95,545 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
The Theory of Quasiconformal Mappings in Higher Dimensions, I
We present a survey of the many and various elements of the modern
higher-dimensional theory of quasiconformal mappings and their wide and varied
application. It is unified (and limited) by the theme of the author's
interests. Thus we will discuss the basic theory as it developed in the 1960s
in the early work of F.W. Gehring and Yu G. Reshetnyak and subsequently explore
the connections with geometric function theory, nonlinear partial differential
equations, differential and geometric topology and dynamics as they ensued over
the following decades. We give few proofs as we try to outline the major
results of the area and current research themes. We do not strive to present
these results in maximal generality, as to achieve this considerable technical
knowledge would be necessary of the reader. We have tried to give a feel of
where the area is, what are the central ideas and problems and where are the
major current interactions with researchers in other areas. We have also added
a bit of history here and there. We have not been able to cover the many recent
advances generalising the theory to mappings of finite distortion and to
degenerate elliptic Beltrami systems which connects the theory closely with the
calculus of variations and nonlinear elasticity, nonlinear Hodge theory and
related areas, although the reader may see shadows of this aspect in parts
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