1,352 research outputs found
Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection
To assure that an autonomous car is driving safely on public roads, its
object detection module should not only work correctly, but show its prediction
confidence as well. Previous object detectors driven by deep learning do not
explicitly model uncertainties in the neural network. We tackle with this
problem by presenting practical methods to capture uncertainties in a 3D
vehicle detector for Lidar point clouds. The proposed probabilistic detector
represents reliable epistemic uncertainty and aleatoric uncertainty in
classification and localization tasks. Experimental results show that the
epistemic uncertainty is related to the detection accuracy, whereas the
aleatoric uncertainty is influenced by vehicle distance and occlusion. The
results also show that we can improve the detection performance by 1%-5% by
modeling the aleatoric uncertainty.Comment: Accepted to present in the 21st IEEE International Conference on
Intelligent Transportation Systems (ITSC 2018
Docent: A content-based recommendation system to discover contemporary art
Recommendation systems have been widely used in various domains such as
music, films, e-shopping etc. After mostly avoiding digitization, the art world
has recently reached a technological turning point due to the pandemic, making
online sales grow significantly as well as providing quantitative online data
about artists and artworks. In this work, we present a content-based
recommendation system on contemporary art relying on images of artworks and
contextual metadata of artists. We gathered and annotated artworks with
advanced and art-specific information to create a completely unique database
that was used to train our models. With this information, we built a proximity
graph between artworks. Similarly, we used NLP techniques to characterize the
practices of the artists and we extracted information from exhibitions and
other event history to create a proximity graph between artists. The power of
graph analysis enables us to provide an artwork recommendation system based on
a combination of visual and contextual information from artworks and artists.
After an assessment by a team of art specialists, we get an average final
rating of 75% of meaningful artworks when compared to their professional
evaluations.Comment: submitted to NeurIPS202
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