11,582 research outputs found
Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation
We propose a convolutional network with hierarchical classifiers for
per-pixel semantic segmentation, which is able to be trained on multiple,
heterogeneous datasets and exploit their semantic hierarchy. Our network is the
first to be simultaneously trained on three different datasets from the
intelligent vehicles domain, i.e. Cityscapes, GTSDB and Mapillary Vistas, and
is able to handle different semantic level-of-detail, class imbalances, and
different annotation types, i.e. dense per-pixel and sparse bounding-box
labels. We assess our hierarchical approach, by comparing against flat,
non-hierarchical classifiers and we show improvements in mean pixel accuracy of
13.0% for Cityscapes classes and 2.4% for Vistas classes and 32.3% for GTSDB
classes. Our implementation achieves inference rates of 17 fps at a resolution
of 520x706 for 108 classes running on a GPU.Comment: IEEE Intelligent Vehicles 201
Clustering-Based Predictive Process Monitoring
Business process enactment is generally supported by information systems that
record data about process executions, which can be extracted as event logs.
Predictive process monitoring is concerned with exploiting such event logs to
predict how running (uncompleted) cases will unfold up to their completion. In
this paper, we propose a predictive process monitoring framework for estimating
the probability that a given predicate will be fulfilled upon completion of a
running case. The predicate can be, for example, a temporal logic constraint or
a time constraint, or any predicate that can be evaluated over a completed
trace. The framework takes into account both the sequence of events observed in
the current trace, as well as data attributes associated to these events. The
prediction problem is approached in two phases. First, prefixes of previous
traces are clustered according to control flow information. Secondly, a
classifier is built for each cluster using event data to discriminate between
fulfillments and violations. At runtime, a prediction is made on a running case
by mapping it to a cluster and applying the corresponding classifier. The
framework has been implemented in the ProM toolset and validated on a log
pertaining to the treatment of cancer patients in a large hospital
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