A Bayesian network framework for relational shape matching

By Li Fei-Fei, Rob Fergus, Pietro Perona. 2003

578 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions (2015, background): “The concept of “Zero-Shot Learning” has been introduced in the literature [7, 8, 16, 21, 5, 17] with the aim to improve the scalability of traditional object recognition systems.”
LabelMe: A Database and Web-Based Tool for Image Annotation (2008, background): “Even algorithms requiring no supervision (Sivic et al. 2005; Russell et al. 2006; Fei-Fei et al. 2003; Sudderth et al. 2005b, 2005a; Quelhas et al. 2005) need this quantitative framework.”
Continual Lifelong Learning with Neural Networks: A Review (2018, background): “lability of multiple 17 learning tasks with the aim to improve the performance at one speciﬁc task. Similarly, zero-shot learning (Lampert et al., 2009; Palatucci et al., 2009) and one-shot learning (Fei-Fei et al., 2003; Vinyals et al., 2016) aim at performing well on novel tasks but do not prevent”
Gradient Episodic Memory for Continual Learning (2017, background): “Zero-shot learning [Lampert et al., 2009, Palatucci et al., 2009] and one-shot learning [Fei-Fei et al., 2003, Vinyals et al., 2016, Santoro et al., 2016, Bertinetto et al., 2016] aim at performing well on unseen tasks, but ignore the catastrophic forgetting of previously learned tasks.”
Lifelong Generative Modeling (2017, background): “Furthermore, latent-variable posterior approximations are a requirement in many learning scenarios such as clustering [93], compression [92] and unsupervised representation learning [30].”
Taskonomy: Disentangling Task Transfer Learning (2018, background): “compositional modeling [33, 8, 11, 21, 53, 89, 87], homomorphic cryptography [40], lifelong learning [90, 13, 82, 81], functional maps [68], certain aspects of Bayesian inference and Dirichlet processes [52, 88, 87, 86, 35, 37], few-shot learning [78, 23, 22, 67, 83], transfer learning [72, 81, 27, ”
Sharing Visual Features for Multiclass and Multiview Object Detection (2007, background): “[9] propose a model based on the geometric configuration of parts; each part is represented as a local PCA template.”
Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning (2019, background): “Its industrial potential increasingly drives the emergence of solution, falling under the umbrellas of Bayesian approaches [10, 26], similarity learning [25, 36] and meta-learning [40, 42, 41, 37].”
Semi-Supervised Self-Training of Object Detection Models (2005, methodology): “Recent work [8], [7] utilizes an EM based approach.”
Boosting Few-Shot Visual Learning With Self-Supervision (2019, background): “The aim of few-shot learning [9, 10, 26, 30, 54] is to endow artificial perception systems with a similar ability, especially with the help of modern deep learning.”
Recent Advances in Open Set Recognition: A Survey (2018, background): “To meet this challenge, several related research topics have been explored including lifelong learning [1], [2], transfer learning [3], [4], [5], domain adaptation [6], [7], zero-shot [8], [9], [10], one-shot (few-shot) [11], [12], [13], [14], [15], [16], [17], [18], [19], [20] recognition/learning,”
Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs (2019, methodology): “The first strategy learns a classifier for a set of frequent categories and then uses them to learn the few-shot categories [21, 22, 57].”
Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities (2021, methodology): “In [91], a one-shot learning technique was presented for incorporating knowledge about object categories, which may be obtained from previously learned models, to predict new objects when very few examples are available from a given class.”
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild (2018, unknown): “variety while Table 1 shows a comprehensive comparison of GOT10k against other tracking datasets. Unlike existing datasets, GOT-10k follows the standard experimental setting of oneshot learning [35], [36] that the object classes between training and testing videos are non-overlapped, while the suppo”