7,244 research outputs found
ThumbNet: One Thumbnail Image Contains All You Need for Recognition
Although deep convolutional neural networks (CNNs) have achieved great
success in computer vision tasks, its real-world application is still impeded
by its voracious demand of computational resources. Current works mostly seek
to compress the network by reducing its parameters or parameter-incurred
computation, neglecting the influence of the input image on the system
complexity. Based on the fact that input images of a CNN contain substantial
redundancy, in this paper, we propose a unified framework, dubbed as ThumbNet,
to simultaneously accelerate and compress CNN models by enabling them to infer
on one thumbnail image. We provide three effective strategies to train
ThumbNet. In doing so, ThumbNet learns an inference network that performs
equally well on small images as the original-input network on large images.
With ThumbNet, not only do we obtain the thumbnail-input inference network that
can drastically reduce computation and memory requirements, but also we obtain
an image downscaler that can generate thumbnail images for generic
classification tasks. Extensive experiments show the effectiveness of ThumbNet,
and demonstrate that the thumbnail-input inference network learned by ThumbNet
can adequately retain the accuracy of the original-input network even when the
input images are downscaled 16 times
Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis
We introduce a data-driven approach to complete partial 3D shapes through a
combination of volumetric deep neural networks and 3D shape synthesis. From a
partially-scanned input shape, our method first infers a low-resolution -- but
complete -- output. To this end, we introduce a 3D-Encoder-Predictor Network
(3D-EPN) which is composed of 3D convolutional layers. The network is trained
to predict and fill in missing data, and operates on an implicit surface
representation that encodes both known and unknown space. This allows us to
predict global structure in unknown areas at high accuracy. We then correlate
these intermediary results with 3D geometry from a shape database at test time.
In a final pass, we propose a patch-based 3D shape synthesis method that
imposes the 3D geometry from these retrieved shapes as constraints on the
coarsely-completed mesh. This synthesis process enables us to reconstruct
fine-scale detail and generate high-resolution output while respecting the
global mesh structure obtained by the 3D-EPN. Although our 3D-EPN outperforms
state-of-the-art completion method, the main contribution in our work lies in
the combination of a data-driven shape predictor and analytic 3D shape
synthesis. In our results, we show extensive evaluations on a newly-introduced
shape completion benchmark for both real-world and synthetic data
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Research has proven that stress reduces quality of life and causes many
diseases. For this reason, several researchers devised stress detection systems
based on physiological parameters. However, these systems require that
obtrusive sensors are continuously carried by the user. In our paper, we
propose an alternative approach providing evidence that daily stress can be
reliably recognized based on behavioral metrics, derived from the user's mobile
phone activity and from additional indicators, such as the weather conditions
(data pertaining to transitory properties of the environment) and the
personality traits (data concerning permanent dispositions of individuals). Our
multifactorial statistical model, which is person-independent, obtains the
accuracy score of 72.28% for a 2-class daily stress recognition problem. The
model is efficient to implement for most of multimedia applications due to
highly reduced low-dimensional feature space (32d). Moreover, we identify and
discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US
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