Awesome Open Set Recognition list

A curated list of papers & ressources linked to open set recognition, out-of-distribution, open set domain adaptation, and open world recognition

Note that:

• This list is not exhaustive.
• Tables use alphabetical order for fairness.

Contents

• Tutorials

• Challenges

• Papers

  • Open Set Recognition
    • Deep Neural Network-based
    • Traditional Machine Learning Methods-based
    • Open Set Learning Theory
  • Out-of-Distribution
  • Anomaly Detection
  • Open Set Domain Adaptation
  • Open World Recognition

• License

• Contributing

Tutorials

Tutorial & survey

• Toward Open Set Recognition, Scheirer W J, de Rezende Rocha A, Sapkota A, et al. (PAMI, 2013).

• Towards Open World Recognition, Bendale A, Boult T. (CVPR, 2015).

• Lifelong Machine Learning, Zhiyuan Chen and Bing Liu. (2018).

• Recent Advances in Open Set Recognition: A Survey, Geng C, Huang S, Chen S. (arXiv, 2018).

• Recent Advances in Open Set Recognition: A Survey v2, Chuanxing Geng, Sheng-jun Huang, Songcan Chen. (arXiv, 2019).

• A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges. Salehi M, Mirzaei H, Hendrycks D, Li Y, Rohban MH, Sabokrou M. (arXiv 2021).

Challenges

Open World Vision

• Visual Perception and Learning in an Open World, CVPR 2022.
• Dealing with Novelty in Open Worlds: DNOW, WACV 2022.
• Open World Image Classification Challenge, CVPR 2021.

Papers

Open Set Recognition

Deep Neural Network-based

2023

• Understanding open-set recognition by Jacobian norm and inter-class separation (Arxiv) Jaewoo Park, Hojin Park, Eunju Jeong, Andrew Beng Jin Teoh (Pattern Recognition 2023)
• Deep Learning-Based Material Characterization Using FMCW Radar With Open-Set Recognition Technique. Salah Abouzaid, Timo Jaeschke, Simon Kueppers, Jan Barowski, Nils Pohl. (IEEE TMTT 2023). [code].
• From anomaly detection to open set recognition: Bridging the gap Hakan Cevikalp, Bedirhan Uzun, Yusuf Salk, Hasan Saribas, Okan Köpüklü. (Pattern Recognition 2023)
• Large-Scale Open-Set Classification Protocols for ImageNet. Andres Palechor, Annesha Bhoumik, Manuel Günther. (WACV 2023) [code]
• The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition. Jun Cen, Di Luan, Shiwei Zhang, Yixuan Pei, Yingya Zhang, Deli Zhao, Shaojie Shen, Qifeng Chen. (ICLR 2022).

2022

• Class-specific semantic reconstruction for open set recognition. Hongzhi Huang, Yu Wang, Qinghua Hu, Ming-Ming Cheng. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI 2022). [code].
• OpenAUC: Towards AUC-Oriented Open-Set Recognition. Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang. (NeurIPS 2022).
• Towards Open Set 3D Learning: A Benchmark on Object Point Clouds. Antonio Alliegro, Francesco Cappio Borlino, Tatiana Tommasi. (ArXiv 2022). [code].
• Measuring Human Perception to Improve Handwritten Document Transcription. Jin Huang, Derek Prijatelj, Justin Dulay, Walter Scheirer. (TPAMI 2022)
• Open-Set Semi-Supervised Object Detection. Yen-Cheng Liu, Chih-Yao Ma, Xiaoliang Dai, Junjiao Tian, Peter Vajda, Zijian He, Zsolt Kira. (ECCV 2022). [code].
• DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition. Matej Grcić, Petra Bevandić, Siniša Šegvić. (ECCV 2022). [code].
• Difficulty-Aware Simulator for Open Set Recognition. WonJun Moon, Junho Park, Hyun Seok Seong, Cheol-Ho Cho, Jae-Pil Heo. (ECCV 2022). [code].
• Unseen Classes at a Later Time? No Problem. Hari Chandana Kuchibhotla, Sumitra S Malagi, Shivam Chandhok, Vineeth N Balasubramanian. (CVPR 2022). [code].
• OSSGAN: Open-Set Semi-Supervised Image Generation. Kai Katsumata, Duc Minh Vo, Hideki Nakayama. (CVPR 2022). [code].
• OpenTAL: Towards Open Set Temporal Action Localization. Wentao Bao, Qi Yu, Yu Kong. (CVPR 2022). [code].
• The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods. Thomas G. Dietterich, Alexander Guyer. (ArXiv 2022).
• LUNA: Localizing Unfamiliarity Near Acquaintance for Open-Set Long-Tailed Recognition. Jiarui Cai, Yizhou Wang, Hung-Min Hsu, Jenq-Neng Hwang, Kelsey Magrane, Craig Rose. (AAAI 2022).
• Learngene: From Open-World to Your Learning Task. Qiufeng Wang, Xin Geng, Shuxia Lin, Shiyu Xia, Lei Qi, Ning Xu. (AAAI 2022).
• Learning Network Architecture for Open-Set Recognition. Xuelin Zhang, Xuelian Cheng, Donghao Zhang, Paul Bonnington, Zongyuan Ge. (AAAI 2022).
• PMAL: Open Set Recognition via Robust Prototype Mining. Jing Lu, Yunlu Xu, Hao Li, Zhanzhan Cheng, Yi Niu. (AAAI 2022).
• Open-Set Recognition: A Good Closed-Set Classifier is All You Need. Sagar Vaze, Kai Han, Andrea Vedaldi, Andrew Zisserman. (ICLR 2022). [code].

