394 research outputs found
The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots
Deep networks have brought significant advances in robot perception, enabling
to improve the capabilities of robots in several visual tasks, ranging from
object detection and recognition to pose estimation, semantic scene
segmentation and many others. Still, most approaches typically address visual
tasks in isolation, resulting in overspecialized models which achieve strong
performances in specific applications but work poorly in other (often related)
tasks. This is clearly sub-optimal for a robot which is often required to
perform simultaneously multiple visual recognition tasks in order to properly
act and interact with the environment. This problem is exacerbated by the
limited computational and memory resources typically available onboard to a
robotic platform. The problem of learning flexible models which can handle
multiple tasks in a lightweight manner has recently gained attention in the
computer vision community and benchmarks supporting this research have been
proposed. In this work we study this problem in the robot vision context,
proposing a new benchmark, the RGB-D Triathlon, and evaluating state of the art
algorithms in this novel challenging scenario. We also define a new evaluation
protocol, better suited to the robot vision setting. Results shed light on the
strengths and weaknesses of existing approaches and on open issues, suggesting
directions for future research.Comment: This work has been submitted to IROS/RAL 201
Best Sources Forward: Domain Generalization through Source-Specific Nets
A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions. The problem has been formalized as a covariate shift among the probability distributions generating the training data (source) and the test data (target) and several domain adaptation methods have been proposed to address this issue. While these approaches have considered the single source-single target scenario, it is plausible to have multiple sources and require adaptation to any possible target domain. This last scenario, named Domain Generalization (DG), is the focus of our work. Differently from previous DG methods which learn domain invariant representations from source data, we design a deep network with multiple domain-specific classifiers, each associated to a source domain. At test time we estimate the probabilities that a target sample belongs to each source domain and exploit them to optimally fuse the classifiers predictions. To further improve the generalization ability of our model, we also introduced a domain agnostic component supporting the final classifier. Experiments on two public benchmarks demonstrate the power of our approach
Robust Place Categorization With Deep Domain Generalization
Traditional place categorization approaches in robot vision assume that training and test images have similar visual appearance. Therefore, any seasonal, illumination, and environmental changes typically lead to severe degradation in performance. To cope with this problem, recent works have been proposed to adopt domain adaptation techniques. While effective, these methods assume that some prior information about the scenario where the robot will operate is available at training time. Unfortunately, in many cases, this assumption does not hold, as we often do not know where a robot will be deployed. To overcome this issue, in this paper, we present an approach that aims at learning classification models able to generalize to unseen scenarios. Specifically, we propose a novel deep learning framework for domain generalization. Our method develops from the intuition that, given a set of different classification models associated to known domains (e.g., corresponding to multiple environments, robots), the best model for a new sample in the novel domain can be computed directly at test time by optimally combining the known models. To implement our idea, we exploit recent advances in deep domain adaptation and design a convolutional neural network architecture with novel layers performing a weighted version of batch normalization. Our experiments, conducted on three common datasets for robot place categorization, confirm the validity of our contribution
Learning Deep NBNN Representations for Robust Place Categorization
This paper presents an approach for semantic place categorization using data
obtained from RGB cameras. Previous studies on visual place recognition and
classification have shown that, by considering features derived from
pre-trained Convolutional Neural Networks (CNNs) in combination with part-based
classification models, high recognition accuracy can be achieved, even in
presence of occlusions and severe viewpoint changes. Inspired by these works,
we propose to exploit local deep representations, representing images as set of
regions applying a Na\"{i}ve Bayes Nearest Neighbor (NBNN) model for image
classification. As opposed to previous methods where CNNs are merely used as
feature extractors, our approach seamlessly integrates the NBNN model into a
fully-convolutional neural network. Experimental results show that the proposed
algorithm outperforms previous methods based on pre-trained CNN models and
that, when employed in challenging robot place recognition tasks, it is robust
to occlusions, environmental and sensor changes
Boosting Deep Open World Recognition by Clustering
While convolutional neural networks have brought significant advances in
robot vision, their ability is often limited to closed world scenarios, where
the number of semantic concepts to be recognized is determined by the available
training set. Since it is practically impossible to capture all possible
semantic concepts present in the real world in a single training set, we need
to break the closed world assumption, equipping our robot with the capability
to act in an open world. To provide such ability, a robot vision system should
be able to (i) identify whether an instance does not belong to the set of known
categories (i.e. open set recognition), and (ii) extend its knowledge to learn
