Activity recognition from first-person (ego-centric) videos has recently
gained attention due to the increasing ubiquity of the wearable cameras. There
has been a surge of efforts adapting existing feature descriptors and designing
new descriptors for the first-person videos. An effective activity recognition
system requires selection and use of complementary features and appropriate
kernels for each feature. In this study, we propose a data-driven framework for
first-person activity recognition which effectively selects and combines
features and their respective kernels during the training. Our experimental
results show that use of Multiple Kernel Learning (MKL) and Boosted MKL in
first-person activity recognition problem exhibits improved results in
comparison to the state-of-the-art. In addition, these techniques enable the
expansion of the framework with new features in an efficient and convenient
way.

本文提出了一种基于数据驱动的框架来选择和组合一些特征及其核，在第一人称视角的活动识别中，采用多核学习 (MKL) 和基于 MKL 的 Boosted MKL 方法能够有效提高识别效果。

基于增强多核学习的第一人称活动识别

Boosted Multiple Kernel Learning for First-Person Activity Recognition

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.

本研究首次提出了一种数据驱动的框架，它利用图形模型解决了科学论文生成海报的复杂性和时间成本问题，并成功地创建了海报 - 论文数据集，定量和定性结果表明该方法非常有效。