Computer vision and pattern recognition

By Nanning Zheng, G. Loizou, Xiaoyi Jiang, Xuguang Lan, Xuelong Li. International Journal of Computational Mathematics, 2007

1,314 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/26a73f0efe1f7c24d400612047af34a2d6dcea06

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Towards constraining warm dark matter with stellar streams through neural simulation-based inference (2020, methodology): “(3) Hermans et al. (2019) show that an amortised estimator 𝑟 (𝑥 |𝜗) of the intractable likelihood-to-evidence ratio can be obtained by training a discriminator 𝑑 (𝜗, 𝑥) with inputs 𝜗 and 𝑥, to distinguish between samples from the joint 𝑝(𝜗, 𝑥) with class label 1 and samples from the…”
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning (2018, methodology): “As presented, DeepLabCut extracts the posture data frame-by-frame, but one can add temporal Kalman filtering to improve performance (as for other approaches) [6, 45, 46].”
A Robust and Efficient Video Representation for Action Recognition (2015, methodology): “The UCF101 dataset (Soomro et al, 2012) is extended from UCF50 with additional 51 action categories.”
Knowledge Aware Semantic Concept Expansion for Image-Text Matching (2019, methodology): “Knowledge can be used for image tasks like image classification [Marino et al., 2017], zero shot classification [Lee et al., 2018a] and for text tasks [Zhou et al., 2018].”
Principal Component Analysis (2018, methodology): “In [119], PCA is used to combine image descriptors given by the SIFT method [120].”
Learning Sight from Sound: Ambient Sound Provides Supervision for Visual Learning (2017, methodology): “As shown in Table 2, we found that the overall best-performing model was the recent colorization method of Zhang et al (2016), but that the best-performing variation of our model (the binary-coding method) obtained comparable performance to the other unsupervised learning methods, such as Doersch…”
Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers (2019, background): “[Misra et al., 2016] propose a cross-stitch network for MTL to learn an optimal combination of shared and task-specific representations.”