Awesome

Awesome Weakly-Supervised Multi-Label Learning List

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.

Contents

• 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

<a name="papers-mlml"></a>

Multi-Label Learning with Missing (Partial) Labels (MLML)

2023

• CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification. Rabab Abdelfattah, Qing Guo, Xiaoguang Li, Xiaofeng Wang, and Song Wang. [Code] (CVPR 2023).
• 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]

2022

• 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).

2021

• 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].

2020

• Interactive Multi-Label CNN Learning With Partial Labels. Dat Huynh, Ehsan Elhamifar. (CVPR 2020). [Code].

2019

• 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).

2018

• Deep Generative Models for Weakly-Supervised Multi-Label Classification. Hong-Min Chu, Chih-Kuan Yeh, and Yu-Chiang Frank Wang. (ECCV 2018).

2016

• Multi-label Ranking from Positive and Unlabeled Data. Atsushi Kanehira, and Tatsuya Harada. (CVPR 2016).

<a name="papers-spml"></a>

Single Positive Multi-Label Learning (SPML)

2023

• Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification. Youngwook Kim, Jae Myung Kim, Jieun Jeong, Cordelia Schmid, Zeynep Akata, Jungwoo Lee. (CVPR 2023). [Code].
• Revisiting Pseudo-Label for Single-Positive Multi-Label Learning. Biao Liu, Ning Xu, Jiaqi Lv, Xin Geng. (ICML 2023).
• Semantic Contrastive Bootstrapping for Single-Positive Multi-label Recognition. Cheng Chen, Yifan Zhao, Jia Li. (IJCV). [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].
• Spatial Implicit Neural Representations for Global-Scale Species Mapping. Elijah Cole, Grant Van Horn, Christian Lange, Alexander Shepard, Patrick Leary, Pietro Perona, Scott Loarie, Oisin Mac Aodha. (ICML 2023). [GitHub].
• Can Class-Priors Help Single-Positive Multi-Label Learning?. Biao Liu, Jie Wang, Ning Xu, Xin Geng. (arXiv 2023).
• 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).
• Towards Diverse Temporal Grounding under Single Positive Labels. Hao Zhou, Chongyang Zhang, Yanjun Chen, Chuanping Hu. (arXiv 2023).
• Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning. Xin Xing, Zhexiao Xiong, Abby Stylianou, Srikumar Sastry, Liyu Gong, Nathan Jacobs. (arXiv 2023). [Code]

2022

• 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).

2021

• Multi-Label Learning from Single Positive Labels. Elijah Cole, Oisin Mac Aodha, Titouan Lorieul, Pietro Perona, Dan Morris, Nebojsa Jojic. (CVPR 2021). [Code].

<a name="papers-ssmll"></a>

Semi-Supervised Multi-Label Learning (SSMLL)

• 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].
• Conditional consistency regularization for semi-supervised multi-label image classification. Zhengning Wu, Tianyu He, Xiaobo Xia, Jun Yu, Xu Shen, Tongliang Liu. (TMM).
• Dual Relation Semi-Supervised Multi-Label Learning. Lichen Wang, Yunyu Liu, Can Qin, Gan Sun, Yun Fu. (AAAI 2020).

<a name="papers-pml"></a>

Partial Multi-Label Learning (PML)

• 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).

Multi-Label Learning with Noisy Labels (MLNL)

• 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).

<a name="papers-other"></a>

Other Settings

2023

• 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].

2022

• Multi-label Iterated Learning for Image Classification with Label Ambiguity. Sai Rajeswar, Pau Rodríguez, Soumye Singhal, David Vazquez, Aaron Courville. (CVPR 2022).