistency under Image Transforms for Multi-Label Image ClassificationPDF /Code
ICCV [SSGRL] Learning Semantic-Specific Graph Representation for Multi-Label Image RecognitionPDF /Code
Pub. Title Links
TPAMI'15 [HCP] HCP: A Flexible CNN Framework for Multi-Label Image ClassificationPDF
AAAI'18 [Order-Free RNN] Order-Free RNN with Visual Attention for Multi-Label ClassificationPDF
AAAI'19 Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition
PDF
IJCAI'18 [MsDPD] Multi-scale and Discriminative Part Detectors Based Features for Multi-label Image ClassificationPDF
ICCV'17 [WILDCAT] WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and SegmentationPDF /Code
ICCV'17 [RDAR] Multi-label Image Recognition by Recurrently Discovering Attentional RegionsPDF /Code
CVPR'17 [SRN] Learning Spatial Regularization with Image-level Supervisions for Multi-label Image ClassificationPDF /Code
CVPR'16 [CNN-RNN] CNN-RNN: A Unified Framework for Multi-label Image ClassificationPDF /Code
Few/Zero-shot Multi-label Image Recognition
Pub. Title Links
TPAMI'20 [KGGR] Knowledge-Guided Multi-Label Few-Shot Learning for General Image RecognitionPDF
TIP'20 Deep Ranking for Image Zero-Shot Multi-Label Classification
PDF
AAAI'22 Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention
PDF
ICCV'21 [BiAM] Discriminative Region-based Multi-Label Zero-Shot LearningPDF /Code
ICCV'21 Semantic Diversity Learning for Zero-Shot Multi-label Classification
PDF /Code
ICCV'21 Interaction Compass: Multi-Label Zero-Shot Learning of Human-Object Interactions via Spatial Relations
PDF
CVPR'20 A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning
PDF
CVPR'18 Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
PDF /Code
CVPR'16 Fast Zero-Shot Image Tagging
PDF /Code
arxiv
Multi-Label Learning from Single Positive Labels
PDF
arxiv
Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention
PDF
Multi-label Image Recognition with Missing Labels
Pub. Title Links
TPAMI'21 [P-GCN] Learning Graph Convolutional Networks for Multi-Label Recognition and ApplicationsPDF
CVPR'22 Large Loss Matters in Weakly Supervised Multi-Label Classification
PDF /Code
AAAI'22 [SARB] Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial LabelsPDF /Code
AAAI'22 [SST] Structured Semantic Transfer for Multi-Label Recognition with Partial LabelsPDF /Code
CVPR'21 Multi-Label Learning from Single Positive Labels
PDF
CVPR'20 Interactive Multi-Label CNN Learning with Partial Labels
PDF
NeurIPS'20 Exploiting weakly supervised visual patterns to learn from partial annotations
PDF
CVPR'19 Learning a Deep ConvNet for Multi-label Classification with Partial Labels
PDF
arxiv
Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations
PDF
arxiv
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels
PDF
arxiv
[ATAM] Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image ClassificationPDF
arxiv
Multi-label Classification with Partial Annotations using Class-aware Selective Loss
PDF /Code
arxiv
Acknowledging the Unknown for Multi-label Learning with Single Positive Labels
PDF
Multi-label Image Recognition with Nosiy Labels
Pub. Title Links
CVPR'19 Weakly Supervised Image Classification through Noise Regularization
PDF
CVPR'17 Learning From Noisy Large-Scale Datasets With Minimal Supervision
PDF
Multi-label Image Recognition with Long-tailed Labels
Pub. Title Links
CVPR'21 Long-Tailed Multi-Label Visual Recognition by Collaborative Training on Uniform and Re-balanced Samplings
PDF
ECCV'20 Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
PDF /Code
Tutorials published on 2021
Talks published on 2022
Pub. Title Links
AI Time 2022 AAAI 专场
部分标签场景下多标签图像识别
BiliBili
COCO2014
Methods Input Size Architecture mAP CP CR CF1 OP OR OF1
ML-GCN (CVPR'19)(448, 448)
CNN-based
83.0
85.1
72.0
78.0
85.8
75.4
80.3
P-GCN （TPAMI'21）(448,448)
CNN-based
83.2
84.9
72.7
78.3
85.0
76.4
80.5
MCAR （TIP'21）(448,448)
CNN-based
83.8
85.0
72.1
78.0
88.0
73.9
80.3
ADD-GCN (ECCV'20)(448, 448)
CNN-based
85.2
84.7
75.9
80.1
84.9
79.4
82.0
SSGRL (ICCV'19)(576,576)
CNN-based
83.8
89.9
68.5
76.8
91.3
70.8
79.7
KGGR (TPAMI'20)(576, 576)
CNN-based
84.3
85.6
72.7
78.6
87.1
75.6
80.9
MCAR （TIP'21）(576,576)
CNN-based
84.5
84.3
73.9
78.7
86.9
76.1
81.1
AdaHGNN （ACM MM'20）(576,576)
CNN-based
85.0
-
-
79.9
-
-
81.8
TDRG （ICCV21）(448,448)
CNN-Transformer
84.6
86.0
73.1
79.0
86.6
76.4
81.2
C-Tran （CVPR'21）(576,576)
CNN-Transformer
85.1
86.3
74.3
79.9
87.7
76.5
81.7
TDRG （ICCV'21）(576,576)
CNN-Transformer
86.0
87.0
74.7
80.4
87.5
77.9
82.4
Note: Only present the precision, recall, and F1-measure for all prediction scores.
Visual Genome 500
Methods Input Size Architecture mAP CP CR CF1 OP OR OF1
SSGRL (ICCV'19)(576,576)
CNN-based
36.6
-
-
-
-
-
-
KGGR (TPAMI'20)(576, 576)
CNN-based
37.4
48.7
12.1
19.4
78.6
17.1
28.1
AdaHGNN (ACM MM'20）(576,576)
CNN-based
38.2
-
-
-
-
-
-
C-Tran （CVPR'21）(576,576)
CNN-Transformer
38.4
49.8
27.2
35.2
66.9
39.2
49.5
Note: Only present the precision, recall, and F1-measure for all prediction scores.
VOC2007
Methods Input Size Architecture mAP CP CR CF1 OP OR OF1
ML-GCN (CVPR'19)(448, 448)
CNN-based
94.0
-
-
-
-
-
-
P-GCN （TPAMI'21）(448,448)
CNN-based
94.3
-
-
-
-
-
-
ADD-GCN (ECCV'20)(448,448)
CNN-based
96.0
-
-
-
-
-
-
SSGRL (ICCV'19)(576,576)
CNN-based
93.4
-
-
-
-
-
-
KGGR (TPAMI'20)(576,576)
CNN-based
93.6
-
-
-
-
-
-
MCAR （TIP'21）(576,576)
CNN-based
94.8
-
-
-
-
-
-
AdaHGNN （ACM MM'20）(576,576)
CNN-based
95.2
-
-
-
-
-
-
TDRG （ICCV21）(448,448)
CNN-Transformer
95.0
-
-
-
-
-
-
Note: Only present the precision, recall, and F1-measure for all prediction scores.
