1,705 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
Attentive Single-Tasking of Multiple Tasks
In this work we address task interference in universal networks by
considering that a network is trained on multiple tasks, but performs one task
at a time, an approach we refer to as "single-tasking multiple tasks". The
network thus modifies its behaviour through task-dependent feature adaptation,
or task attention. This gives the network the ability to accentuate the
features that are adapted to a task, while shunning irrelevant ones. We further
reduce task interference by forcing the task gradients to be statistically
indistinguishable through adversarial training, ensuring that the common
backbone architecture serving all tasks is not dominated by any of the
task-specific gradients. Results in three multi-task dense labelling problems
consistently show: (i) a large reduction in the number of parameters while
preserving, or even improving performance and (ii) a smooth trade-off between
computation and multi-task accuracy. We provide our system's code and
pre-trained models at http://vision.ee.ethz.ch/~kmaninis/astmt/.Comment: CVPR 2019 Camera Read
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