597 research outputs found
Image Captioning and Classification of Dangerous Situations
Current robot platforms are being employed to collaborate with humans in a
wide range of domestic and industrial tasks. These environments require
autonomous systems that are able to classify and communicate anomalous
situations such as fires, injured persons, car accidents; or generally, any
potentially dangerous situation for humans. In this paper we introduce an
anomaly detection dataset for the purpose of robot applications as well as the
design and implementation of a deep learning architecture that classifies and
describes dangerous situations using only a single image as input. We report a
classification accuracy of 97 % and METEOR score of 16.2. We will make the
dataset publicly available after this paper is accepted
Real-time Convolutional Neural Networks for Emotion and Gender Classification
In this paper we propose an implement a general convolutional neural network
(CNN) building framework for designing real-time CNNs. We validate our models
by creating a real-time vision system which accomplishes the tasks of face
detection, gender classification and emotion classification simultaneously in
one blended step using our proposed CNN architecture. After presenting the
details of the training procedure setup we proceed to evaluate on standard
benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66%
in the FER-2013 emotion dataset. Along with this we also introduced the very
recent real-time enabled guided back-propagation visualization technique.
Guided back-propagation uncovers the dynamics of the weight changes and
evaluates the learned features. We argue that the careful implementation of
modern CNN architectures, the use of the current regularization methods and the
visualization of previously hidden features are necessary in order to reduce
the gap between slow performances and real-time architectures. Our system has
been validated by its deployment on a Care-O-bot 3 robot used during
RoboCup@Home competitions. All our code, demos and pre-trained architectures
have been released under an open-source license in our public repository.Comment: Submitted to ICRA 201
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Echo State Networks: analysis, training and predictive control
The goal of this paper is to investigate the theoretical properties, the
training algorithm, and the predictive control applications of Echo State
Networks (ESNs), a particular kind of Recurrent Neural Networks. First, a
condition guaranteeing incremetal global asymptotic stability is devised. Then,
a modified training algorithm allowing for dimensionality reduction of ESNs is
presented. Eventually, a model predictive controller is designed to solve the
tracking problem, relying on ESNs as the model of the system. Numerical results
concerning the predictive control of a nonlinear process for pH neutralization
confirm the effectiveness of the proposed algorithms for the identification,
dimensionality reduction, and the control design for ESNs.Comment: 6 pages,5 figures, submitted to European Control Conference (ECC
On Comments in Visual Languages
Visual languages based on node-link diagrams can be used to develop software and, like textual languages, offer the possibility to write explanatory comments. Which node a comment refers to is usually not made explicit, but is implicitly clear to readers through placement and content. While automatic layout algorithms can make working with diagrams more productive, they tend to destroy such implicit clues because they are not aware of them and thus do not preserve the relative placement of comments and the nodes they refer to. Implicit clues thus need to be inferred and made explicit to be taken into account by layout algorithms. This is what we call the comment attachment problem. In this paper, we improve upon a previous paper on the subject [9], introducing further heuristics that aim to describe relations between comments and nodes. Based on an analysis of comment placement in a set of example diagrams, we develop a general comment attachment framework and evaluate the quality of its inferred attachments
Forward-Looking Sonar Patch Matching:Modern CNNs, Ensembling, and Uncertainty
Application of underwater robots are on the rise, most of them are dependent on sonar for underwater vision, but the lack of strong perception capabilities limits them in this task. An important issue in sonar perception is matching image patches, which can enable other techniques like localization, change detection, and mapping. There is a rich literature for this problem in color images, but for acoustic images, it is lacking, due to the physics that produce these images. In this paper we improve on our previous results for this problem (Valdenegro-Toro et al, 2017), instead of modeling features manually, a Convolutional Neural Network (CNN) learns a similarity function and predicts if two input sonar images are similar or not. With the objective of improving the sonar image matching problem further, three state of the art CNN architectures are evaluated on the Marine Debris dataset, namely DenseNet, and VGG, with a siamese or two-channel architecture, and contrastive loss. To ensure a fair evaluation of each network, thorough hyper-parameter optimization is executed. We find that the best performing models are DenseNet Two-Channel network with 0.955 AUC, VGG-Siamese with contrastive loss at 0.949 AUC and DenseNet Siamese with 0.921 AUC. By ensembling the top performing DenseNet two-channel and DenseNet-Siamese models overall highest prediction accuracy obtained is 0.978 AUC, showing a large improvement over the 0.91 AUC in the state of the art
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