2 research outputs found
EGO-CH: Dataset and Fundamental Tasks for Visitors BehavioralUnderstanding using Egocentric Vision
Equipping visitors of a cultural site with a wearable device allows to easily
collect information about their preferences which can be exploited to improve
the fruition of cultural goods with augmented reality. Moreover, egocentric
video can be processed using computer vision and machine learning to enable an
automated analysis of visitors' behavior. The inferred information can be used
both online to assist the visitor and offline to support the manager of the
site. Despite the positive impact such technologies can have in cultural
heritage, the topic is currently understudied due to the limited number of
public datasets suitable to study the considered problems. To address this
issue, in this paper we propose EGOcentric-Cultural Heritage (EGO-CH), the
first dataset of egocentric videos for visitors' behavior understanding in
cultural sites. The dataset has been collected in two cultural sites and
includes more than hours of video acquired by subjects, with labels
for environments and over different Points of Interest. A large
subset of the dataset, consisting of videos, is associated with surveys
filled out by real visitors. To encourage research on the topic, we propose
challenging tasks (room-based localization, point of interest/object
recognition, object retrieval and survey prediction) useful to understand
visitors' behavior and report baseline results on the dataset
Small Sample Learning in Big Data Era
As a promising area in artificial intelligence, a new learning paradigm,
called Small Sample Learning (SSL), has been attracting prominent research
attention in the recent years. In this paper, we aim to present a survey to
comprehensively introduce the current techniques proposed on this topic.
Specifically, current SSL techniques can be mainly divided into two categories.
The first category of SSL approaches can be called "concept learning", which
emphasizes learning new concepts from only few related observations. The
purpose is mainly to simulate human learning behaviors like recognition,
generation, imagination, synthesis and analysis. The second category is called
"experience learning", which usually co-exists with the large sample learning
manner of conventional machine learning. This category mainly focuses on
learning with insufficient samples, and can also be called small data learning
in some literatures. More extensive surveys on both categories of SSL
techniques are introduced and some neuroscience evidences are provided to
clarify the rationality of the entire SSL regime, and the relationship with
human learning process. Some discussions on the main challenges and possible
future research directions along this line are also presented.Comment: 76 pages, 15 figures, survey of small sample learnin