See Google Scholar or DBLP for more detail.

*  Co-first author; ** Corresponding author

Preprints

1. Ruohuan Fang, Guansong Pang**, Lei Zhou, Xiao Bai, and Jin Zheng. "Unsupervised Recognition of Unknown Objects for Open-World Object Detection." arXiv preprint arXiv:2308.16527 (2023). [pdf] [code]

2. Tianqi Li, Guansong Pang**, Xiao Bai, Jin Zheng, Lei Zhou, and Xin Ning. "Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds." arXiv preprint arXiv:2307.03416 (2023). [pdf] [code]

3. Chaoxi Niu, Guansong Pang**, and Ling Chen. "Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning." arXiv preprint arXiv:2301.13340 (2023). [pdf] [code]

4. Choubo Ding, Guansong Pang, and Chunhua Shen. "Background Matters: Enhancing Out-of-distribution Detection with Domain Features". arXiv preprint:2303.08727 (2023). [pdf] [code]

5. Peng Wu, Xuerong Zhou, Guansong Pang, Lingru Zhou, Qingsen Yan, Peng Wang, and Yanning Zhang. "VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection." arXiv preprint arXiv:2308.11681 (2023). [pdf] [code]

6. Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Zhang et al. "Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects." arXiv preprint arXiv:2306.10125 (2023).

7. Hongzuo Xu, Yijie Wang, Songlei Jian, Qing Liao, Yongjun Wang, and Guansong Pang. "Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection." arXiv preprint:2207.12201 (2022). [pdf] [code]

8. Jitendra Singh Malik, Guansong Pang**, and Anton van den Hengel. "Deep Learning for Hate Speech Detection: A Comparative Study." arXiv preprint :2202.09517 (2022). [pdf] [code] [data]

9. Guansong Pang, Choubo Ding, Chunhua Shen, and Anton van den Hengel. "Explainable Deep Few-shot Anomaly Detection with Deviation Networks." arXiv preprint:2108.00462 (2021). [pdf] [code] [data]

Tutorials

1. Guansong Pang, Joey Tianyi Zhou, Radu Tudor Ionescu, Yu Tian, and Kihyuk Sohn. "Recent Advances in Anomaly Detection". In: CVPR'23. Vancouver, Canada. [website] [slides] [video]

2. Ye Zhu, Guansong Pang, Sutharshan Rajasegarar, Xuyun Zhang, and Gang Li. "Moving Beyond Traditional Anomaly Detection." In: IJCAI'23. Macao, China. [webiste]

3. Guansong Pang and Charu Aggarwal. "Toward Explainable Deep Anomaly Detection". In: KDD'21, pp. 4056-4057, 2021. [pdf] [slides] [website]

4. Guansong Pang, Longbing Cao,  and Charu Aggarwal. "Deep Learning for Anomaly Detection: Challenges, Methods, and Opportunities". In: WSDM'21, pp. 1127–1130, 2021. Jerusalem, Israel. [pdf] [slides] [video] [website]

5. Longbing Cao, Philip Yu, and Guansong Pang. "Behavior Analytics: Methods and Applications". In: KDD'18. London, United Kingdom. [slides] [website]

6. Longbing Cao, Philip Yu, Guansong Pang, and Chengzhang Zhu. "Non-IID Learning". In: KDD'17. Halifax, Canada. [slides] [website]

Editorials

• 1. Guansong Pang. "Artificial Intelligence for Natural Disaster Management." IEEE Intelligent Systems 37.6 (2022): 3-6.

