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» Efficient inference on sequence segmentation models
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ICML
2006
IEEE
14 years 11 months ago
Efficient inference on sequence segmentation models
Sequence segmentation is a flexible and highly accurate mechanism for modeling several applications. Inference on segmentation models involves dynamic programming computations tha...
Sunita Sarawagi
EMNLP
2006
14 years 11 days ago
A Hybrid Markov/Semi-Markov Conditional Random Field for Sequence Segmentation
Markov order-1 conditional random fields (CRFs) and semi-Markov CRFs are two popular models for sequence segmentation and labeling. Both models have advantages in terms of the typ...
Galen Andrew
CVPR
2005
IEEE
15 years 29 days ago
A Dynamic Conditional Random Field Model for Object Segmentation in Image Sequences
This paper presents a dynamic conditional random field (DCRF) model to integrate contextual constraints for object segmentation in image sequences. Spatial and temporal dependenci...
Qiang Ji, Yang Wang 0002
CVPR
2007
IEEE
15 years 29 days ago
Latent-Dynamic Discriminative Models for Continuous Gesture Recognition
Many problems in vision involve the prediction of a class label for each frame in an unsegmented sequence. In this paper, we develop a discriminative framework for simultaneous se...
Louis-Philippe Morency, Ariadna Quattoni, Trevor D...
ICML
2004
IEEE
14 years 11 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...