1,080 research outputs found
Using Convolutional Neural Networks in Robots with Limited Computational Resources: Detecting NAO Robots while Playing Soccer
The main goal of this paper is to analyze the general problem of using
Convolutional Neural Networks (CNNs) in robots with limited computational
capabilities, and to propose general design guidelines for their use. In
addition, two different CNN based NAO robot detectors that are able to run in
real-time while playing soccer are proposed. One of the detectors is based on
the XNOR-Net and the other on the SqueezeNet. Each detector is able to process
a robot object-proposal in ~1ms, with an average number of 1.5 proposals per
frame obtained by the upper camera of the NAO. The obtained detection rate is
~97%.Comment: Accepted in the RoboCup Symposium 2017. Final version will be
published at Springe
Associative Embedding for Game-Agnostic Team Discrimination
Assigning team labels to players in a sport game is not a trivial task when
no prior is known about the visual appearance of each team. Our work builds on
a Convolutional Neural Network (CNN) to learn a descriptor, namely a pixel-wise
embedding vector, that is similar for pixels depicting players from the same
team, and dissimilar when pixels correspond to distinct teams. The advantage of
this idea is that no per-game learning is needed, allowing efficient team
discrimination as soon as the game starts. In principle, the approach follows
the associative embedding framework introduced in arXiv:1611.05424 to
differentiate instances of objects. Our work is however different in that it
derives the embeddings from a lightweight segmentation network and, more
fundamentally, because it considers the assignment of the same embedding to
unconnected pixels, as required by pixels of distinct players from the same
team. Excellent results, both in terms of team labelling accuracy and
generalization to new games/arenas, have been achieved on panoramic views of a
large variety of basketball games involving players interactions and
occlusions. This makes our method a good candidate to integrate team separation
in many CNN-based sport analytics pipelines.Comment: Published in CVPR 2019 workshop Computer Vision in Sports, under the
name "Associative Embedding for Team Discrimination"
(http://openaccess.thecvf.com/content_CVPRW_2019/html/CVSports/Istasse_Associative_Embedding_for_Team_Discrimination_CVPRW_2019_paper.html
Adversarial Machine Learning For Advanced Medical Imaging Systems
Although deep neural networks (DNNs) have achieved significant advancement in various challenging tasks of computer vision, they are also known to be vulnerable to so-called adversarial attacks. With only imperceptibly small perturbations added to a clean image, adversarial samples can drastically change models’ prediction, resulting in a significant drop in DNN’s performance. This phenomenon poses a serious threat to security-critical applications of DNNs, such as medical imaging, autonomous driving, and surveillance systems. In this dissertation, we present adversarial machine learning approaches for natural image classification and advanced medical imaging systems.
We start by describing our advanced medical imaging systems to tackle the major challenges of on-device deployment: automation, uncertainty, and resource constraint. It is followed by novel unsupervised and semi-supervised robust training schemes to enhance the adversarial robustness of these medical imaging systems. These methods are designed to tackle the unique challenges of defending against adversarial attacks on medical imaging systems and are sufficiently flexible to generalize to various medical imaging modalities and problems. We continue on developing novel training scheme to enhance adversarial robustness of the general DNN based natural image classification models. Based on a unique insight into the predictive behavior of DNNs that they tend to misclassify adversarial samples into the most probable false classes, we propose a new loss function as a drop-in replacement for the cross-entropy loss to improve DNN\u27s adversarial robustness. Specifically, it enlarges the probability gaps between true class and false classes and prevents them from being melted by small perturbations. Finally, we conclude the dissertation by summarizing original contributions and discussing our future work that leverages DNN interpretability constraint on adversarial training to tackle the central machine learning problem of generalization gap
Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review
The Human Activity Recognition (HAR) tasks automatically identify human
activities using the sensor data, which has numerous applications in
healthcare, sports, security, and human-computer interaction. Despite
significant advances in HAR, critical challenges still exist. Game theory has
emerged as a promising solution to address these challenges in machine learning
problems including HAR. However, there is a lack of research work on applying
game theory solutions to the HAR problems. This review paper explores the
potential of game theory as a solution for HAR tasks, and bridges the gap
between game theory and HAR research work by suggesting novel game-theoretic
approaches for HAR problems. The contributions of this work include exploring
how game theory can improve the accuracy and robustness of HAR models,
investigating how game-theoretic concepts can optimize recognition algorithms,
and discussing the game-theoretic approaches against the existing HAR methods.
The objective is to provide insights into the potential of game theory as a
solution for sensor-based HAR, and contribute to develop a more accurate and
efficient recognition system in the future research directions
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