119,745 research outputs found
Partial sequence labeling with structured Gaussian Processes
Existing partial sequence labeling models mainly focus on max-margin
framework which fails to provide an uncertainty estimation of the prediction.
Further, the unique ground truth disambiguation strategy employed by these
models may include wrong label information for parameter learning. In this
paper, we propose structured Gaussian Processes for partial sequence labeling
(SGPPSL), which encodes uncertainty in the prediction and does not need extra
effort for model selection and hyperparameter learning. The model employs
factor-as-piece approximation that divides the linear-chain graph structure
into the set of pieces, which preserves the basic Markov Random Field structure
and effectively avoids handling large number of candidate output sequences
generated by partially annotated data. Then confidence measure is introduced in
the model to address different contributions of candidate labels, which enables
the ground-truth label information to be utilized in parameter learning. Based
on the derived lower bound of the variational lower bound of the proposed
model, variational parameters and confidence measures are estimated in the
framework of alternating optimization. Moreover, weighted Viterbi algorithm is
proposed to incorporate confidence measure to sequence prediction, which
considers label ambiguity arose from multiple annotations in the training data
and thus helps improve the performance. SGPPSL is evaluated on several sequence
labeling tasks and the experimental results show the effectiveness of the
proposed model
Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss
Recent works on deep conditional random fields (CRF) have set new records on
many vision tasks involving structured predictions. Here we propose a
fully-connected deep continuous CRF model for both discrete and continuous
labelling problems. We exemplify the usefulness of the proposed model on
multi-class semantic labelling (discrete) and the robust depth estimation
(continuous) problems.
In our framework, we model both the unary and the pairwise potential
functions as deep convolutional neural networks (CNN), which are jointly
learned in an end-to-end fashion. The proposed method possesses the main
advantage of continuously-valued CRF, which is a closed-form solution for the
Maximum a posteriori (MAP) inference.
To better adapt to different tasks, instead of using the commonly employed
maximum likelihood CRF parameter learning protocol, we propose task-specific
loss functions for learning the CRF parameters.
It enables direct optimization of the quality of the MAP estimates during the
course of learning.
Specifically, we optimize the multi-class classification loss for the
semantic labelling task and the Turkey's biweight loss for the robust depth
estimation problem.
Experimental results on the semantic labelling and robust depth estimation
tasks demonstrate that the proposed method compare favorably against both
baseline and state-of-the-art methods.
In particular, we show that although the proposed deep CRF model is
continuously valued, with the equipment of task-specific loss, it achieves
impressive results even on discrete labelling tasks
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