3,722 research outputs found
Image to Image Translation for Domain Adaptation
We propose a general framework for unsupervised domain adaptation, which
allows deep neural networks trained on a source domain to be tested on a
different target domain without requiring any training annotations in the
target domain. This is achieved by adding extra networks and losses that help
regularize the features extracted by the backbone encoder network. To this end
we propose the novel use of the recently proposed unpaired image-toimage
translation framework to constrain the features extracted by the encoder
network. Specifically, we require that the features extracted are able to
reconstruct the images in both domains. In addition we require that the
distribution of features extracted from images in the two domains are
indistinguishable. Many recent works can be seen as specific cases of our
general framework. We apply our method for domain adaptation between MNIST,
USPS, and SVHN datasets, and Amazon, Webcam and DSLR Office datasets in
classification tasks, and also between GTA5 and Cityscapes datasets for a
segmentation task. We demonstrate state of the art performance on each of these
datasets
Human Motion Capture Data Tailored Transform Coding
Human motion capture (mocap) is a widely used technique for digitalizing
human movements. With growing usage, compressing mocap data has received
increasing attention, since compact data size enables efficient storage and
transmission. Our analysis shows that mocap data have some unique
characteristics that distinguish themselves from images and videos. Therefore,
directly borrowing image or video compression techniques, such as discrete
cosine transform, does not work well. In this paper, we propose a novel
mocap-tailored transform coding algorithm that takes advantage of these
features. Our algorithm segments the input mocap sequences into clips, which
are represented in 2D matrices. Then it computes a set of data-dependent
orthogonal bases to transform the matrices to frequency domain, in which the
transform coefficients have significantly less dependency. Finally, the
compression is obtained by entropy coding of the quantized coefficients and the
bases. Our method has low computational cost and can be easily extended to
compress mocap databases. It also requires neither training nor complicated
parameter setting. Experimental results demonstrate that the proposed scheme
significantly outperforms state-of-the-art algorithms in terms of compression
performance and speed
DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning
In this paper, we investigate estimating emergence and biomass traits from
color images and elevation maps of wheat field plots. We employ a
state-of-the-art deconvolutional network for segmentation and convolutional
architectures, with residual and Inception-like layers, to estimate traits via
high dimensional nonlinear regression. Evaluation was performed on two
different species of wheat, grown in field plots for an experimental plant
breeding study. Our framework achieves satisfactory performance with mean and
standard deviation of absolute difference of 1.05 and 1.40 counts for emergence
and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants
from field images are better than the accuracy reported for the similar, but
arguably less difficult, task of counting leaves from indoor images of rosette
plants. Our results for biomass estimation, even with a very small dataset,
improve upon all previously proposed approaches in the literature.Comment: WACV 2018 (Code repository:
https://github.com/p2irc/deepwheat_WACV-2018
Intersubject Regularity in the Intrinsic Shape of Human V1
Previous studies have reported considerable intersubject variability in the three-dimensional geometry of the human primary visual cortex (V1). Here we demonstrate that much of this variability is due to extrinsic geometric features of the cortical folds, and that the intrinsic shape of V1 is similar across individuals. V1 was imaged in ten ex vivo human hemispheres using high-resolution (200 μm) structural magnetic resonance imaging at high field strength (7 T). Manual tracings of the stria of Gennari were used to construct a surface representation, which was computationally flattened into the plane with minimal metric distortion. The instrinsic shape of V1 was determined from the boundary of the planar representation of the stria. An ellipse provided a simple parametric shape model that was a good approximation to the boundary of flattened V1. The aspect ration of the best-fitting ellipse was found to be consistent across subject, with a mean of 1.85 and standard deviation of 0.12. Optimal rigid alignment of size-normalized V1 produced greater overlap than that achieved by previous studies using different registration methods. A shape analysis of published macaque data indicated that the intrinsic shape of macaque V1 is also stereotyped, and similar to the human V1 shape. Previoud measurements of the functional boundary of V1 in human and macaque are in close agreement with these results
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