A curated list of papers & resources related to weakly supervised multi-label learning, including multi-label learning with missing labels, single positive multi-label learning, partial multi-label learning, and multi-label learning with noisy labels.
Note that:
- This list only contains deep learning based methods.
- This list is not exhaustive.
- Papers use alphabetical order for fairness.
- Multi-Label Learning with Missing (Partial) Labels (MLML)
- Single Positive Multi-Label Learning (SPML)
- Semi-Supervised Multi-Label Learning (SSMLL)
- Partial Multi-Label Learning (PML)
- Multi-Label Learning with Noisy Labels (MLNL)
- Other Settings
- Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels. Zixuan Ding, Ao Wang, Hui Chen, Qiang Zhang, Pengzhang Liu, Yongjun Bao, Weipeng Yan, Jungong Han. [Code] (CVPR 2023).
- CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification. Rabab Abdelfattah, Qing Guo, Xiaoguang Li, Xiaofeng Wang, and Song Wang. [Code]
- DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations. Ximeng Sun, Ping Hu, Kate Saenko. (NeurIPS 2022). [Code].
- G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification. Rabab Abdelfattah, Xin Zhang, Mostafa M. Fouda, Xiaofeng Wang, Song Wang. (BMVC 2022).
- Multi-label Classification with Partial Annotations using Class-aware Selective Loss. Emanuel Ben-Baruch, Tal Ridnik. (CVPR 2022). [Code].
- PLMCL: Partial-Label Momentum Curriculum Learning for Multi-label Image Classification. Rabab Abdelfattah, Xin Zhang, Zhenyao Wu, Xinyi Wu, Xiaofeng Wang, and Song Wang. (ECCVW 2022).
- Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcity. Nanqing Dong, Jiayi Wang, Irina Voiculescu. (CVPR 2022). [Code].
- Robust Recurrent Classifier Chains for Multi-Label Learning with Missing Labels. Walter Gerych, Thomas Hartvigsen, Luke Buquicchio, Emmanuel Agu, Elke Rundensteiner. (CIKM 2022).
- Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels. Tao Pu, Tianshui Chen, Hefeng Wu, Liang Lin. (AAAI 2022). [Code].
- Structured Semantic Transfer for Multi-Label Recognition with Partial Labels. Tianshui Chen, Tao Pu, Hefeng Wu, Yuan Xie, Liang Lin. (AAAI 2022). [Code].
- Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels. Tianshui Chen, Tao Pu, Lingbo Liu, Yukai Shi, Zhijing Yang, Liang Lin. (arXiv 2022).
-
General multi-label image classification with transformers. Jack Lanchantin, Tianlu Wang, Vicente Ordonez, Yanjun Qi. (CVPR 2021). [Code].
-
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels. Youcai Zhang, Yuhao Cheng, Xinyu Huang, Fei Wen, Rui Feng, Yaqian Li, and Yandong Guo. (arXiv 2021). [Code].
- Interactive Multi-Label CNN Learning With Partial Labels. Dat Huynh, Ehsan Elhamifar. (CVPR 2020). [Code].
-
Learning a Deep ConvNet for Multi-label Classification with Partial Labels. Thibaut Durand, Nazanin Mehrasa, Greg Mori. (CVPR 2019).
-
A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision . Cheng-Yu Hsieh, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama. (ICLRW 2019).
- Deep Generative Models for Weakly-Supervised Multi-Label Classification. Hong-Min Chu, Chih-Kuan Yeh, and Yu-Chiang Frank Wang. (ECCV 2018).
- Multi-label Ranking from Positive and Unlabeled Data. Atsushi Kanehira, and Tatsuya Harada. (CVPR 2016).
- Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification. Youngwook Kim, Jae Myung Kim, Jieun Jeong, Cordelia Schmid, Zeynep Akata, and Jungwoo Lee. (CVPR 2023). [Code].
- Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations. Thomas Verelst, Paul K. Rubenstein, Marcin Eichner, Tinne Tuytelaars, Maxim Berman. (WACV 2023). [Code].
- Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels. Xiang Li, Xinrui Wang, Songcan Chen. (arXiv 2023).
- Acknowledging the Unknown for Multi-label Learning with Single Positive Labels. Donghao Zhou, Pengfei Chen, Qiong Wang, Guangyong Chen, Pheng-Ann Heng. (ECCV 2022). [Code].
- Hyperspherical Learning in Multi-Label Classification. Bo Ke, Yunquan Zhu, Mengtian Li, Xiujun Shu, Ruizhi Qiao, and Bo Ren. (ECCV 2022). [Code].
- Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels. Ming-Kun Xie, Jia-Hao Xiao, Sheng-Jun Huang. (NeurIPS 2022). [Code].
- Large Loss Matters in Weakly Supervised Multi-Label Classification. Youngwook Kim, Jae Myung Kim, Zeynep Akata, Jungwoo Lee. (CVPR 2022). [Code].
- One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement. Ning Xu, Congyu Qiao, Jiaqi Lv, Xin Geng, Min-Ling Zhang. (NeurIPS 2022). [Code].
- A Patch-Based Architecture for Multi-Label Classification from Single Label Annotations. Warren Jouanneau, Aurélie Bugeau, Marc Palyart, Nicolas Papadakis, Laurent Vézard. (arXiv 2022).
- Multi-Label Learning from Single Positive Labels. Elijah Cole, Oisin Mac Aodha, Titouan Lorieul, Pietro Perona, Dan Morris, Nebojsa Jojic. (CVPR 2021). [Code].
-
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning.. Ming-Kun Xie, Jia-Hao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang. (NeurIPS 2023). [Code].
-
Dual Relation Semi-Supervised Multi-Label Learning. Lichen Wang, Yunyu Liu, Can Qin, Gan Sun, Yun Fu. (AAAI 2020).
-
A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation.. Feng Sun, Ming-Kun Xie, and Sheng-Jun Huang. (arXiv 2022).
-
Partial Multi-Label Learning with Meta Disambiguation. Ming-Kun Xie, Feng Sun, and Sheng-Jun Huang. (KDD 2021).
-
CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise. Ming-Kun Xie, Sheng-Jun Huang. (TPAMI 2022). [Code].
-
Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning. Shikun Li, Xiaobo Xia, Hansong Zhang, Yibing Zhan, Shiming Ge, Tongliang Liu. (NeurIPS 2022). [Code].
-
Weakly Supervised Image Classification through Noise Regularization. Mengying Hu, Hu Han, Shiguang Shan, Xilin Chen. (CVPR 2019).
-
Evaluating Multi-label Classifiers with Noisy Labels. Wenting Zhao, Carla Gomes. (arXiv 2021).
-
Learning from Noisy Labels with Noise Modeling Network. Zhuolin Jiang, Jan Silovsky, Man-Hung Siu, William Hartmann, Herbert Gish, Sancar Adali. (arXiv 2020).
-
Multi-Label Knowledge Distillation. Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang(ICCV 2023). [Code].
-
Self-Supervised Pyramid Representation Learning for Multi-Label Visual Analysis and Beyond. Cheng-Yen Hsieh∗ Chih-Jung Chang∗ Fu-En Yang Yu-Chiang Frank Wang. (WACV 2023). [Code].
- Multi-label Iterated Learning for Image Classification with Label Ambiguity. Sai Rajeswar, Pau Rodríguez, Soumye Singhal, David Vazquez, Aaron Courville. (CVPR 2022).