27 research outputs found
Spotting Agreement and Disagreement: A Survey of Nonverbal Audiovisual Cues and Tools
While detecting and interpreting temporal patterns of nonâverbal behavioral cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and humanâcentered interaction they are bound to experience. This paper surveys the nonverbal cues that could be present during agreement and disagreement behavioural displays and lists a number of tools that could be useful in detecting them, as well as a few publicly available databases that could be used to train these tools for analysis of spontaneous, audiovisual instances of agreement and disagreement
A deep matrix factorization method for learning attribute representations
Semi-Non-negative Matrix Factorization is a technique that learns a
low-dimensional representation of a dataset that lends itself to a clustering
interpretation. It is possible that the mapping between this new representation
and our original data matrix contains rather complex hierarchical information
with implicit lower-level hidden attributes, that classical one level
clustering methodologies can not interpret. In this work we propose a novel
model, Deep Semi-NMF, that is able to learn such hidden representations that
allow themselves to an interpretation of clustering according to different,
unknown attributes of a given dataset. We also present a semi-supervised
version of the algorithm, named Deep WSF, that allows the use of (partial)
prior information for each of the known attributes of a dataset, that allows
the model to be used on datasets with mixed attribute knowledge. Finally, we
show that our models are able to learn low-dimensional representations that are
better suited for clustering, but also classification, outperforming
Semi-Non-negative Matrix Factorization, but also other state-of-the-art
methodologies variants.Comment: Submitted to TPAMI (16-Mar-2015
Infinite Hidden Conditional Random Fields for the Recognition of Human Behaviour
While detecting and interpreting temporal patterns of nonverbal behavioral cues
in a given context is a natural and often unconscious process for humans, it
remains a rather difficult task for computer systems.
In this thesis we are primarily motivated by the problem of recognizing
expressions of high--level behavior, and specifically agreement and
disagreement.
We thoroughly dissect the problem by surveying the nonverbal behavioral cues
that could be present during displays of agreement and disagreement; we discuss
a number of methods that could be used or adapted to detect these suggested
cues; we list some publicly available databases these tools could be trained on
for the analysis of spontaneous, audiovisual instances of agreement and
disagreement, we examine the few existing attempts at agreement and disagreement
classification, and we discuss the challenges in automatically detecting
agreement and disagreement.
We present
experiments that show that an existing discriminative graphical model, the
Hidden Conditional Random Field (HCRF) is the best performing on this task. The
HCRF is a discriminative latent variable model which has been previously shown
to successfully learn the hidden structure of a given classification problem
(provided an appropriate validation of the number of hidden states).
We show here that HCRFs are also able to capture what makes each of these social
attitudes unique. We present an efficient technique to analyze the concepts
learned by the HCRF model and show that these coincide with the findings from
social psychology regarding which cues are most prevalent in agreement and
disagreement. Our experiments are performed on a spontaneous expressions dataset
curated from real televised debates.
The HCRF model outperforms conventional approaches such as Hidden Markov Models
and Support Vector Machines.
Subsequently, we examine existing graphical models that use Bayesian
nonparametrics to have a countably infinite number of hidden states and adapt
their complexity to the data at hand.
We identify a gap in the literature that is the lack of a discriminative such
graphical model and we present our suggestion for the first such model: an HCRF
with an infinite number of hidden states, the Infinite Hidden Conditional Random
Field (IHCRF).
In summary, the IHCRF is an undirected discriminative graphical model for
sequence classification and uses a countably infinite number of hidden states.
We present two variants of this model. The first is a fully nonparametric model
that relies on Hierarchical Dirichlet Processes and a Markov Chain Monte Carlo
inference approach. The second is a semi--parametric model that uses Dirichlet
Process Mixtures and relies on a mean--field variational inference approach. We
show that both models are able to converge to a correct number of represented
hidden states, and perform as well as the best finite HCRFs ---chosen via
cross--validation--- for the difficult tasks of recognizing instances of
agreement, disagreement, and pain in audiovisual sequences.Open Acces
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Instrumenting and collecting annotated visual grasping datasets to train
modern machine learning algorithms can be extremely time-consuming and
expensive. An appealing alternative is to use off-the-shelf simulators to
render synthetic data for which ground-truth annotations are generated
automatically. Unfortunately, models trained purely on simulated data often
fail to generalize to the real world. We study how randomized simulated
environments and domain adaptation methods can be extended to train a grasping
system to grasp novel objects from raw monocular RGB images. We extensively
evaluate our approaches with a total of more than 25,000 physical test grasps,
studying a range of simulation conditions and domain adaptation methods,
including a novel extension of pixel-level domain adaptation that we term the
GraspGAN. We show that, by using synthetic data and domain adaptation, we are
able to reduce the number of real-world samples needed to achieve a given level
of performance by up to 50 times, using only randomly generated simulated
objects. We also show that by using only unlabeled real-world data and our
GraspGAN methodology, we obtain real-world grasping performance without any
real-world labels that is similar to that achieved with 939,777 labeled
real-world samples.Comment: 9 pages, 5 figures, 3 table
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning
Many advances that have improved the robustness and efficiency of deep
reinforcement learning (RL) algorithms can, in one way or another, be
understood as introducing additional objectives, or constraints, in the policy
optimization step. This includes ideas as far ranging as exploration bonuses,
entropy regularization, and regularization toward teachers or data priors when
learning from experts or in offline RL. Often, task reward and auxiliary
objectives are in conflict with each other and it is therefore natural to treat
these examples as instances of multi-objective (MO) optimization problems. We
study the principles underlying MORL and introduce a new algorithm,
Distillation of a Mixture of Experts (DiME), that is intuitive and
scale-invariant under some conditions. We highlight its strengths on standard
MO benchmark problems and consider case studies in which we recast offline RL
and learning from experts as MO problems. This leads to a natural algorithmic
formulation that sheds light on the connection between existing approaches. For
offline RL, we use the MO perspective to derive a simple algorithm, that
optimizes for the standard RL objective plus a behavioral cloning term. This
outperforms state-of-the-art on two established offline RL benchmarks