  2. Guansong Pang, Charu Aggarwal, Chunhua Shen, and Nicu Sebe. "Editorial: Deep Learning for Anomaly Detection".  IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 33.6 (2022):  2282 - 2286. [pdf]

  3. Guansong Pang. "The AI Chip Race". IEEE Intelligent Systems 37.2 (2022): 111-112. [pdf]

  4. Guansong Pang. "AI in Beijing 2022 Olympic Winter Games". IEEE Intelligent Systems 37.1 (2022): 110-110. [pdf]

  5. Guansong Pang, Fabrizio Angiulli, Mihai Cucuringu, and Huan Liu. "Guest Editorial: Non-IID Outlier Detection in Complex Contexts". IEEE Intelligent Systems 36.3 (2021): 3-4. [pdf]

Conference papers

• 1. Hezhe Qiao and Guansong Pang**. "Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection." In: NeurIPS'23, to appear. Acceptance rate:  26.1% (3221/12343). [pdf] [code]

  2. Jiaxi Li, Guansong Pang, Ling Chen, and Mohammad-Reza Namazi-Rad. "HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks." In: DSAA'23, to appear. [pdf] [code]

  3. Tri Cao, Jiawen Zhu, and Guansong Pang**. "Anomaly Detection under Distribution Shift." In: ICCV'23, to appear. [pdf] [code]

  4. Yuyuan Liu, Choubo Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, and Gustavo Carneiro. "Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation." In: ICCV'23, to appear. [pdf] [code]

  5. Cheng Yan, Zhang Shiyu, Yang Liu, Guansong Pang**, and Wenjun Wang. "Prediction Diffusion Model for Video Anomaly Detection". In: ICCV'23, to appear. [pdf] [code]

  6. Chaoxi Niu, Guansong Pang**, and Ling Chen. "Graph-level Anomaly Detection via Hierarchical Memory Networks". In: ECML/PKDD'23. Acceptance rate:  24.0% (199/830). [pdf] [code]

  7. Guansong Pang, Chunhua Shen, Huidong Jin, and Anton van den Hengel. "Deep Weakly-supervised Anomaly Detection". In: KDD'23. Acceptance rate: 22.1% (313/1416).  [pdf] [code]

  8. Haoyu Wang*, Guansong Pang*, Peng Wang*, Lei Zhang, Wei Wei, and Yanning Zhang. "Glocal Energy-based Learning for Few-Shot Open-Set Recognition". In: CVPR'23. Acceptance rate: 25.8% (2360/9155). [pdf] [code]

  9. Zhong Zhuang, Kai Ming Ting, Guansong Pang, and Shuaibin Song. "Subgraph Centralization: A Necessary Step in Graph Anomaly Detection. " In: SDM'23. Acceptance rate: 27.4% (105/459). [pdf][code]

  10. Qizhou Wang, Guansong Pang**, Mahsa Salehi, Wray Buntine, and Christopher Leckie. "Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment." In: AAAI'23. Acceptance rate: 19.6% (1721/8777). [pdf][code][data]

  11. Yu Tian, Yuyuan Liu, Guansong Pang**, Fengbei Liu, Yuanhong Chen, and Gustavo Carneiro. " Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes." In: ECCV'22 (Oral), pp.246-263, 2022. Acceptance rate: 28% (1650/5803). Oral: 2.7% (158/5803). [pdf] [code]

  12. Yu Tian, Guansong Pang, Fengbei Liu, Yuyuan Liu, Chong Wang, Yuanhong Chen, Johan W. Verjans, and Gustavo Carneiro. "Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection." In: MICCAI'22, pp. 88-98, 2022. Acceptance rate: 31.3% (574/1831). [pdf] [code] 

  13. Choubo Ding*, Guansong Pang*, and Chunhua Shen. "Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection". In: CVPR'22, pp. 7388-7398, 2022. Acceptance rate: 25.33% (2067/8161). [pdf] [code] [data]

  14. Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley, and Anton Van Den Hengel. "Deep Depression Prediction on Longitudinal Data via Joint Anomaly Ranking and Classification." In: PAKDD'22, pp. 236-248, 2022. Acceptance rate: 19.30% (121/627). [pdf]

  15. Yuanhong Chen*, Yu Tian*, Guansong Pang, and Gustavo Carneiro. "Deep One-class Classification via Interpolated Gaussian Descriptor". In: AAAI'22, 36 (1), 383-392, 2022. Acceptance rate: 14.96% (1349/9020). Oral. [pdf] [code] [data]

  16. Rongrong Ma, Guansong Pang**, Ling Chen, and Anton van den Hengel. "Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation." In: WSDM'22, pp. 704-714, 2022. Acceptance rate: 20.23% (159 /786 ). [pdf] [code] [data]

