17,013 research outputs found
Multimodal Classification of Urban Micro-Events
In this paper we seek methods to effectively detect urban micro-events. Urban
micro-events are events which occur in cities, have limited geographical
coverage and typically affect only a small group of citizens. Because of their
scale these are difficult to identify in most data sources. However, by using
citizen sensing to gather data, detecting them becomes feasible. The data
gathered by citizen sensing is often multimodal and, as a consequence, the
information required to detect urban micro-events is distributed over multiple
modalities. This makes it essential to have a classifier capable of combining
them. In this paper we explore several methods of creating such a classifier,
including early, late, hybrid fusion and representation learning using
multimodal graphs. We evaluate performance on a real world dataset obtained
from a live citizen reporting system. We show that a multimodal approach yields
higher performance than unimodal alternatives. Furthermore, we demonstrate that
our hybrid combination of early and late fusion with multimodal embeddings
performs best in classification of urban micro-events
Improvement of the sensory and autonomous capability of robots through olfaction: the IRO Project
Proyecto de Excelencia Junta de Andalucía TEP2012-530Olfaction is a valuable source of information about the environment that has not been su ciently exploited in mobile robotics
yet. Certainly, odor information can contribute to other sensing modalities, e.g. vision, to successfully accomplish high-level robot
activities, such as task planning or execution in human environments. This paper describes the developments carried out in the scope of the IRO project, which aims at making progress in this direction by investigating mechanisms that exploit odor information (usually coming in the form of the type of volatile and its concentration) in problems like object recognition and scene-activity understanding. A distinctive aspect of this research is the special attention paid to the role of semantics within the robot perception and decisionmaking processes. The results of the IRO project have improved the robot capabilities in terms of efciency, autonomy and usefulness.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec
Data Cube Approximation and Mining using Probabilistic Modeling
On-line Analytical Processing (OLAP) techniques commonly used in data warehouses allow the exploration of data cubes according to different analysis axes (dimensions) and under different abstraction levels in a dimension hierarchy. However, such techniques are not aimed at mining multidimensional data.
Since data cubes are nothing but multi-way tables, we propose to analyze the potential of two probabilistic modeling techniques, namely non-negative multi-way array factorization and log-linear modeling, with the ultimate objective of compressing and mining aggregate and multidimensional values. With the first technique, we compute the set of components that best fit the initial data set and whose superposition coincides with the original data; with the second technique we identify a parsimonious model (i.e., one with a reduced set of parameters), highlight strong associations among dimensions and discover possible outliers in data cells. A real life example will be
used to (i) discuss the potential benefits of the modeling output on cube exploration and mining, (ii) show how OLAP queries can be answered in an approximate way, and (iii) illustrate the strengths and limitations of these modeling approaches
Multi-Source Neural Variational Inference
Learning from multiple sources of information is an important problem in
machine-learning research. The key challenges are learning representations and
formulating inference methods that take into account the complementarity and
redundancy of various information sources. In this paper we formulate a
variational autoencoder based multi-source learning framework in which each
encoder is conditioned on a different information source. This allows us to
relate the sources via the shared latent variables by computing divergence
measures between individual source's posterior approximations. We explore a
variety of options to learn these encoders and to integrate the beliefs they
compute into a consistent posterior approximation. We visualise learned beliefs
on a toy dataset and evaluate our methods for learning shared representations
and structured output prediction, showing trade-offs of learning separate
encoders for each information source. Furthermore, we demonstrate how conflict
detection and redundancy can increase robustness of inference in a multi-source
setting.Comment: AAAI 2019, Association for the Advancement of Artificial Intelligence
(AAAI) 201
Multimodal Visual Concept Learning with Weakly Supervised Techniques
Despite the availability of a huge amount of video data accompanied by
descriptive texts, it is not always easy to exploit the information contained
in natural language in order to automatically recognize video concepts. Towards
this goal, in this paper we use textual cues as means of supervision,
introducing two weakly supervised techniques that extend the Multiple Instance
Learning (MIL) framework: the Fuzzy Sets Multiple Instance Learning (FSMIL) and
the Probabilistic Labels Multiple Instance Learning (PLMIL). The former encodes
the spatio-temporal imprecision of the linguistic descriptions with Fuzzy Sets,
while the latter models different interpretations of each description's
semantics with Probabilistic Labels, both formulated through a convex
optimization algorithm. In addition, we provide a novel technique to extract
weak labels in the presence of complex semantics, that consists of semantic
similarity computations. We evaluate our methods on two distinct problems,
namely face and action recognition, in the challenging and realistic setting of
movies accompanied by their screenplays, contained in the COGNIMUSE database.
We show that, on both tasks, our method considerably outperforms a
state-of-the-art weakly supervised approach, as well as other baselines.Comment: CVPR 201
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