Fetching the paper…
Reading the bibliography…
Open-set segmentation can be conceived by complementing closed-set classification with anomaly detection.
D. M. Hawkins, Identification of Outliers , ser. Monographs on Applied Probability and Statistics. Springer, 1980
1980
Earlier work this paper cites.
M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf. Process. Manag. , vol. 45, no. 4, pp. 427–437, 2009
2009
Earlier work this paper cites.
M. Everingham, L. V. Gool, C. K. I. Williams, J. M. Winn, and A. Zisserman, “The pascal visual object classes (VOC) challenge,” Int. J. Comput. Vis. , vol. 88, no. 2, pp. 303–338, 2010
2010
Earlier work this paper cites.
B. Hariharan, P. Arbelaez, L. D. Bourdev, S. Maji, and J. Malik, “Semantic contours from inverse detectors,” in IEEE International Conference on Computer Vision, ICCV , 2011
2011
Earlier work this paper cites.
C. Farabet, C. Couprie, L. Najman, and Y. LeCun, “Learning hierarchical features for scene labeling,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 35, no. 8, pp. 1915–1929, 2013
2013
Earlier work this paper cites.
W. J. Scheirer, A. de Rezende Rocha, A. Sapkota, and T. E. Boult, “Toward open set recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 7, pp. 1757–1772, 2013
2013
Earlier work this paper cites.
W. J. Scheirer, L. P. Jain, and T. E. Boult, “Probability models for open set recognition,” IEEE Trans. Pattern Anal. Mach. Intell. , 2014
2014
Earlier work this paper cites.
T. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: common objects in context,” in European Conference on Computer Vision , 2014
2014
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 , 2016, pp. 770–778
2016
Earlier work this paper cites.
P. Pinggera, S. Ramos, S. Gehrig, U. Franke, C. Rother, and R. Mester, “Lost and found: detecting small road hazards for self-driving vehicles,” in International Conference on Intelligent Robots and Systems, IROS , 2016
2016
Earlier work this paper cites.
A. Bendale and T. E. Boult, “Towards open set deep networks,” in IEEE Conference on Computer Vision and Pattern Recognition , 2016
2016
Earlier work this paper cites.
M. D. Scherreik and B. D. Rigling, “Open set recognition for automatic target classification with rejection,” IEEE Trans. Aerosp. Electron. Syst. , vol. 52, no. 2, pp. 632–642, 2016
2016
Earlier work this paper cites.
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” in Neural Information Processing Systems 2016 , 2016, pp. 2226–2234
2016
Earlier work this paper cites.
J. Steinhardt and P. Liang, “Unsupervised risk estimation using only conditional independence structure,” in Neural Information Processing Systems 2016 , 2016, pp. 3657–3665
2016
Earlier work this paper cites.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR , 2016
2016
Earlier work this paper cites.
E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 39, no. 4, pp. 640–651, 2017
2017
Earlier work this paper cites.
A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” in Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
D. Hendrycks and K. Gimpel, “A baseline for detecting misclassified and out-of-distribution examples in neural networks,” in 5th International Conference on Learning Representations, ICLR , 2017
2017
Earlier work this paper cites.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” in Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
G. Neuhold, T. Ollmann, S. R. Bulò, and P. Kontschieder, “The mapillary vistas dataset for semantic understanding of street scenes,” in IEEE International Conference on Computer Vision , 2017
2017
Earlier work this paper cites.
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 40, no. 4, pp. 834–848, 2018
2018
Earlier work this paper cites.
T. DeVries and G. W. Taylor, “Learning confidence for out-of-distribution detection in neural networks,” CoRR , 2018
2018
Earlier work this paper cites.
K. Lee, H. Lee, K. Lee, and J. Shin, “Training confidence-calibrated classifiers for detecting out-of-distribution samples,” in 6th International Conference on Learning Representations, ICLR , 2018
2018
Earlier work this paper cites.
S. Liang, Y. Li, and R. Srikant, “Enhancing the reliability of out-of-distribution image detection in neural networks,” in 6th International Conference on Learning Representations, ICLR , 2018
2018
Earlier work this paper cites.
A. R. Dhamija, M. Günther, and T. E. Boult, “Reducing network agnostophobia,” in Annual Conference on Neural Information Processing Systems 2018, NeurIPS , 2018
2018
Earlier work this paper cites.
L. Neal, M. L. Olson, X. Z. Fern, W. Wong, and F. Li, “Open set learning with counterfactual images,” in ECCV 2018 - 15th European Conference, Munich, German , 2018
2018
Earlier work this paper cites.
O. Zendel, K. Honauer, M. Murschitz, D. Steininger, and G. F. Dominguez, “Wilddash - creating hazard-aware benchmarks,” in European Conference on Computer Vision (ECCV) , 2018
2018
Earlier work this paper cites.