  17. Guansong Pang, Anton van den Hengel, Chunhua Shen, and Longbing Cao. "Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data." In: KDD'21 , pp. 1298-1308, 2021. Acceptance rate: 15.4% (238/1541). [pdf]

  18. Cheng Yan, Guansong Pang**, Jile Jiao, Xiao Bai, Xuetao Feng, and Chunhua Shen. "Occluded Person Re-Identification with Single-scale Global Representations." In: ICCV'21, pp. 11875-11884, 2021. Acceptance rate: 25.9% (1617/6236). Oral: 3.4% (210/6236). [pdf][code]

  19. Cheng Yan*, Guansong Pang*, Lei Wang, Jile Jiao, Xuetao Feng, Chunhua Shen, and Jingjing Li. "BV-Person: A Large-scale Dataset for Bird-view Person Re-identification" In: ICCV'21, pp. 10943-10952, 2021. Acceptance rate: 25.9% (1617/6236). [pdf]

  20. Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, and Gustavo Carneiro. "Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning".  In: ICCV'21, pp. 4975-4986, 2021. Acceptance rate: 25.9% (1617/6236). [pdf][code][data]

  21. Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W. Verjans, Rajvinder Singh, and Gustavo Carneiro. "Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images." In: MICCAI'21. Acceptance rate: 32.7% (533/1631)  [pdf] [code]

  22. Hu Wang*, Guansong Pang*, Chunhua Shen, and Congbo Ma. "Unsupervised Representation Learning by Predicting Random Distances", In: IJCAI'20. Yokohama, Japan. Acceptance rate: 12.6% (592/4717). [pdf] [code] 

  23. Guansong Pang*, Cheng Yan*, Chunhua Shen, Xiao Bai, and Anton van den Hengel. "Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection", In: CVPR'20. Seattle, US. Acceptance rate: 22.1% (1470/6656). [pdf]

  24. Jingjing Zhao, Yao Yang, Guansong Pang, Lei Lv, Hong Shang, Zhongqian Sun, and Wei Yang. "Learning Discriminative Neural Sentiment Units for Semi-supervised Target-level Sentiment Classification", In: 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'20),pp. 798-810, 2020. Singapore. Acceptance rate: 21.5% (135/628). [pdf]

  25. Cheng Yan, Guansong Pang, Xiao Bai, Chunhua Shen, Jun Zhou, and Edwin Hancock. "Deep Hashing by Discriminating Hard Examples", In: 27th ACM International Conference on Multimedia (ACM Multimedia'19), pp. 1535-1542. Nice, France. Acceptance rate: 26.9% (252/936). [pdf]

  26. Guansong Pang, Chunhua Shen, and Anton van den Hengel. "Deep Anomaly Detection with Deviation Networks", In: 25th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19). Anchorage, US. Acceptance rate: 9.2% (110/1200) (Oral presentation). [pdf] [code] [video] [slides] [data]

  27. Guansong Pang, Longbing Cao, Ling Chen, and Huan Liu. "Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection", In: 24th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'18). London, UK. Acceptance rate: 10.9%(107/983) (Oral presentation) . [pdf] [code] [video] [data]

  28. Guansong Pang, Longbing Cao, Ling Chen, Defu Lian and Huan Liu. "Sparse Modeling-based Sequential Ensemble Learning for Effective Outlier Detection in High-dimensional Numeric Data", In: 32nd AAAI Conference on Artificial Intelligence (AAAI'18). New Orleans, US. Acceptance rate: 24.6% (933/3800). [pdf] [code]

  29. Guansong Pang, Hongzuo Xu, Longbing Cao, Wentao Zhao. "Selective Value Coupling Learning for Detecting Outliers in High-Dimensional Categorical Data". In: 26th ACM International Conference on Information and Knowledge Management (CIKM'17). Singapore. Acceptance rate: 20.0% (171/855) (Long paper). [pdf] [slides] [code] [data]

