Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanat...
Conditional random field (CRF) is a popular graphical model for sequence labeling. The flexibility of CRF poses significant computational challenges for training. Using existing o...
This paper studies global ranking problem by learning to rank methods. Conventional learning to rank methods are usually designed for `local ranking', in the sense that the r...
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang...
In this paper, we design a novel MRF framework which is called Non-Local Range Markov Random Field (NLRMRF). The local spatial range of clique in traditional MRF is extended to th...
We formulate a model for multi-class object detection in a multi-camera environment. From our knowledge, this is the first time that this problem is addressed taken into account ...
Xavier Boix, Gemma Roig, Horesh Ben Shitrit, Pasca...