Spotting Agreement and Disagreement: A Survey of Nonverbal Audiovisual Cues and Tools

While detecting and interpreting temporal patterns of non–verbal 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 human–centered 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