  30. Guansong Pang, Longbing Cao, Ling Chen and Huan Liu. "Learning Homophily Couplings from Non-IID Data for Joint Feature Selection and Noise-Resilient Outlier Detection". In: 26th International Joint Conference on Artificial Intelligence (IJCAI'17). Acceptance rate: 26.0% (660/2540). [pdf] [slides] [code] [data]

  31. Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu and Hang Gao. "Embedding-based Representation of Categorical Data by Hierarchical Value Coupling Learning". In: 26th International Joint Conference on Artificial Intelligence (IJCAI'17). Acceptance rate: 26.0% (660/2540). [pdf] [code]

  32. Guansong Pang, Longbing Cao, Ling Chen and Huan Liu. "Unsupervised Feature Selection for Outlier Detection by Modelling Hierarchical Value-Feature Couplings." In: 2016 IEEE International Conference on Data Mining (ICDM'16). Barcelona, Spain. Acceptance rate: 8.6% (78/904) (Full paper). [pdf] [code] [data]

  33. Guansong Pang, Longbing Cao, and Ling Chen. “Outlier Detection in Complex Categorical Data by Modelling Feature Value Couplings”. In: 25th International Joint Conference on Artificial Intelligence (IJCAI'16). AAAI Press, pp. 1902–1908, 2016. New York City, US. Acceptance rate: 24.0% (551/2294). [pdf] [slides] [code] [data]

Journal papers

• 1. Hongzuo Xu, Yijie Wang, Guansong Pang, Songlei Jian, Ning Liu, and Yongjun Wang. "RoSAS: Deep Semi-supervised Anomaly Detection with Contamination-resilient Continuous Supervision." Information Processing & Management (IP&M) , 2023. To appear. [pdf] [code]

  2. Yu Tian*, Fengbei Liu*, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh, and Gustavo Carneiro. "Self-supervised Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images." Medical Image Analysis (MedIA), 2023. To appear. [pdf] [code]

  3. Hongzuo Xu, Guansong Pang**, Yijie Wang, and Yongjun Wang. "Deep Isolation Forest for Anomaly Detection." IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023. To appear. [pdf] [code] [data]

  4. Jiaying Liu, Feng Xia, Jing Ren, Bo Xu, Guansong Pang, and Lianhua Chi. "MIRROR: Mining Implicit Relationships via Structure-Enhanced Graph Convolutional Networks". ACM Transactions on Knowledge Discovery from Data (TKDD)  17.4, Article 55 (February 2023), 24 pages. [pdf]

  5. Xiaoyu Xu, Guansong Pang**, Di Wu, and Mingsheng Shang. "Joint Hyperbolic and Euclidean Geometry Contrastive Graph Neural Networks". Information Sciences (INS), 609 (2022) 799–815, 2022. [pdf] [code]

  6. Qing Li, Guansong Pang, and Mingsheng Shang. "An Efficient Annealing-Assisted Differential Evolution for Multi-parameter Adaptive Latent Factor Analysis". Journal of Big Data, 9:95, 2022. [pdf]

  7. Cheng Yan*, Guansong Pang*, Xiao Bai, Jun Zhou, and Lin Gu.  “Beyond Triplet Loss:  Person Re-identification with Fine-grained Difference-aware Pairwise Loss.” IEEE Transactions on Multimedia (TMM), 24, pp. 1665-1677, 2022. [pdf] [preprint] [code] ESI Highly Cited Papers (top 1%) 

  8. Guansong Pang, Chunhua Shen, Longbing Cao, and Anton van den Hengel. "Deep learning for anomaly detection: A review". ACM Computing Survey (CSUR)  54, 2, Article 38 (January 2022), 38 pages. [pdf] [preprint] [tutorial] ESI Highly Cited Papers (top 1%) & ESI Hot Papers (top 0.1%)

  9. Guansong Pang, Longbing Cao, and Ling Chen. “Homophily outlier detection in non-IID categorical data". Data Mining and Knowledge Discovery (DMKD), 35(4): 1163-1224 (2021). [pdf] [preprint] [code]

  10. Jianpeng Zhang, Yutong Xie, Guansong Pang, Zhibin Liao, Johan Verjans, Wenxin Li, Zongji Sun, Jian He, Yi Li, Chunhua Shen, Yong Xia. "Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection". IEEE Transactions on Medical Imaging (TMI), 40.3, pp. 879 - 890, 2021. [pdf] [preprint] ESI Highly Cited Papers (top 1%)

