38,358 research outputs found
Learning Visual Question Answering by Bootstrapping Hard Attention
Attention mechanisms in biological perception are thought to select subsets
of perceptual information for more sophisticated processing which would be
prohibitive to perform on all sensory inputs. In computer vision, however,
there has been relatively little exploration of hard attention, where some
information is selectively ignored, in spite of the success of soft attention,
where information is re-weighted and aggregated, but never filtered out. Here,
we introduce a new approach for hard attention and find it achieves very
competitive performance on a recently-released visual question answering
datasets, equalling and in some cases surpassing similar soft attention
architectures while entirely ignoring some features. Even though the hard
attention mechanism is thought to be non-differentiable, we found that the
feature magnitudes correlate with semantic relevance, and provide a useful
signal for our mechanism's attentional selection criterion. Because hard
attention selects important features of the input information, it can also be
more efficient than analogous soft attention mechanisms. This is especially
important for recent approaches that use non-local pairwise operations, whereby
computational and memory costs are quadratic in the size of the set of
features.Comment: ECCV 201
A reusable iterative optimization software library to solve combinatorial problems with approximate reasoning
Real world combinatorial optimization problems such as scheduling are
typically too complex to solve with exact methods. Additionally, the problems
often have to observe vaguely specified constraints of different importance,
the available data may be uncertain, and compromises between antagonistic
criteria may be necessary. We present a combination of approximate reasoning
based constraints and iterative optimization based heuristics that help to
model and solve such problems in a framework of C++ software libraries called
StarFLIP++. While initially developed to schedule continuous caster units in
steel plants, we present in this paper results from reusing the library
components in a shift scheduling system for the workforce of an industrial
production plant.Comment: 33 pages, 9 figures; for a project overview see
http://www.dbai.tuwien.ac.at/proj/StarFLIP
An approach for uncertainty aggregation using generalised conjunction/disjunction aggregators
Decision Support Systems are often used in the area of system evaluation. The quality of the output of such a system is only as good as the quality of the data that is used as input. Uncertainty on data, if not taken into account, can lead to evaluation results that are not representative. In this paper, we propose a technique to extend Generalised Con- junction/Disjunction aggregators to deal with un- certainty in Decision Support Systems. We first de- fine the logic properties of uncertainty aggregation through reasoning on strict aggregators and after- wards extend this logic to partial aggregators
Assessment of Sustainable Development
The objective of this paper is to introduce fuzzy set theory and develop fuzzy mathematical models to assess sustainable development based on context-dependent economic, ecological, and societal sustainability indicators. Membership functions are at the core of fuzzy models, and define the degree to which indicators contribute to development. Although a decision-making process regarding sustainable development is subjective, fuzzy set theory links human expectations about development, expressed in linguistic propositions, to numerical data, expressed in measurements of sustainability indicators. In the future, practical implementation of such models will be based on elicitation of expert knowledge to construct a membership function. The fuzzy models developed in this paper provide a novel approach to support decisions regarding sustainable development.agriculture;assessment;fuzzy set theory;sustainable development
Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory
Land cover classification using multispectral satellite image is a very
challenging task with numerous practical applications. We propose a multi-stage
classifier that involves fuzzy rule extraction from the training data and then
generation of a possibilistic label vector for each pixel using the fuzzy rule
base. To exploit the spatial correlation of land cover types we propose four
different information aggregation methods which use the possibilistic class
label of a pixel and those of its eight spatial neighbors for making the final
classification decision. Three of the aggregation methods use Dempster-Shafer
theory of evidence while the remaining one is modeled after the fuzzy k-NN
rule. The proposed methods are tested with two benchmark seven channel
satellite images and the results are found to be quite satisfactory. They are
also compared with a Markov random field (MRF) model-based contextual
classification method and found to perform consistently better.Comment: 14 pages, 2 figure
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