Toward Open Set Recognition

By Walter J. Scheirer, Anderson de Rezende Rocha, Archana Sapkota, Terrance E. Boult. 2013

1,502 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Unbounded cache model for online language modeling with open vocabulary (2017, background): “Scheirer et al. [49] proposed a framework for open set recognition based on one-class SVMs. Adaptive language models.”
Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects (2018, background): “, classes not found in the training set, present a significant challenge for computer vision technologies in real-world settings [38].”
Language-driven Semantic Segmentation (2022, background): “A variety of solutions have been proposed (Zhang et al., 2020b; Liu et al., 2020a; Perera et al., 2020; Zhou et al., 2021) for open-set recognition (Scheirer et al., 2012; Geng et al., 2020).”
Towards Open Set Deep Networks (2015, methodology): “Altough a range of algorithms has been developed to address this issue [4, 20, 21, 25, 2], performing open set recognition with deep networks has remained an unsolved problem.”
Novel Class Discovery without Forgetting (2022, background): “Methods that perform out-of-distribution detection [32,38] and open-set learning [46,18] identify instances significantly different from the training data distribution as novel samples, but do not identify sub-groups within these identified instances automatically.”
Subspace Regularizers for Few-Shot Class Incremental Learning (2021, background): “…describes several versions of this problem in featuring more complex perception or control: Few-shot and incremental learning Few-shot classiﬁcation problems test learners’ ability to distinguish among a ﬁxed set of classes using only a handful of labeled examples per class (Scheirer et al., 2012).”
Towards Open World Object Detection (2021, background): “[58] developed open set classiﬁers in a one-vs-rest setting to balance the performance and the risk of labeling a sample far from the known training examples (termed as open space risk).”
Out-of-Domain Detection for Natural Language Understanding in Dialog Systems (2019, neutral): “of open-world [4], [5] has been proposed.”
DOC: Deep Open Classification of Text Documents (2017, background): “In computer vision, Scheirer et al. (2013) studied the problem of recognizing unseen images that the system was not trained for by reducing open space risk.”
The limits and potentials of deep learning for robotics (2018, background): “In these so called open-set conditions [11], [101], it is crucial to identify the unknowns: The perception system must not assign high-conﬁdence scores to unknown objects or falsely recognize them as one of the known classes.”
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning (2020, background): “…possible inputs to our model (Scheirer et al., 2013) The broader inspiration for this categorization is commonly attributed to a notorious, machine learning unrelated, quote by Rumsfeld (Naylor, 2010; Scheirer et al., 2013): “We know that there are known knowns; these are things we think we know.”
Large-Scale Long-Tailed Recognition in an Open World (2019, background): “Open-set recognition [44, 2], or out-of-distribution detection [9, 26], aims to re-calibrate the sample confidence in the presence of open classes.”
Learning Placeholders for Open-Set Recognition (2021, methodology): “Based on SVM, [27] proposed 1-vs-Set machine, aiming to create a slab in the feature space.”
VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding (2021, background): “This is similar to open set recognition (Scheirer et al., 2012) or out-of-domain intent detection (Lane et al.”
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild (2016, background): “Finally, open set recognition [35,36,37] considers testing on both types of classes, but treating the unseen ones as a single outlier class.”
Recent Advances in Open Set Recognition: A Survey (2018, background): “(The openness defined in [21]) Let CTA, CTR, and CTE respectively represent the set of classes to be recognized, the set of classes used in training and the set of classes”
Open Category Detection with PAC Guarantees (2018, methodology): “Variants of SVMs have also been developed, such as the One-vs-Set Machine (Scheirer et al., 2013) and the Weibull-calibrated SVM (Scheirer et al.”