A. Malinin and M. J. F. Gales, “Predictive uncertainty estimation via prior networks,” in Neural Information Processing Systems , 2018
2018
Earlier work this paper cites.
K. Lee, K. Lee, H. Lee, and J. Shin, “A simple unified framework for detecting out-of-distribution samples and adversarial attacks,” in Neural Information Processing Systems, NeurIPS , 2018
2018
Earlier work this paper cites.
T. E. Boult, S. Cruz, A. R. Dhamija, M. Günther, J. Henrydoss, and W. J. Scheirer, “Learning and the unknown: Surveying steps toward open world recognition,” in AAAI Conference on Artificial Intelligence . AAAI Press, 2019
2019
Earlier work this paper cites.
H. Blum, P. Sarlin, J. I. Nieto, R. Siegwart, and C. Cadena, “Fishyscapes: A benchmark for safe semantic segmentation in autonomous driving,” in 2019 IEEE/CVF International Conference on Computer Vision Workshops . IEEE, 2019, pp. 2403–2412
2019
Cited alongside, same era.
K. Lis, K. K. Nakka, P. Fua, and M. Salzmann, “Detecting the unexpected via image resynthesis,” in International Conference on Computer Vision, ICCV , 2019
2019
Cited alongside, same era.
E. T. Nalisnick, A. Matsukawa, Y. W. Teh, D. Görür, and B. Lakshminarayanan, “Do deep generative models know what they don’t know?” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
T. Lucas, K. Shmelkov, K. Alahari, C. Schmid, and J. Verbeek, “Adaptive density estimation for generative models,” in Neural Information Processing Systems , 2019
2019
Cited alongside, same era.
R. Chan, M. Rottmann, and H. Gottschalk, “Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation,” in International Conference on Computer Vision, ICCV , 2021
2021
Later among the works it cites.
V. Besnier, A. Bursuc, D. Picard, and A. Briot, “Triggering failures: Out-of-distribution detection by learning from local adversarial attacks in semantic segmentation,” in International Conference on Computer Vision , 2021
2021
Later among the works it cites.
M. Grcić, P. Bevandić, and S. Šegvić, “Dense open-set recognition with synthetic outliers generated by real NVP,” in Int’l Conference on Computer Vision Theory and Applications , 2021
2021
Later among the works it cites.
V. Zavrtanik, M. Kristan, and D. Skocaj, “Reconstruction by inpainting for visual anomaly detection,” Pattern Recognit. , 2021
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. Hendrycks, M. Mazeika, and T. G. Dietterich, “Deep anomaly detection with outlier exposure,” in 7th International Conference on Learning Representations, ICLR , 2019
2019
Cited alongside, same era.
P. Bevandic, I. Kreso, M. Orsic, and S. Segvic, “Simultaneous semantic segmentation and outlier detection in presence of domain shift,” in 41st DAGM German Conference, DAGM GCPR , 2019
2019
Cited alongside, same era.
Y. Xian, C. H. Lampert, B. Schiele, and Z. Akata, “Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 41, no. 9, 2019
2019
Cited alongside, same era.
Y. Du and I. Mordatch, “Implicit generation and modeling with energy based models,” in Neural Information Processing Systems 2019, NeurIPS 2019 , 2019
2019
Cited alongside, same era.
Y. Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,” in Neural Information Processing Systems 2019, NeurIPS 2019 , 2019, pp. 11 895–11 907
2019
Cited alongside, same era.
Y. Zhu, K. Sapra, F. A. Reda, K. J. Shih, S. D. Newsam, A. Tao, and B. Catanzaro, “Improving semantic segmentation via video propagation and label relaxation,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR , 2019
2019
Cited alongside, same era.
P. Oza and V. M. Patel, “C2ae: Class conditioned auto-encoder for open-set recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
2019
Cited alongside, same era.
X. Li, A. You, Z. Zhu, H. Zhao, M. Yang, K. Yang, S. Tan, and Y. Tong, “Semantic flow for fast and accurate scene parsing,” in European Conference on Computer Vision , 2020
2020
Cited alongside, same era.
C. Geng, S. Huang, and S. Chen, “Recent advances in open set recognition: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 43, no. 10, pp. 3614–3631, 2021
2021
Later among the works it cites.
R. Chan, K. Lis, S. Uhlemeyer, H. Blum, S. Honari, R. Siegwart, P. Fua, M. Salzmann, and M. Rottmann, “Segmentmeifyoucan: A benchmark for anomaly segmentation,” in Neural Information Processing Systems Track on Datasets and Benchmarks , 2021
2021
Later among the works it cites.
U. Michieli and P. Zanuttigh, “Knowledge distillation for incremental learning in semantic segmentation,” Comput. Vis. Image Underst. , vol. 205, p. 103167, 2021
2021
Later among the works it cites.
M. Grcić, I. Grubišić, and S. Šegvić, “Densely connected normalizing flows,” in Neural Information Processing Systems , 2021
2021
Later among the works it cites.
