Search Results for author:
Found 14 papers, 5 papers with code
Realistic Saliency Guided Image Enhancement
We thus offer a viable solution for automating image enhancement and photo cleanup operations.
Towards Better User Studies in Computer Graphics and Vision
We discuss foundational user research methods (e. g., needfinding) that are presently underutilized in computer vision and graphics research, but can provide valuable project direction.
KDSalBox: A toolbox of efficient knowledge-distilled saliency models
Dozens of saliency models have been designed over the last few decades, targeted at diverse applications ranging from image compression and retargeting to robot navigation, surveillance, and distractor detection.
Memorability: An image-computable measure of information utility
The pixels in an image, and the objects, scenes, and actions that they compose, determine whether an image will be memorable or forgettable.
Toward Quantifying Ambiguities in Artistic Images
It has long been hypothesized that perceptual ambiguities play an important role in aesthetic experience: a work with some ambiguity engages a viewer more than one that does not.
Look here! A parametric learning based approach to redirect visual attention
Extensions of our model allow for multi-style edits and the ability to both increase and attenuate attention in an image region.
Predicting Visual Importance Across Graphic Design Types
This paper introduces a Unified Model of Saliency and Importance (UMSI), which learns to predict visual importance in input graphic designs, and saliency in natural images, along with a new dataset and applications.
Bottom-up Attention, Models of
In this review, we examine the recent progress in saliency prediction and proposed several avenues for future research.
Synthetically Trained Icon Proposals for Parsing and Summarizing Infographics
While automatic text extraction works well on infographics, computer vision approaches trained on natural images fail to identify the stand-alone visual elements in infographics, or `icons'.
Understanding Infographics through Textual and Visual Tag Prediction
And second, we use these predicted text tags as a supervisory signal to localize the most diagnostic visual elements from within the infographic i. e. visual hashtags.
Learning Visual Importance for Graphic Designs and Data Visualizations
Our models are neural networks trained on human clicks and importance annotations on hundreds of designs.
BubbleView: an interface for crowdsourcing image importance maps and tracking visual attention
In this paper, we present BubbleView, an alternative methodology for eye tracking using discrete mouse clicks to measure which information people consciously choose to examine.
What do different evaluation metrics tell us about saliency models?
How best to evaluate a saliency model's ability to predict where humans look in images is an open research question.
Are all training examples equally valuable?
When learning a new concept, not all training examples may prove equally useful for training: some may have higher or lower training value than others.
