Group-sensitive multiple kernel learning for object categorization
By Jingjing Yang, Yuanning Li, Yonghong Tian, Ling-yu Duan, Wen Gao. 2009
173 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Trevor Darrell
- Yoshua Bengio
- Andrew Zisserman
- Andrea Vedaldi
- Yi Yang
- Dacheng Tao
- Cordelia Schmid
- Luc Van Gool
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Trevor Darrell
- Andrew Zisserman
- Dacheng Tao
- Jitendra Malik
- Xiaogang Wang
- Andrea Vedaldi
- Ming-Hsuan Yang
In Media Technology
- Yangqing Jia
- Jean Ponce
- Yann LeCun
- Thomas Mensink
- Jorge Sánchez
- Francis R. Bach
- Trevor Darrell
- Florent Perronnin
Citations in the corpus
- DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition (2013, methodology): “We compare our performance against the current state-ofthe-art on this benchmark from Yang et al. (2009), a method employing a combination of 5 traditional hand-engineered image features followed by a multi-kernel based classifier.”
- Learning Semantic Image Representations at a Large Scale (2014, methodology): “We compare our performance against the current state-of-the-art on this benchmark from [119], a method employing a combination of 5 traditional hand-engineered image features followed by a multi-kernel based classifier.”
- Multiple Kernel Learning Algorithms (2011, unknown): “Zien and Ong (2007) develop a QCQP formulation and convert this formulation in two different SILP problems for multiclass classification.”
- Improving the Fisher Kernel for Large-Scale Image Classification (2010, methodology): “[6] uses, again, many channels and a sophisticated Multiple Kernel Learning (MKL) algorithm.”
- Learning Discriminative Part Detectors for Image Classification and Cosegmentation (2013, unknown): “3% achieved in [37] by multiple kernel learning.”
- Ask the locals: Multi-way local pooling for image recognition (2011, background): “3% [40] on Caltech-101 with 30 training examples), or subcategory learning (83% on Caltech-101 [35]).”
- Traffic Sign Recognition via Multi-Modal Tree-Structure Embedded Multi-Task Learning (2017, methodology): “Multiple kernel learning (MKL) methods [5], [6] have been widely stud-”