S. Jung, J. Lee, D. Gwak, S. Choi, and J. Choo, “Standardized max logits: A simple yet effective approach for identifying unexpected road obstacles in urban-scene segmentation,” in International Conference on Computer Vision, ICCV , 2021
2021
Later among the works it cites.
I. Kreso, J. Krapac, and S. Segvic, “Efficient ladder-style densenets for semantic segmentation of large images,” IEEE Trans. Intell. Transp. Syst. , vol. 22, 2021
2021
Later among the works it cites.
Y. Sun, C. Guo, and Y. Li, “React: Out-of-distribution detection with rectified activations,” in NeurIPS , 2021
2021
Later among the works it cites.
S. Fort, J. Ren, and B. Lakshminarayanan, “Exploring the limits of out-of-distribution detection,” in Neural Information Processing Systems, NeurIPS 2021 , 2021, pp. 7068–7081
2021
Later among the works it cites.
S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image segmentation using deep learning: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 44, no. 7, 2022
2022
Later among the works it cites.
H. Pan, Y. Hong, W. Sun, and Y. Jia, “Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes,” IEEE Trans. on Intelligent Transportation Systems , 2022
2022
Later among the works it cites.
C. González, K. Gotkowski, M. Fuchs, A. Bucher, A. Dadras, R. Fischbach, I. J. Kaltenborn, and A. Mukhopadhyay, “Distance-based detection of out-of-distribution silent failures for covid-19 lung lesion segmentation,” Medical Image Anal. , vol. 82, 2022
2022
Later among the works it cites.
P. Bevandić, I. Krešo, M. Oršić, and S. Šegvić, “Dense open-set recognition based on training with noisy negative images,” Image and Vision Computing , vol. 124, p. 104490, 2022
2022
Later among the works it cites.
S. Kong and D. Ramanan, “Opengan: Open-set recognition via open data generation,” IEEE Trans. Pattern Anal. Mach. Intell. , 2022
2022
Later among the works it cites.
X. Du, Z. Wang, M. Cai, and Y. Li, “VOS: learning what you don’t know by virtual outlier synthesis,” in The Tenth International Conference on Learning Representations, ICLR 2022 , 2022
2022
Later among the works it cites.
M. Grcic, P. Bevandic, and S. Segvic, “Densehybrid: Hybrid anomaly detection for dense open-set recognition,” in European Conference on Computer Vision, ECCV , 2022
2022
Later among the works it cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Computer Vision and Pattern Recognition, CVPR , 2022
2022
Later among the works it cites.
Y. Tian, Y. Liu, G. Pang, F. Liu, Y. Chen, and G. Carneiro, “Pixel-wise energy-biased abstention learning for anomaly segmentation on complex urban driving scenes,” in European Conference on Computer Vision , 2022
2022
Later among the works it cites.
C. Liang, W. Wang, J. Miao, and Y. Yang, “Gmmseg: Gaussian mixture based generative semantic segmentation models,” Advances in Neural Information Processing Systems , 2022
2022
Later among the works it cites.
S. Vaze, K. Han, A. Vedaldi, and A. Zisserman, “Open-set recognition: A good closed-set classifier is all you need,” in The Tenth International Conference on Learning Representations, ICLR 2022 , 2022
2022
Later among the works it cites.
G. Chen, P. Peng, X. Wang, and Y. Tian, “Adversarial reciprocal points learning for open set recognition,” IEEE Trans. Pattern Anal. Mach. Intell. , 2022
2022
Later among the works it cites.
C. Sakaridis, D. Dai, and L. V. Gool, “Map-guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 44, no. 6, 2022
2022
Later among the works it cites.
S. Uhlemeyer, M. Rottmann, and H. Gottschalk, “Towards unsupervised open world semantic segmentation,” in Uncertainty in Artificial Intelligence , 2022
2022
Later among the works it cites.
D. Hendrycks, S. Basart, M. Mazeika, A. Zou, J. Kwon, M. Mostajabi, J. Steinhardt, and D. Song, “Scaling out-of-distribution detection for real-world settings,” in International Conference on Machine Learning, ICML , 2022
2022
Later among the works it cites.
Z. Zhao, L. Cao, and K. Lin, “Revealing the distributional vulnerability of discriminators by implicit generators,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 45, no. 7, pp. 8888–8901, 2023
2023
Closest in time.
N. Kumar, S. Segvic, A. Eslami, and S. Gumhold, “Normalizing flow based feature synthesis for outlier-aware object detection,” in IEEE/CVF Computer Vision and Pattern Recognition, CVPR , 2023
2023
Closest in time.
M. Grcić, P. Bevandić, Z. Kalafatić, and S. Šegvić, “Dense out-of-distribution detection by robust learning on synthetic negative data,” Sensors , 2024
2024
Closest in time.