13 research outputs found
Learning to Generate Posters of Scientific Papers
Researchers often summarize their work in the form of posters. Posters
provide a coherent and efficient way to convey core ideas from scientific
papers. Generating a good scientific poster, however, is a complex and time
consuming cognitive task, since such posters need to be readable, informative,
and visually aesthetic. In this paper, for the first time, we study the
challenging problem of learning to generate posters from scientific papers. To
this end, a data-driven framework, that utilizes graphical models, is proposed.
Specifically, given content to display, the key elements of a good poster,
including panel layout and attributes of each panel, are learned and inferred
from data. Then, given inferred layout and attributes, composition of graphical
elements within each panel is synthesized. To learn and validate our model, we
collect and make public a Poster-Paper dataset, which consists of scientific
papers and corresponding posters with exhaustively labelled panels and
attributes. Qualitative and quantitative results indicate the effectiveness of
our approach.Comment: in Proceedings of the 30th AAAI Conference on Artificial Intelligence
(AAAI'16), Phoenix, AZ, 201
Learning Visual Importance for Graphic Designs and Data Visualizations
Knowing where people look and click on visual designs can provide clues about
how the designs are perceived, and where the most important or relevant content
lies. The most important content of a visual design can be used for effective
summarization or to facilitate retrieval from a database. We present automated
models that predict the relative importance of different elements in data
visualizations and graphic designs. Our models are neural networks trained on
human clicks and importance annotations on hundreds of designs. We collected a
new dataset of crowdsourced importance, and analyzed the predictions of our
models with respect to ground truth importance and human eye movements. We
demonstrate how such predictions of importance can be used for automatic design
retargeting and thumbnailing. User studies with hundreds of MTurk participants
validate that, with limited post-processing, our importance-driven applications
are on par with, or outperform, current state-of-the-art methods, including
natural image saliency. We also provide a demonstration of how our importance
predictions can be built into interactive design tools to offer immediate
feedback during the design process
Enabling Hyper-Personalisation: Automated Ad Creative Generation and Ranking for Fashion e-Commerce
Homepage is the first touch point in the customer's journey and is one of the
prominent channels of revenue for many e-commerce companies. A user's attention
is mostly captured by homepage banner images (also called Ads/Creatives). The
set of banners shown and their design, influence the customer's interest and
plays a key role in optimizing the click through rates of the banners.
Presently, massive and repetitive effort is put in, to manually create
aesthetically pleasing banner images. Due to the large amount of time and
effort involved in this process, only a small set of banners are made live at
any point. This reduces the number of banners created as well as the degree of
personalization that can be achieved. This paper thus presents a method to
generate creatives automatically on a large scale in a short duration. The
availability of diverse banners generated helps in improving personalization as
they can cater to the taste of larger audience. The focus of our paper is on
generating wide variety of homepage banners that can be made as an input for
user level personalization engine. Following are the main contributions of this
paper: 1) We introduce and explain the need for large scale banner generation
for e-commerce 2) We present on how we utilize existing deep learning based
detectors which can automatically annotate the required objects/tags from the
image. 3) We also propose a Genetic Algorithm based method to generate an
optimal banner layout for the given image content, input components and other
design constraints. 4) Further, to aid the process of picking the right set of
banners, we designed a ranking method and evaluated multiple models. All our
experiments have been performed on data from Myntra (http://www.myntra.com),
one of the top fashion e-commerce players in India.Comment: Workshop on Recommender Systems in Fashion, 13th ACM Conference on
Recommender Systems, 201
Reflow: Automatically Improving Touch Interactions in Mobile Applications through Pixel-based Refinements
Touch is the primary way that users interact with smartphones. However,
building mobile user interfaces where touch interactions work well for all
users is a difficult problem, because users have different abilities and
preferences. We propose a system, Reflow, which automatically applies small,
personalized UI adaptations, called refinements -- to mobile app screens to
improve touch efficiency. Reflow uses a pixel-based strategy to work with
existing applications, and improves touch efficiency while minimally disrupting
the design intent of the original application. Our system optimizes a UI by (i)
extracting its layout from its screenshot, (ii) refining its layout, and (iii)
re-rendering the UI to reflect these modifications. We conducted a user study
with 10 participants and a heuristic evaluation with 6 experts and found that
applications optimized by Reflow led to, on average, 9% faster selection time
with minimal layout disruption. The results demonstrate that Reflow's
refinements useful UI adaptations to improve touch interactions
UEyes: Understanding Visual Saliency across User Interface Types
Funding Information: This work was supported by Aalto University’s Department of Information and Communications Engineering, the Finnish Center for Artifcial Intelligence (FCAI), the Academy of Finland through the projects Human Automata (grant 328813) and BAD (grant 318559), the Horizon 2020 FET program of the European Union (grant CHISTERA-20-BCI-001), and the European Innovation Council Pathfnder program (SYMBIOTIK project, grant 101071147). We appreciate Chuhan Jiao’s initial implementation of the baseline methods for saliency prediction and active discussion with Yao (Marc) Wang. Publisher Copyright: © 2023 Owner/Author.While user interfaces (UIs) display elements such as images and text in a grid-based layout, UI types differ significantly in the number of elements and how they are displayed. For example, webpage designs rely heavily on images and text, whereas desktop UIs tend to feature numerous small images. To examine how such differences affect the way users look at UIs, we collected and analyzed a large eye-tracking-based dataset, UEyes (62 participants and 1,980 UI screenshots), covering four major UI types: webpage, desktop UI, mobile UI, and poster. We analyze its differences in biases related to such factors as color, location, and gaze direction. We also compare state-of-the-art predictive models and propose improvements for better capturing typical tendencies across UI types. Both the dataset and the models are publicly available.Peer reviewe