53,463 research outputs found
Who am I talking with? A face memory for social robots
In order to provide personalized services and to
develop human-like interaction capabilities robots need to rec-
ognize their human partner. Face recognition has been studied
in the past decade exhaustively in the context of security systems
and with significant progress on huge datasets. However, these
capabilities are not in focus when it comes to social interaction
situations. Humans are able to remember people seen for a
short moment in time and apply this knowledge directly in
their engagement in conversation. In order to equip a robot with
capabilities to recall human interlocutors and to provide user-
aware services, we adopt human-human interaction schemes to
propose a face memory on the basis of active appearance models
integrated with the active memory architecture. This paper
presents the concept of the interactive face memory, the applied
recognition algorithms, and their embedding into the robot’s
system architecture. Performance measures are discussed for
general face databases as well as scenario-specific datasets
Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification
In person re-identification (ReID) task, because of its shortage of trainable
dataset, it is common to utilize fine-tuning method using a classification
network pre-trained on a large dataset. However, it is relatively difficult to
sufficiently fine-tune the low-level layers of the network due to the gradient
vanishing problem. In this work, we propose a novel fine-tuning strategy that
allows low-level layers to be sufficiently trained by rolling back the weights
of high-level layers to their initial pre-trained weights. Our strategy
alleviates the problem of gradient vanishing in low-level layers and robustly
trains the low-level layers to fit the ReID dataset, thereby increasing the
performance of ReID tasks. The improved performance of the proposed strategy is
validated via several experiments. Furthermore, without any add-ons such as
pose estimation or segmentation, our strategy exhibits state-of-the-art
performance using only vanilla deep convolutional neural network architecture.Comment: Accepted to AAAI 201
Dropout Sampling for Robust Object Detection in Open-Set Conditions
Dropout Variational Inference, or Dropout Sampling, has been recently
proposed as an approximation technique for Bayesian Deep Learning and evaluated
for image classification and regression tasks. This paper investigates the
utility of Dropout Sampling for object detection for the first time. We
demonstrate how label uncertainty can be extracted from a state-of-the-art
object detection system via Dropout Sampling. We evaluate this approach on a
large synthetic dataset of 30,000 images, and a real-world dataset captured by
a mobile robot in a versatile campus environment. We show that this uncertainty
can be utilized to increase object detection performance under the open-set
conditions that are typically encountered in robotic vision. A Dropout Sampling
network is shown to achieve a 12.3% increase in recall (for the same precision
score as a standard network) and a 15.1% increase in precision (for the same
recall score as the standard network).Comment: to appear in IEEE International Conference on Robotics and Automation
2018 (ICRA 2018
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