Timezone: »

Kernel Latent SVM for Visual Recognition
Weilong Yang · Yang Wang · Arash Vahdat · Greg Mori

Tue Dec 04 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

Latent SVMs (LSVMs) are a class of powerful tools that have been successfully applied to many applications in computer vision. However, a limitation of LSVMs is that they rely on linear models. For many computer vision tasks, linear models are suboptimal and nonlinear models learned with kernels typically perform much better. Therefore it is desirable to develop the kernel version of LSVM. In this paper, we propose kernel latent SVM (KLSVM) -- a new learning framework that combines latent SVMs and kernel methods. We develop an iterative training algorithm to learn the model parameters. We demonstrate the effectiveness of KLSVM using three different applications in visual recognition. Our KLSVM formulation is very general and can be applied to solve a wide range of applications in computer vision and machine learning.

Author Information

Weilong Yang (Google Inc.)
Yang Wang (University of Manitoba)
Arash Vahdat (Simon Fraser University)
Greg Mori (Borealis AI)

More from the Same Authors