fast MAP inference on xRF (2012)

After almost two weeks of travel, I'm finally back from my CVPR+new England trip : ) I guess that I should start writing about the interesting trend I observed in the papers published in major vision related conferences recently.

The first trend that I would like to talk about is "fast MAP inference on xRF model".

*Philipp Krähenbühl and Vladlen Koltun, "Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials",  NIPS'11

*Yimeng Zhang, and Tsuhan Chen. “Efficient Inference for Fully Connected CRFs with Stationary” , CVPR'12

*Min Sun, Murali Telaprolu, Honglak Lee, and Silvio Savarese. "An Efficient Branch-and-Bound Algorithm for Optimal Human Pose Estimation", CVPR'12

The first two papers present methods to solve MAP inference on fully connected CRF approximately very efficiently using efficient high-dimensional filtering algorithms. Yimeng et al.'s method can even handle pair-wise potentials other than Gaussian kernel as long as they are stationary.

The last work is actually my own work (XD) which introduces an exact inference algorithm to solve xRF efficiently which has large number of states per node (e.g., object detection and human pose estimation). The work shares similar high-level idea with the first two papers, where efficient data structure and search algorithm are used to significantly speed-up the inference.

In general, vision researches nowadays really need to be familiar with tools and algorithms maybe introduced in other fields enough so that they can use them to solve specific types of interesting vision task.


More observation coming up....