2021

• Adversarial Motorial Prototype Framework for Open Set Recognition. Ziheng Xia, Penghui Wang, Ganggang Dong, Hongwei Liu. (ArXiv 2021).
• OpenGAN: Open-Set Recognition via Open Data Generation. Shu Kong, Deva Ramanan. (ICCV 2021). [code].
• Trash To Treasure: Harvesting OOD Data With Cross-Modal Matching for Open-Set Semi-Supervised Learning. Junkai Huang, Chaowei Fang, Weikai Chen, Zhenhua Chai, Xiaolin Wei, Pengxu Wei, Liang Lin, Guanbin Li. (ICCV 2021)
• Energy-Based Open-World Uncertainty Modeling for Confidence Calibration. Yezhen Wang, Bo Li, Tong Che, Kaiyang Zhou, Ziwei Liu, Dongsheng Li. (ICCV 2021)
• Prototypical Matching and Open Set Rejection for Zero-Shot Semantic Segmentation. Hui Zhang, Henghui Ding. (ICCV 2021)
• Towards Discovery and Attribution of Open-World GAN Generated Images. Sharath Girish, Saksham Suri, Saketh Rambhatla, Abhinav Shrivastava. (ICCV 2021)
• Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation. Weiyao Wang, Matt Feiszli, Heng Wang, Du Tran. (ICCV 2021)
• Deep Metric Learning for Open World Semantic Segmentation. Jun Cen, Peng Yun, Junhao Cai, Michael Yu Wang, Ming Liu. (ICCV 2021)
• NGC: A Unified Framework for Learning With Open-World Noisy Data. Zhi-Fan Wu, Tong Wei, Jianwen Jiang, Chaojie Mao, Mingqian Tang, Yu-Feng Li. (ICCV 2021)
• Large Scale Open-Set Deep Logo Detection. Muhammet Bastan, Hao-Yu Wu, Tian Cao, Bhargava Kota, Mehmet Tek. (ArXiv 2021). [code].
• OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers. Kuniaki Saito, Donghyun Kim, Kate Saenko. (ArXiv 2021). [code].
• Zero-Shot Open Set Detection by Extending CLIP. Sepideh Esmaeilpour, Bing Liu, Eric Robertson, Lei Shu. (ArXiv 2021).
• Adversarial Reciprocal Points Learning for Open Set Recognition. Guangyao Chen, Peixi Peng, Xiangqian Wang, Yonghong Tian. (TPAMI 2021). [code].
• Conditional Variational Capsule Network for Open Set Recognition. Yunrui Guo, Guglielmo Camporese, Wenjing Yang, Alessandro Sperduti, Lamberto Ballan. (ICCV 2021). [code]
• Evidential Deep Learning for Open Set Action Recognition. Wentao Bao, Qi Yu, Yu Kong. (ICCV 2021). [code]
• M2IOSR: Maximal Mutual Information Open Set Recognition. Xin Sun, Henghui Ding, Chi Zhang, Guosheng Lin, Keck-Voon Ling. (ArXiv 2021)
• OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers. Kuniaki Saito, Donghyun Kim, Kate Saenko. (ArXiv 2021)
• Exemplar-Based Open-Set Panoptic Segmentation Network. Jaedong Hwang, Seoung Wug Oh, Joon-Young Lee, Bohyung Han. (CVPR 2021)
• Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios.