Applications

Psychology

Facial Emotion Recognition

LDL-ALSG

class pyldl.applications.facial_emotion_recognition.LDL_ALSG(*args, **kwargs)

LDL-ALSG is proposed in [APP-CWC+20].

extract_ck_plus

pyldl.applications.facial_emotion_recognition.extract_ck_plus(input_dir, output_dir, openface_path, basic=True)

load_bu_3dfe

pyldl.applications.facial_emotion_recognition.load_bu_3dfe(path, size=(256, 256))

load_ck_plus

pyldl.applications.facial_emotion_recognition.load_ck_plus(image_dir, feature_dir=None, size=(196, 256), basic=True)

load_jaffe

pyldl.applications.facial_emotion_recognition.load_jaffe(path, indices=None, size=(256, 256))

load_jaffe_single

pyldl.applications.facial_emotion_recognition.load_jaffe_single(path, i, size=(256, 256))

visualization

pyldl.applications.facial_emotion_recognition.visualization(image, distribution, real, style_real='distribution', labels=['HA', 'SA', 'SU', 'AN', 'DI', 'FE'])

References

[APP-CWC+20]

Shikai Chen, Jianfeng Wang, Yuedong Chen, Zhongchao Shi, Xin Geng, and Yong Rui. Label distribution learning on auxiliary label space graphs for facial expression recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13984–13993. 2020. URL: https://doi.org/10.1109/CVPR42600.2020.01400.

Further Reading

[APP-BAJB22]

Morgan Buisson, Pablo Alonso-Jiménez, and Dmitry Bogdanov. Ambiguity modelling with label distribution learning for music classification. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 611–615. 2022. URL: https://doi.org/10.1109/ICASSP43922.2022.9747467.

[APP-SWPX22]

Shijing Si, Jianzong Wang, Junqing Peng, and Jing Xiao. Towards speaker age estimation with label distribution learning. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 4618–4622. 2022. URL: https://doi.org/10.1109/ICASSP43922.2022.9746378.

[APP-SWL+22]

Jianjian Shao, Zhenqian Wu, Yuanyan Luo, Shudong Huang, Xiaorong Pu, and Yazhou Ren. Self-paced label distribution learning for in-the-wild facial expression recognition. In Proceedings of the ACM International Conference on Multimedia, 161–169. 2022. URL: https://doi.org/10.1145/3503161.3547960.

[APP-CGX+21]

Jingying Chen, Chen Guo, Ruyi Xu, Kun Zhang, Zongkai Yang, and Honghai Liu. Toward children's empathy ability analysis: joint facial expression recognition and intensity estimation using label distribution learning. IEEE Transactions on Industrial Informatics, 18(1):16–25, 2021. URL: https://doi.org/10.1109/TII.2021.3075989.

[APP-ZZG21]

Huiying Zhang, Yu Zhang, and Xin Geng. Practical age estimation using deep label distribution learning. Frontiers of Computer Science, 15:1–6, 2021. URL: https://doi.org/10.1007/s11704-020-8272-4.

[APP-SMT+20]

Atsuya Sakata, Yasushi Makihara, Noriko Takemura, Daigo Muramatsu, and Yasushi Yagi. How confident are you in your estimate of a human age? uncertainty-aware gait-based age estimation by label distribution learning. In Proceedings of the IEEE International Joint Conference on Biometrics, 1–10. 2020. URL: https://doi.org/10.1109/IJCB48548.2020.9304914.

[APP-WLG+20]

Xin Wen, Biying Li, Haiyun Guo, Zhiwei Liu, Guosheng Hu, Ming Tang, and Jinqiao Wang. Adaptive variance based label distribution learning for facial age estimation. In Proceedings of the European Conference on Computer Vision, 379–395. 2020. URL: https://doi.org/10.1007/978-3-030-58592-1_23.

[APP-ZLLL20]

Zhaoli Zhang, Chenghang Lai, Hai Liu, and You-Fu Li. Infrared facial expression recognition via gaussian-based label distribution learning in the dark illumination environment for human emotion detection. Neurocomputing, 409:341–350, 2020. URL: https://doi.org/10.1016/j.neucom.2020.05.081.

[APP-GZWG18]

Bin-Bin Gao, Hong-Yu Zhou, Jianxin Wu, and Xin Geng. Age estimation using expectation of label distribution learning. In Proceedings of the International Joint Conference on Artificial Intelligence, 712–718. 2018. URL: https://doi.org/10.24963/ijcai.2018/99.

Medicine

Lesion Counting

LDL-ACNE

class pyldl.applications.lesion_counting.LDL_ACNE(*args, **kwargs)

This approach is proposed in paper [APP-WWL+19].

load_acne04

pyldl.applications.lesion_counting.load_acne04(path, index=0, mode='train')

preprocessing

pyldl.applications.lesion_counting.preprocessing(labels, sigma=3.0)

visualization

pyldl.applications.lesion_counting.visualization(X, grade, count, grade_real=None, count_real=None, colors=['#00FF00', '#FFFF00', '#FF5500', '#FF0000'], grade_desc=['Mild', 'Moderate', 'Severe', 'Very Severe'])

References

[APP-WWL+19]

Xiaoping Wu, Ni Wen, Jie Liang, Yu-Kun Lai, Dongyu She, Ming-Ming Cheng, and Jufeng Yang. Joint acne image grading and counting via label distribution learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 10642–10651. 2019. URL: https://doi.org/10.1109/ICCV.2019.01074.

Further Reading

[APP-LLL+23]

Xiangyu Li, Xinjie Liang, Gongning Luo, Wei Wang, Kuanquan Wang, and Shuo Li. Ambiguity-aware breast tumor cellularity estimation via self-ensemble label distribution learning. Medical Image Analysis, 90:102944, 2023. URL: https://doi.org/10.1016/j.media.2023.102944.

[APP-LLW+23]

Xiangyu Li, Gongning Luo, Wei Wang, Kuanquan Wang, and Shuo Li. Curriculum label distribution learning for imbalanced medical image segmentation. Medical Image Analysis, 89:102911, 2023. URL: https://doi.org/10.1016/j.media.2023.102911.

[APP-QHZ23]

Keke Qin, Wu Huang, and Tao Zhang. Multitask deep label distribution learning for blood pressure prediction. Information Fusion, 95:426–445, 2023. URL: https://doi.org/10.1016/j.inffus.2023.02.019.

[APP-WZJ+22]

Jun Wang, Fengyexin Zhang, Xiuyi Jia, Xin Wang, Han Zhang, Shihui Ying, Qian Wang, Jun Shi, and Dinggang Shen. Multi-class asd classification via label distribution learning with class-shared and class-specific decomposition. Medical Image Analysis, 75:102294, 2022. URL: https://doi.org/10.1016/j.media.2021.102294.

[APP-CCJ+21]

Chao Chen, Zhihong Chen, Xinyu Jin, Lanjuan Li, William Speier, and Corey W Arnold. Attention-guided discriminative region localization and label distribution learning for bone age assessment. IEEE Journal of Biomedical and Health Informatics, 26(3):1208–1218, 2021. URL: https://doi.org/10.1109/JBHI.2021.3095128.

Text/Natural Language

Emphasis Selection

DL-BiLSTM

class pyldl.applications.emphasis_selection.DL_BiLSTM(*args, **kwargs)

This approach is proposed in paper [APP-SDA+19].

load_glove

pyldl.applications.emphasis_selection.load_glove(path, tokenizer, embedding_dim=100)

load_semeval2020

pyldl.applications.emphasis_selection.load_semeval2020(path)

preprocessing

pyldl.applications.emphasis_selection.preprocessing(words, freqs, tokenizer=None, maxlen=None)

visualization

pyldl.applications.emphasis_selection.visualization(words, y=None, threshold=0.2, r=255, g=68, b=68)

References

[APP-SDA+19]

Amirreza Shirani, Franck Dernoncourt, Paul Asente, Nedim Lipka, Seokhwan Kim, Jose Echevarria, and Thamar Solorio. Learning emphasis selection for written text in visual media from crowd-sourced label distributions. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 1167–1172. 2019. URL: https://doi.org/10.18653/v1/P19-1112.

Computer Vision

Further Reading

[APP-MLM+23]

Haoyu Ma, Ningning Lu, Junjun Mei, Tao Guan, Yu Zhang, and Xin Geng. Label distribution learning for scene text detection. Frontiers of Computer Science, 17(6):176339, 2023. URL: https://doi.org/10.1007/s11704-022-1446-5.

[APP-XLZ+23]

Hang Xu, Xinyuan Liu, Qiang Zhao, Yike Ma, Chenggang Yan, and Feng Dai. Gaussian label distribution learning for spherical image object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1033–1042. 2023. URL: https://doi.org/10.1109/CVPR52729.2023.00106.

[APP-CLWC22]

Zhiwen Cao, Dongfang Liu, Qifan Wang, and Yingjie Chen. Towards unbiased label distribution learning for facial pose estimation using anisotropic spherical gaussian. In Proceedings of the European Conference on Computer Vision, 737–753. 2022. URL: https://doi.org/10.1007/978-3-031-19775-8_43.

[APP-LG19]

Miaogen Ling and Xin Geng. Indoor crowd counting by mixture of gaussians label distribution learning. IEEE Transactions on Image Processing, 28(11):5691–5701, 2019. URL: https://doi.org/10.1109/TIP.2019.2922818.

[APP-SG19]

Kai Su and Xin Geng. Soft facial landmark detection by label distribution learning. In Proceedings of the AAAI Conference on Artificial Intelligence, 5008–5015. 2019. URL: https://doi.org/10.1609/aaai.v33i01.33015008.

[APP-YCZ+18]

Jufeng Yang, Liyi Chen, Le Zhang, Xiaoxiao Sun, Dongyu She, Shao-Ping Lu, and Ming-Ming Cheng. Historical context-based style classification of painting images via label distribution learning. In Proceedings of the ACM International Conference on Multimedia, 1154–1162. 2018. URL: https://doi.org/10.1145/3240508.3240593.

[APP-GL17]

Xin Geng and Miaogen Ling. Soft video parsing by label distribution learning. In Proceedings of the AAAI Conference on Artificial Intelligence, 1331–1337. 2017. URL: https://doi.org/10.1609/aaai.v31i1.10729.

[APP-RG17]

Yi Ren and Xin Geng. Sense beauty by label distribution learning. In Proceedings of the International Joint Conference on Artificial Intelligence, 2648–2654. 2017. URL: https://doi.org/10.24963/ijcai.2017/369.

[APP-ZWG15]

Zhaoxiang Zhang, Mo Wang, and Xin Geng. Crowd counting in public video surveillance by label distribution learning. Neurocomputing, 166:151–163, 2015. URL: https://doi.org/10.1016/j.neucom.2015.03.083.