new classes over time (i.e. incremental learning). In this work, we show how we
can boost the performance of deep open world recognition algorithms by means of
a new loss formulation enforcing a global to local clustering of class-specific
features. In particular, a first loss term, i.e. global clustering, forces the
network to map samples closer to the class centroid they belong to while the
second one, local clustering, shapes the representation space in such a way
that samples of the same class get closer in the representation space while
pushing away neighbours belonging to other classes. Moreover, we propose a
strategy to learn class-specific rejection thresholds, instead of heuristically
estimating a single global threshold, as in previous works. Experiments on
RGB-D Object and Core50 datasets show the effectiveness of our approach.Comment: IROS/RAL 202
Dysfunctional eating behaviours, anxiety and depression in Italian boys and girls: the role of mass media
Objective: Extensive research has implicated identification with characters in mass media in the emergence of disordered eating behavior in adolescents. We explored the possible influence of the models offered by television (TV) on adolescents’ body image, body uneasiness, eating-disordered behavior, depression, and anxiety. Methods: Three hundred and one adolescents (aged 14-19) from southern Italy participated. They completed a questionnaire on media exposure and body dissatisfaction, the Eating Disorder Inventory-2, the Body Uneasiness Test, the Beck Depression Inventory, and the State-Trait Anxiety Inventory – Form Y. Results: The main factors contributing to females’ eating-disordered behaviors were their own desires to be similar to TV characters, the amount of reality and entertainment TV they watched, and the discrepancy between their perceptions of their bodies and those of TV characters. Friends’ desire to be similar to TV characters contributed most to depression, anxiety, body uneasiness, and eating disorders for both males and females. Conclusion: Our data confirm that extensive watching of reality and entertainment TV correlates with eating-disordered behavior among females. Moreover, the well-known negative effects of the media on adolescents’ eating-disordered behaviors may also be indirectly transmitted by friends who share identification with TV characters
Association between attention and heart rate fluctuations in pathological worriers
Recent data suggests that several psychopathological conditions are associated with alterations in the variability of behavioral and physiological responses. Pathological worry, defined as the cognitive representation of a potential threat, has been associated with reduced variability of heart beat oscillations (i.e., decreased heart rate variability; HRV) and lapses of attention indexed by reaction times (RTs). Clinical populations with attention deficit show RTs oscillation around 0.05 and 0.01 Hz when performing a sustained attention task. We tested the hypothesis that people who are prone to worry do it in a predictable oscillating pattern revealed through recurrent lapses in attention and concomitant oscillating HRV. Sixty healthy young adults (50% women) were recruited: 30 exceeded the clinical cut-off on the Penn State Worry Questionnaire (PSWQ; High-Worry, HW); the remaining 30 constituted the Low-Worry (LW) group. After a diagnostic assessment, participants performed two 15-min sustained attention tasks, interspersed by a standardized worry-induction procedure. RTs, HRV and moods were assessed. The analyses of the frequency spectrum showed that the HW group presents a significant higher and constant peak of RTs oscillation around 0.01 Hz (period 100 s) after the induction of worry, in comparison with their baseline and with the LW group that was not responsive to the induction procedure. Physiologically, the induction significantly reduced high-frequency HRV and such reduction was associated with levels of self-reported worry. Results are coherent with the oscillatory nature of the default mode network (DMN) and further confirm an association between cognitive rigidity and autonomic nervous system inflexibility
Validation of Geant4 nuclear reaction models for hadrontherapy and preliminary results with SMF and BLOB
Reliable nuclear fragmentation models are of utmost importance in hadrontherapy, where Monte Carlo (MC) simulations are used to compute the input parameters of the treatment planning software, to validate the deposited dose calculation, to evaluate the biological effectiveness of the radiation, to correlate the bĂľ emitters production in the patient body with the delivered dose, and to allow a non- invasive treatment verification.
Despite of its large use, the models implemented in Geant4 have shown severe limitations in reproducing the measured secondaries yields in ions interaction below 100 MeV/A, in term of production rates, angular and energy distributions [1–3]. We will present a benchmark of the Geant4 models with double-differential cross sec- tion and angular distributions of the secondary fragments produced in the 12C fragmentation at 62 MeV/A on thin carbon target, such a benchmark includes the recently implemented model INCL++ [4,5]. Moreover, we will present the preliminary results, obtained in simulating the same interaction, with SMF [6] and BLOB [7]. Both, SMF and BLOB are semiclassical one-body approaches to solve the Boltzmann-Langevin equation. They include an identical treatment of the mean-field propagation, on the basis of the same effective interaction, but they differ in the way fluctuations are included.
In particular, while SMF employs a Uehling-Uhlenbeck collision term and introduces fluctuations as projected on the density space, BLOB introduces fluctuations in full phase space through a modified collision term where nucleon-nucleon correlations are explicitly involved. Both of them, SMF and BLOB, have been developed to sim- ulate the heavy ion interactions in the Fermi-energy regime. We will show their capabilities in describing 12C fragmentation foreseen their implementation in Geant4
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