6,485 research outputs found
How is Gaze Influenced by Image Transformations? Dataset and Model
Data size is the bottleneck for developing deep saliency models, because
collecting eye-movement data is very time consuming and expensive. Most of
current studies on human attention and saliency modeling have used high quality
stereotype stimuli. In real world, however, captured images undergo various
types of transformations. Can we use these transformations to augment existing
saliency datasets? Here, we first create a novel saliency dataset including
fixations of 10 observers over 1900 images degraded by 19 types of
transformations. Second, by analyzing eye movements, we find that observers
look at different locations over transformed versus original images. Third, we
utilize the new data over transformed images, called data augmentation
transformation (DAT), to train deep saliency models. We find that label
preserving DATs with negligible impact on human gaze boost saliency prediction,
whereas some other DATs that severely impact human gaze degrade the
performance. These label preserving valid augmentation transformations provide
a solution to enlarge existing saliency datasets. Finally, we introduce a novel
saliency model based on generative adversarial network (dubbed GazeGAN). A
modified UNet is proposed as the generator of the GazeGAN, which combines
classic skip connections with a novel center-surround connection (CSC), in
order to leverage multi level features. We also propose a histogram loss based
on Alternative Chi Square Distance (ACS HistLoss) to refine the saliency map in
terms of luminance distribution. Extensive experiments and comparisons over 3
datasets indicate that GazeGAN achieves the best performance in terms of
popular saliency evaluation metrics, and is more robust to various
perturbations. Our code and data are available at:
https://github.com/CZHQuality/Sal-CFS-GAN
A machine learning approach for efficient uncertainty quantification using multiscale methods
Several multiscale methods account for sub-grid scale features using coarse
scale basis functions. For example, in the Multiscale Finite Volume method the
coarse scale basis functions are obtained by solving a set of local problems
over dual-grid cells. We introduce a data-driven approach for the estimation of
these coarse scale basis functions. Specifically, we employ a neural network
predictor fitted using a set of solution samples from which it learns to
generate subsequent basis functions at a lower computational cost than solving
the local problems. The computational advantage of this approach is realized
for uncertainty quantification tasks where a large number of realizations has
to be evaluated. We attribute the ability to learn these basis functions to the
modularity of the local problems and the redundancy of the permeability patches
between samples. The proposed method is evaluated on elliptic problems yielding
very promising results.Comment: Journal of Computational Physics (2017
SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes
The objective of this paper is 3D shape understanding from single and
multiple images. To this end, we introduce a new deep-learning architecture and
loss function, SilNet, that can handle multiple views in an order-agnostic
manner. The architecture is fully convolutional, and for training we use a
proxy task of silhouette prediction, rather than directly learning a mapping
from 2D images to 3D shape as has been the target in most recent work.
We demonstrate that with the SilNet architecture there is generalisation over
the number of views -- for example, SilNet trained on 2 views can be used with
3 or 4 views at test-time; and performance improves with more views.
We introduce two new synthetics datasets: a blobby object dataset useful for
pre-training, and a challenging and realistic sculpture dataset; and
demonstrate on these datasets that SilNet has indeed learnt 3D shape. Finally,
we show that SilNet exceeds the state of the art on the ShapeNet benchmark
dataset, and use SilNet to generate novel views of the sculpture dataset.Comment: BMVC 2017; Best Poste
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