  11. Guansong Pang and Longbing Cao. "Heterogeneous Univariate Outlier Ensembles in Multidimensional Data". ACM Transactions on Knowledge Discovery from Data (TKDD), 14.6, Article 68, 27 pages, 2020. [pdf] [code]

  12. Dandan Zheng*, Guansong Pang*, Bo Liu, Lihong Chen and Jian Yang. "Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors". Bioinformatics, 36.12, pp. 3693–3702, 2020. [pdf] [code] [data]

  13. Songlei Jian, Guansong Pang, Longbing Cao, Kai Lu, and Hang Gao. “ CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning". IEEE Transactions on Knowledge and Data Engineering (TKDE), 31.5, pp. 853-866, 2019. [code]

  14. Guansong Pang, Kai Ming Ting, David Albrecht and Huidong Jin. “ ZERO++: Harnessing the Power of Zero Appearances to Detect Anomalies in Large-Scale Data Sets”. Journal of Artificial Intelligence Research (JAIR)  57, pp. 593–620, 2016. [pdf] [code]

  15. Guansong Pang, Huidong Jin, and Shengyi Jiang. “CenKNN: a scalable and effective text classifier”. Data Mining and Knowledge Discovery (DMKD) 29.3, pp. 593–625, 2015.

  16. Guansong Pang and Shengyi Jiang. “A generalized cluster centroid based classifier for text categorization”. Information Processing & Management (IP&M) 49.2, pp. 576–586, 2013.

  17. Shengyi Jiang, Guansong Pang, Meiling Wu, and Limin Kuang. “An improved K-nearest-neighbor algorithm for text categorization”. Expert Systems with Applications (ESWA) 39.1, pp. 1503–1509, 2012.

Workshops, Posters, and Abstracts

• 1. Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W. Verjans, and Gustavo Carneiro. "Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder." In: the 14th Workshop on Machine Learning in Medical Imaging (MICCAI 2023 workshop). 2023. [pdf]

  2. Qing Li, Xiao Huang, Ninghao Liu, Yuxiao Dong, and Guansong Pang. "International Workshop on Learning with Knowledge Graphs: Construction, Embedding, and Reasoning." In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (WSDM'23), pp. 1273-1274. 2023.

  3. Guansong Pang, Jundong Li, Anton van den Hengel, Longbing Cao, and Thomas G. Dietterich. "ANDEA: Anomaly and Novelty Detection, Explanation, and Accommodation." In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD'22), pp. 4892-4893. 2022.

  4. Guansong Pang, Jundong Li, Anton van den Hengel, Longbing Cao, and Thomas G. Dietterich. "Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA)." In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD'21), pp. 4145-4146. 2021.

  5. Zhiyue Wu, Hongzuo Xu, Guansong Pang, Fengyuan Yu, Yijie Wang, Songlei Jian, and Yongjun Wang. "DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities." In: PAKDD'21 Alibaba Cloud AIOps Competition. Best Paper Award. [pdf]

  6. Guansong Pang, Kai Ming Ting, and David Albrecht. “LeSiNN: Detecting anomalies   by identifying Least Similar Nearest Neighbours”. In: 2015 IEEE 15th International   Conference on Data Mining Workshops (ICDMW'15). IEEE, pp. 623–630, 2015.

  7. Guansong Pang, Huidong Jin, and Shengyi Jiang. “An effective class-centroid-based dimension reduction method for text classification”. In: Proceedings of the 22nd International Conference on World Wide Web (Companion Volume) (WWW'13). pp. 223–224, 2013.

Dissertations

1. Guansong Pang. "Non-IID outlier detection with coupled outlier factors", Thesis for Doctor of Philosophy , University of Technology Sydney, Australia, 2019 [pdf] (named on the prestigious UTS Chancellor's Award List)

2. Guansong Pang. "Anomaly detection based on zero appearances in subspaces", Thesis for Master of Philosophy, Monash University, Australia, 2015 [pdf]